mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into feat/nodes/freeu
This commit is contained in:
Kent Keirsey
GitHub
commit
e66d0f7372
1
.gitattributes
vendored
1
.gitattributes
vendored
@ -2,3 +2,4 @@
|
||||
# Only affects text files and ignores other file types.
|
||||
# For more info see: https://www.aleksandrhovhannisyan.com/blog/crlf-vs-lf-normalizing-line-endings-in-git/
|
||||
* text=auto
|
||||
docker/** text eol=lf
|
||||
12
.gitignore
vendored
12
.gitignore
vendored
@ -1,8 +1,5 @@
|
||||
.idea/
|
||||
|
||||
# ignore the Anaconda/Miniconda installer used while building Docker image
|
||||
anaconda.sh
|
||||
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
@ -136,12 +133,10 @@ celerybeat.pid
|
||||
|
||||
# Environments
|
||||
.env
|
||||
.venv
|
||||
.venv*
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
@ -186,11 +181,6 @@ cython_debug/
|
||||
.scratch/
|
||||
.vscode/
|
||||
|
||||
# ignore environment.yml and requirements.txt
|
||||
# these are links to the real files in environments-and-requirements
|
||||
environment.yml
|
||||
requirements.txt
|
||||
|
||||
# source installer files
|
||||
installer/*zip
|
||||
installer/install.bat
|
||||
|
||||
@ -123,7 +123,7 @@ and go to http://localhost:9090.
|
||||
|
||||
### Command-Line Installation (for developers and users familiar with Terminals)
|
||||
|
||||
You must have Python 3.9 through 3.11 installed on your machine. Earlier or
|
||||
You must have Python 3.10 through 3.11 installed on your machine. Earlier or
|
||||
later versions are not supported.
|
||||
Node.js also needs to be installed along with yarn (can be installed with
|
||||
the command `npm install -g yarn` if needed)
|
||||
|
||||
@ -1,13 +1,15 @@
|
||||
## Make a copy of this file named `.env` and fill in the values below.
|
||||
## Any environment variables supported by InvokeAI can be specified here.
|
||||
## Any environment variables supported by InvokeAI can be specified here,
|
||||
## in addition to the examples below.
|
||||
|
||||
# INVOKEAI_ROOT is the path to a path on the local filesystem where InvokeAI will store data.
|
||||
# Outputs will also be stored here by default.
|
||||
# This **must** be an absolute path.
|
||||
INVOKEAI_ROOT=
|
||||
|
||||
HUGGINGFACE_TOKEN=
|
||||
# Get this value from your HuggingFace account settings page.
|
||||
# HUGGING_FACE_HUB_TOKEN=
|
||||
|
||||
## optional variables specific to the docker setup
|
||||
## optional variables specific to the docker setup.
|
||||
# GPU_DRIVER=cuda
|
||||
# CONTAINER_UID=1000
|
||||
@ -2,7 +2,7 @@
|
||||
|
||||
## Builder stage
|
||||
|
||||
FROM library/ubuntu:22.04 AS builder
|
||||
FROM library/ubuntu:23.04 AS builder
|
||||
|
||||
ARG DEBIAN_FRONTEND=noninteractive
|
||||
RUN rm -f /etc/apt/apt.conf.d/docker-clean; echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/keep-cache
|
||||
@ -10,7 +10,7 @@ RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
|
||||
--mount=type=cache,target=/var/lib/apt,sharing=locked \
|
||||
apt update && apt-get install -y \
|
||||
git \
|
||||
python3.10-venv \
|
||||
python3-venv \
|
||||
python3-pip \
|
||||
build-essential
|
||||
|
||||
@ -37,7 +37,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \
|
||||
elif [ "$GPU_DRIVER" = "rocm" ]; then \
|
||||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/rocm5.4.2"; \
|
||||
else \
|
||||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu118"; \
|
||||
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
|
||||
fi &&\
|
||||
pip install $extra_index_url_arg \
|
||||
torch==$TORCH_VERSION \
|
||||
@ -70,7 +70,7 @@ RUN --mount=type=cache,target=/usr/lib/node_modules \
|
||||
|
||||
#### Runtime stage ---------------------------------------
|
||||
|
||||
FROM library/ubuntu:22.04 AS runtime
|
||||
FROM library/ubuntu:23.04 AS runtime
|
||||
|
||||
ARG DEBIAN_FRONTEND=noninteractive
|
||||
ENV PYTHONUNBUFFERED=1
|
||||
@ -85,6 +85,7 @@ RUN apt update && apt install -y --no-install-recommends \
|
||||
iotop \
|
||||
bzip2 \
|
||||
gosu \
|
||||
magic-wormhole \
|
||||
libglib2.0-0 \
|
||||
libgl1-mesa-glx \
|
||||
python3-venv \
|
||||
@ -94,10 +95,6 @@ RUN apt update && apt install -y --no-install-recommends \
|
||||
libstdc++-10-dev &&\
|
||||
apt-get clean && apt-get autoclean
|
||||
|
||||
# globally add magic-wormhole
|
||||
# for ease of transferring data to and from the container
|
||||
# when running in sandboxed cloud environments; e.g. Runpod etc.
|
||||
RUN pip install magic-wormhole
|
||||
|
||||
ENV INVOKEAI_SRC=/opt/invokeai
|
||||
ENV VIRTUAL_ENV=/opt/venv/invokeai
|
||||
@ -120,9 +117,7 @@ WORKDIR ${INVOKEAI_SRC}
|
||||
RUN cd /usr/lib/$(uname -p)-linux-gnu/pkgconfig/ && ln -sf opencv4.pc opencv.pc
|
||||
RUN python3 -c "from patchmatch import patch_match"
|
||||
|
||||
# Create unprivileged user and make the local dir
|
||||
RUN useradd --create-home --shell /bin/bash -u 1000 --comment "container local user" invoke
|
||||
RUN mkdir -p ${INVOKEAI_ROOT} && chown -R invoke:invoke ${INVOKEAI_ROOT}
|
||||
RUN mkdir -p ${INVOKEAI_ROOT} && chown -R 1000:1000 ${INVOKEAI_ROOT}
|
||||
|
||||
COPY docker/docker-entrypoint.sh ./
|
||||
ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
|
||||
|
||||
@ -5,7 +5,7 @@ All commands are to be run from the `docker` directory: `cd docker`
|
||||
#### Linux
|
||||
|
||||
1. Ensure builkit is enabled in the Docker daemon settings (`/etc/docker/daemon.json`)
|
||||
2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-compose-on-ubuntu-22-04).
|
||||
2. Install the `docker compose` plugin using your package manager, or follow a [tutorial](https://docs.docker.com/compose/install/linux/#install-using-the-repository).
|
||||
- The deprecated `docker-compose` (hyphenated) CLI continues to work for now.
|
||||
3. Ensure docker daemon is able to access the GPU.
|
||||
- You may need to install [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
|
||||
@ -20,7 +20,6 @@ This is done via Docker Desktop preferences
|
||||
|
||||
## Quickstart
|
||||
|
||||
|
||||
1. Make a copy of `env.sample` and name it `.env` (`cp env.sample .env` (Mac/Linux) or `copy example.env .env` (Windows)). Make changes as necessary. Set `INVOKEAI_ROOT` to an absolute path to:
|
||||
a. the desired location of the InvokeAI runtime directory, or
|
||||
b. an existing, v3.0.0 compatible runtime directory.
|
||||
@ -42,20 +41,22 @@ The Docker daemon on the system must be already set up to use the GPU. In case o
|
||||
|
||||
Check the `.env.sample` file. It contains some environment variables for running in Docker. Copy it, name it `.env`, and fill it in with your own values. Next time you run `docker compose up`, your custom values will be used.
|
||||
|
||||
You can also set these values in `docker compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
|
||||
You can also set these values in `docker-compose.yml` directly, but `.env` will help avoid conflicts when code is updated.
|
||||
|
||||
Example (most values are optional):
|
||||
Example (values are optional, but setting `INVOKEAI_ROOT` is highly recommended):
|
||||
|
||||
```
|
||||
```bash
|
||||
INVOKEAI_ROOT=/Volumes/WorkDrive/invokeai
|
||||
HUGGINGFACE_TOKEN=the_actual_token
|
||||
CONTAINER_UID=1000
|
||||
GPU_DRIVER=cuda
|
||||
```
|
||||
|
||||
Any environment variables supported by InvokeAI can be set here - please see the [Configuration docs](https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/) for further detail.
|
||||
|
||||
## Even Moar Customizing!
|
||||
|
||||
See the `docker compose.yaml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
|
||||
See the `docker-compose.yml` file. The `command` instruction can be uncommented and used to run arbitrary startup commands. Some examples below.
|
||||
|
||||
### Reconfigure the runtime directory
|
||||
|
||||
@ -63,7 +64,7 @@ Can be used to download additional models from the supported model list
|
||||
|
||||
In conjunction with `INVOKEAI_ROOT` can be also used to initialize a runtime directory
|
||||
|
||||
```
|
||||
```yaml
|
||||
command:
|
||||
- invokeai-configure
|
||||
- --yes
|
||||
@ -71,7 +72,7 @@ command:
|
||||
|
||||
Or install models:
|
||||
|
||||
```
|
||||
```yaml
|
||||
command:
|
||||
- invokeai-model-install
|
||||
```
|
||||
```
|
||||
|
||||
@ -5,7 +5,7 @@ build_args=""
|
||||
|
||||
[[ -f ".env" ]] && build_args=$(awk '$1 ~ /\=[^$]/ {print "--build-arg " $0 " "}' .env)
|
||||
|
||||
echo "docker-compose build args:"
|
||||
echo "docker compose build args:"
|
||||
echo $build_args
|
||||
|
||||
docker-compose build $build_args
|
||||
docker compose build $build_args
|
||||
|
||||
@ -19,7 +19,7 @@ set -e -o pipefail
|
||||
# Default UID: 1000 chosen due to popularity on Linux systems. Possibly 501 on MacOS.
|
||||
|
||||
USER_ID=${CONTAINER_UID:-1000}
|
||||
USER=invoke
|
||||
USER=ubuntu
|
||||
usermod -u ${USER_ID} ${USER} 1>/dev/null
|
||||
|
||||
configure() {
|
||||
|
||||
@ -1,8 +1,11 @@
|
||||
#!/usr/bin/env bash
|
||||
set -e
|
||||
|
||||
# This script is provided for backwards compatibility with the old docker setup.
|
||||
# it doesn't do much aside from wrapping the usual docker compose CLI.
|
||||
|
||||
SCRIPTDIR=$(dirname "${BASH_SOURCE[0]}")
|
||||
cd "$SCRIPTDIR" || exit 1
|
||||
|
||||
docker-compose up --build -d
|
||||
docker-compose logs -f
|
||||
docker compose up --build -d
|
||||
docker compose logs -f
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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.
|
||||
|
||||
|
||||
@ -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.) |
|
||||
|
||||
@ -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.
|
||||
@ -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 <ghibli-face> | Japanese gardener <hoi4-leaders> | Japanese gardener <cartoona-animals> |
|
||||
| :--------------------------------: | :-----------------------------------: | :------------------------------------: | :----------------------------------------: |
|
||||
|
||||
@ -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
|
||||
|
||||
@ -17,9 +17,6 @@ image generation, providing you with a way to direct the network
|
||||
towards generating images that better fit your desired style or
|
||||
outcome.
|
||||
|
||||
|
||||
#### How it works
|
||||
|
||||
ControlNet works by analyzing an input image, pre-processing that
|
||||
image to identify relevant information that can be interpreted by each
|
||||
specific ControlNet model, and then inserting that control information
|
||||
@ -27,35 +24,21 @@ into the generation process. This can be used to adjust the style,
|
||||
composition, or other aspects of the image to better achieve a
|
||||
specific result.
|
||||
|
||||
|
||||
#### Models
|
||||
#### Installation
|
||||
|
||||
InvokeAI provides access to a series of ControlNet models that provide
|
||||
different effects or styles in your generated images. Currently
|
||||
InvokeAI only supports "diffuser" style ControlNet models. These are
|
||||
folders that contain the files `config.json` and/or
|
||||
`diffusion_pytorch_model.safetensors` and
|
||||
`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
|
||||
the name of the model.
|
||||
different effects or styles in your generated images.
|
||||
|
||||
***InvokeAI does not currently support checkpoint-format
|
||||
ControlNets. These come in the form of a single file with the
|
||||
extension `.safetensors`.***
|
||||
To install ControlNet Models:
|
||||
|
||||
Diffuser-style ControlNet models are available at HuggingFace
|
||||
(http://huggingface.co) and accessed via their repo IDs (identifiers
|
||||
in the format "author/modelname"). The easiest way to install them is
|
||||
1. The easiest way to install them is
|
||||
to use the InvokeAI model installer application. Use the
|
||||
`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
|
||||
`invoke.sh`/`invoke.bat` launcher to select item [4] and then navigate
|
||||
to the CONTROLNETS section. Select the models you wish to install and
|
||||
press "APPLY CHANGES". You may also enter additional HuggingFace
|
||||
repo_ids in the "Additional models" textbox:
|
||||
repo_ids in the "Additional models" textbox.
|
||||
2. Using the "Add Model" function of the model manager, enter the HuggingFace Repo ID of the ControlNet. The ID is in the format "author/repoName"
|
||||
|
||||
{:width="640px"}
|
||||
|
||||
Command-line users can launch the model installer using the command
|
||||
`invokeai-model-install`.
|
||||
|
||||
_Be aware that some ControlNet models require additional code
|
||||
functionality in order to work properly, so just installing a
|
||||
@ -63,6 +46,17 @@ third-party ControlNet model may not have the desired effect._ Please
|
||||
read and follow the documentation for installing a third party model
|
||||
not currently included among InvokeAI's default list.
|
||||
|
||||
Currently InvokeAI **only** supports 🤗 Diffusers-format ControlNet models. These are
|
||||
folders that contain the files `config.json` and/or
|
||||
`diffusion_pytorch_model.safetensors` and
|
||||
`diffusion_pytorch_model.fp16.safetensors`. The name of the folder is
|
||||
the name of the model.
|
||||
|
||||
🤗 Diffusers-format ControlNet models are available at HuggingFace
|
||||
(http://huggingface.co) and accessed via their repo IDs (identifiers
|
||||
in the format "author/modelname").
|
||||
|
||||
#### ControlNet Models
|
||||
The models currently supported include:
|
||||
|
||||
**Canny**:
|
||||
@ -133,6 +127,29 @@ Start/End - 0 represents the start of the generation, 1 represents the end. The
|
||||
|
||||
Additionally, each ControlNet section can be expanded in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in when you Invoke.
|
||||
|
||||
## T2I-Adapter
|
||||
[T2I-Adapter](https://github.com/TencentARC/T2I-Adapter) is a tool similar to ControlNet that allows for control over the generation process by providing control information during the generation process. T2I-Adapter models tend to be smaller and more efficient than ControlNets.
|
||||
|
||||
##### Installation
|
||||
To install T2I-Adapter Models:
|
||||
|
||||
1. The easiest way to install models is
|
||||
to use the InvokeAI model installer application. Use the
|
||||
`invoke.sh`/`invoke.bat` launcher to select item [5] and then navigate
|
||||
to the T2I-Adapters section. Select the models you wish to install and
|
||||
press "APPLY CHANGES". You may also enter additional HuggingFace
|
||||
repo_ids in the "Additional models" textbox.
|
||||
2. Using the "Add Model" function of the model manager, enter the HuggingFace Repo ID of the T2I-Adapter. The ID is in the format "author/repoName"
|
||||
|
||||
#### Usage
|
||||
Each T2I Adapter has two settings that are applied.
|
||||
|
||||
Weight - Strength of the model applied to the generation for the section, defined by start/end.
|
||||
|
||||
Start/End - 0 represents the start of the generation, 1 represents the end. The Start/end setting controls what steps during the generation process have the ControlNet applied.
|
||||
|
||||
Additionally, each section can be expanded with the "Show Advanced" button in order to manipulate settings for the image pre-processor that adjusts your uploaded image before using it in during the generation process.
|
||||
|
||||
|
||||
## IP-Adapter
|
||||
|
||||
@ -140,13 +157,13 @@ 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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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**
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
@ -57,7 +57,9 @@ Prompts provide the models directions on what to generate. As a general rule of
|
||||
|
||||
Models are the magic that power InvokeAI. These files represent the output of training a machine on understanding massive amounts of images - providing them with the capability to generate new images using just a text description of what you’d like to see. (Like Stable Diffusion!)
|
||||
|
||||
Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at ****. Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision!
|
||||
Invoke offers a simple way to download several different models upon installation, but many more can be discovered online, including at https://models.invoke.ai
|
||||
|
||||
Each model can produce a unique style of output, based on the images it was trained on - Try out different models to see which best fits your creative vision!
|
||||
|
||||
- *Models that contain “inpainting” in the name are designed for use with the inpainting feature of the Unified Canvas*
|
||||
|
||||
|
||||
@ -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`.
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -4,30 +4,31 @@ title: Installing with Docker
|
||||
|
||||
# :fontawesome-brands-docker: Docker
|
||||
|
||||
!!! warning "For most users"
|
||||
!!! warning "macOS and AMD GPU Users"
|
||||
|
||||
We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md)
|
||||
We highly recommend to Install InvokeAI locally using [these instructions](INSTALLATION.md),
|
||||
because Docker containers can not access the GPU on macOS.
|
||||
|
||||
!!! tip "For developers"
|
||||
!!! warning "AMD GPU Users"
|
||||
|
||||
For container-related development tasks or for enabling easy
|
||||
deployment to other environments (on-premises or cloud), follow these
|
||||
instructions.
|
||||
Container support for AMD GPUs has been reported to work by the community, but has not received
|
||||
extensive testing. Please make sure to set the `GPU_DRIVER=rocm` environment variable (see below), and
|
||||
use the `build.sh` script to build the image for this to take effect at build time.
|
||||
|
||||
For general use, install locally to leverage your machine's GPU.
|
||||
!!! tip "Linux and Windows Users"
|
||||
|
||||
For optimal performance, configure your Docker daemon to access your machine's GPU.
|
||||
Docker Desktop on Windows [includes GPU support](https://www.docker.com/blog/wsl-2-gpu-support-for-docker-desktop-on-nvidia-gpus/).
|
||||
Linux users should install and configure the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
|
||||
|
||||
## Why containers?
|
||||
|
||||
They provide a flexible, reliable way to build and deploy InvokeAI. You'll also
|
||||
use a Docker volume to store the largest model files and image outputs as a
|
||||
first step in decoupling storage and compute. Future enhancements can do this
|
||||
for other assets. See [Processes](https://12factor.net/processes) under the
|
||||
Twelve-Factor App methodology for details on why running applications in such a
|
||||
stateless fashion is important.
|
||||
They provide a flexible, reliable way to build and deploy InvokeAI.
|
||||
See [Processes](https://12factor.net/processes) under the Twelve-Factor App
|
||||
methodology for details on why running applications in such a stateless fashion is important.
|
||||
|
||||
You can specify the target platform when building the image and running the
|
||||
container. You'll also need to specify the InvokeAI requirements file that
|
||||
matches the container's OS and the architecture it will run on.
|
||||
The container is configured for CUDA by default, but can be built to support AMD GPUs
|
||||
by setting the `GPU_DRIVER=rocm` environment variable at Docker image build time.
|
||||
|
||||
Developers on Apple silicon (M1/M2): You
|
||||
[can't access your GPU cores from Docker containers](https://github.com/pytorch/pytorch/issues/81224)
|
||||
@ -36,6 +37,16 @@ development purposes it's fine. Once you're done with development tasks on your
|
||||
laptop you can build for the target platform and architecture and deploy to
|
||||
another environment with NVIDIA GPUs on-premises or in the cloud.
|
||||
|
||||
## TL;DR
|
||||
|
||||
This assumes properly configured Docker on Linux or Windows/WSL2. Read on for detailed customization options.
|
||||
|
||||
```bash
|
||||
# docker compose commands should be run from the `docker` directory
|
||||
cd docker
|
||||
docker compose up
|
||||
```
|
||||
|
||||
## Installation in a Linux container (desktop)
|
||||
|
||||
### Prerequisites
|
||||
@ -58,222 +69,44 @@ a token and copy it, since you will need in for the next step.
|
||||
|
||||
### Setup
|
||||
|
||||
Set the fork you want to use and other variables.
|
||||
Set up your environmnent variables. In the `docker` directory, make a copy of `env.sample` and name it `.env`. Make changes as necessary.
|
||||
|
||||
!!! tip
|
||||
Any environment variables supported by InvokeAI can be set here - please see the [CONFIGURATION](../features/CONFIGURATION.md) for further detail.
|
||||
|
||||
I preffer to save my env vars
|
||||
in the repository root in a `.env` (or `.envrc`) file to automatically re-apply
|
||||
them when I come back.
|
||||
|
||||
The build- and run- scripts contain default values for almost everything,
|
||||
besides the [Hugging Face Token](https://huggingface.co/settings/tokens) you
|
||||
created in the last step.
|
||||
|
||||
Some Suggestions of variables you may want to change besides the Token:
|
||||
At a minimum, you might want to set the `INVOKEAI_ROOT` environment variable
|
||||
to point to the location where you wish to store your InvokeAI models, configuration, and outputs.
|
||||
|
||||
<figure markdown>
|
||||
|
||||
| Environment-Variable <img width="220" align="right"/> | Default value <img width="360" align="right"/> | Description |
|
||||
| ----------------------------------------------------- | ---------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `HUGGING_FACE_HUB_TOKEN` | No default, but **required**! | This is the only **required** variable, without it you can't download the huggingface models |
|
||||
| `REPOSITORY_NAME` | The Basename of the Repo folder | This name will used as the container repository/image name |
|
||||
| `VOLUMENAME` | `${REPOSITORY_NAME,,}_data` | Name of the Docker Volume where model files will be stored |
|
||||
| `ARCH` | arch of the build machine | Can be changed if you want to build the image for another arch |
|
||||
| `CONTAINER_REGISTRY` | ghcr.io | Name of the Container Registry to use for the full tag |
|
||||
| `CONTAINER_REPOSITORY` | `$(whoami)/${REPOSITORY_NAME}` | Name of the Container Repository |
|
||||
| `CONTAINER_FLAVOR` | `cuda` | The flavor of the image to built, available options are `cuda`, `rocm` and `cpu`. If you choose `rocm` or `cpu`, the extra-index-url will be selected automatically, unless you set one yourself. |
|
||||
| `CONTAINER_TAG` | `${INVOKEAI_BRANCH##*/}-${CONTAINER_FLAVOR}` | The Container Repository / Tag which will be used |
|
||||
| `INVOKE_DOCKERFILE` | `Dockerfile` | The Dockerfile which should be built, handy for development |
|
||||
| `PIP_EXTRA_INDEX_URL` | | If you want to use a custom pip-extra-index-url |
|
||||
| `INVOKEAI_ROOT` | `~/invokeai` | **Required** - the location of your InvokeAI root directory. It will be created if it does not exist.
|
||||
| `HUGGING_FACE_HUB_TOKEN` | | InvokeAI will work without it, but some of the integrations with HuggingFace (like downloading from models from private repositories) may not work|
|
||||
| `GPU_DRIVER` | `cuda` | Optionally change this to `rocm` to build the image for AMD GPUs. NOTE: Use the `build.sh` script to build the image for this to take effect.
|
||||
|
||||
</figure>
|
||||
|
||||
#### Build the Image
|
||||
|
||||
I provided a build script, which is located next to the Dockerfile in
|
||||
`docker/build.sh`. It can be executed from repository root like this:
|
||||
Use the standard `docker compose build` command from within the `docker` directory.
|
||||
|
||||
```bash
|
||||
./docker/build.sh
|
||||
```
|
||||
|
||||
The build Script not only builds the container, but also creates the docker
|
||||
volume if not existing yet.
|
||||
If using an AMD GPU:
|
||||
a: set the `GPU_DRIVER=rocm` environment variable in `docker-compose.yml` and continue using `docker compose build` as usual, or
|
||||
b: set `GPU_DRIVER=rocm` in the `.env` file and use the `build.sh` script, provided for convenience
|
||||
|
||||
#### Run the Container
|
||||
|
||||
After the build process is done, you can run the container via the provided
|
||||
`docker/run.sh` script
|
||||
Use the standard `docker compose up` command, and generally the `docker compose` [CLI](https://docs.docker.com/compose/reference/) as usual.
|
||||
|
||||
```bash
|
||||
./docker/run.sh
|
||||
```
|
||||
Once the container starts up (and configures the InvokeAI root directory if this is a new installation), you can access InvokeAI at [http://localhost:9090](http://localhost:9090)
|
||||
|
||||
When used without arguments, the container will start the webserver and provide
|
||||
you the link to open it. But if you want to use some other parameters you can
|
||||
also do so.
|
||||
## Troubleshooting / FAQ
|
||||
|
||||
!!! example "run script example"
|
||||
|
||||
```bash
|
||||
./docker/run.sh "banana sushi" -Ak_lms -S42 -s10
|
||||
```
|
||||
|
||||
This would generate the legendary "banana sushi" with Seed 42, k_lms Sampler and 10 steps.
|
||||
|
||||
Find out more about available CLI-Parameters at [features/CLI.md](../../features/CLI/#arguments)
|
||||
|
||||
---
|
||||
|
||||
## Running the container on your GPU
|
||||
|
||||
If you have an Nvidia GPU, you can enable InvokeAI to run on the GPU by running
|
||||
the container with an extra environment variable to enable GPU usage and have
|
||||
the process run much faster:
|
||||
|
||||
```bash
|
||||
GPU_FLAGS=all ./docker/run.sh
|
||||
```
|
||||
|
||||
This passes the `--gpus all` to docker and uses the GPU.
|
||||
|
||||
If you don't have a GPU (or your host is not yet setup to use it) you will see a
|
||||
message like this:
|
||||
|
||||
`docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]].`
|
||||
|
||||
You can use the full set of GPU combinations documented here:
|
||||
|
||||
https://docs.docker.com/config/containers/resource_constraints/#gpu
|
||||
|
||||
For example, use `GPU_FLAGS=device=GPU-3a23c669-1f69-c64e-cf85-44e9b07e7a2a` to
|
||||
choose a specific device identified by a UUID.
|
||||
|
||||
---
|
||||
|
||||
!!! warning "Deprecated"
|
||||
|
||||
From here on you will find the the previous Docker-Docs, which will still
|
||||
provide some usefull informations.
|
||||
|
||||
## Usage (time to have fun)
|
||||
|
||||
### Startup
|
||||
|
||||
If you're on a **Linux container** the `invoke` script is **automatically
|
||||
started** and the output dir set to the Docker volume you created earlier.
|
||||
|
||||
If you're **directly on macOS follow these startup instructions**. With the
|
||||
Conda environment activated (`conda activate ldm`), run the interactive
|
||||
interface that combines the functionality of the original scripts `txt2img` and
|
||||
`img2img`: Use the more accurate but VRAM-intensive full precision math because
|
||||
half-precision requires autocast and won't work. By default the images are saved
|
||||
in `outputs/img-samples/`.
|
||||
|
||||
```Shell
|
||||
python3 scripts/invoke.py --full_precision
|
||||
```
|
||||
|
||||
You'll get the script's prompt. You can see available options or quit.
|
||||
|
||||
```Shell
|
||||
invoke> -h
|
||||
invoke> q
|
||||
```
|
||||
|
||||
### Text to Image
|
||||
|
||||
For quick (but bad) image results test with 5 steps (default 50) and 1 sample
|
||||
image. This will let you know that everything is set up correctly. Then increase
|
||||
steps to 100 or more for good (but slower) results. The prompt can be in quotes
|
||||
or not.
|
||||
|
||||
```Shell
|
||||
invoke> The hulk fighting with sheldon cooper -s5 -n1
|
||||
invoke> "woman closeup highly detailed" -s 150
|
||||
# Reuse previous seed and apply face restoration
|
||||
invoke> "woman closeup highly detailed" --steps 150 --seed -1 -G 0.75
|
||||
```
|
||||
|
||||
You'll need to experiment to see if face restoration is making it better or
|
||||
worse for your specific prompt.
|
||||
|
||||
If you're on a container the output is set to the Docker volume. You can copy it
|
||||
wherever you want. You can download it from the Docker Desktop app, Volumes,
|
||||
my-vol, data. Or you can copy it from your Mac terminal. Keep in mind
|
||||
`docker cp` can't expand `*.png` so you'll need to specify the image file name.
|
||||
|
||||
On your host Mac (you can use the name of any container that mounted the
|
||||
volume):
|
||||
|
||||
```Shell
|
||||
docker cp dummy:/data/000001.928403745.png /Users/<your-user>/Pictures
|
||||
```
|
||||
|
||||
### Image to Image
|
||||
|
||||
You can also do text-guided image-to-image translation. For example, turning a
|
||||
sketch into a detailed drawing.
|
||||
|
||||
`strength` is a value between 0.0 and 1.0 that controls the amount of noise that
|
||||
is added to the input image. Values that approach 1.0 allow for lots of
|
||||
variations but will also produce images that are not semantically consistent
|
||||
with the input. 0.0 preserves image exactly, 1.0 replaces it completely.
|
||||
|
||||
Make sure your input image size dimensions are multiples of 64 e.g. 512x512.
|
||||
Otherwise you'll get `Error: product of dimension sizes > 2**31'`. If you still
|
||||
get the error
|
||||
[try a different size](https://support.apple.com/guide/preview/resize-rotate-or-flip-an-image-prvw2015/mac#:~:text=image's%20file%20size-,In%20the%20Preview%20app%20on%20your%20Mac%2C%20open%20the%20file,is%20shown%20at%20the%20bottom.)
|
||||
like 512x256.
|
||||
|
||||
If you're on a Docker container, copy your input image into the Docker volume
|
||||
|
||||
```Shell
|
||||
docker cp /Users/<your-user>/Pictures/sketch-mountains-input.jpg dummy:/data/
|
||||
```
|
||||
|
||||
Try it out generating an image (or more). The `invoke` script needs absolute
|
||||
paths to find the image so don't use `~`.
|
||||
|
||||
If you're on your Mac
|
||||
|
||||
```Shell
|
||||
invoke> "A fantasy landscape, trending on artstation" -I /Users/<your-user>/Pictures/sketch-mountains-input.jpg --strength 0.75 --steps 100 -n4
|
||||
```
|
||||
|
||||
If you're on a Linux container on your Mac
|
||||
|
||||
```Shell
|
||||
invoke> "A fantasy landscape, trending on artstation" -I /data/sketch-mountains-input.jpg --strength 0.75 --steps 50 -n1
|
||||
```
|
||||
|
||||
### Web Interface
|
||||
|
||||
You can use the `invoke` script with a graphical web interface. Start the web
|
||||
server with:
|
||||
|
||||
```Shell
|
||||
python3 scripts/invoke.py --full_precision --web
|
||||
```
|
||||
|
||||
If it's running on your Mac point your Mac web browser to
|
||||
<http://127.0.0.1:9090>
|
||||
|
||||
Press Control-C at the command line to stop the web server.
|
||||
|
||||
### Notes
|
||||
|
||||
Some text you can add at the end of the prompt to make it very pretty:
|
||||
|
||||
```Shell
|
||||
cinematic photo, highly detailed, cinematic lighting, ultra-detailed, ultrarealistic, photorealism, Octane Rendering, cyberpunk lights, Hyper Detail, 8K, HD, Unreal Engine, V-Ray, full hd, cyberpunk, abstract, 3d octane render + 4k UHD + immense detail + dramatic lighting + well lit + black, purple, blue, pink, cerulean, teal, metallic colours, + fine details, ultra photoreal, photographic, concept art, cinematic composition, rule of thirds, mysterious, eerie, photorealism, breathtaking detailed, painting art deco pattern, by hsiao, ron cheng, john james audubon, bizarre compositions, exquisite detail, extremely moody lighting, painted by greg rutkowski makoto shinkai takashi takeuchi studio ghibli, akihiko yoshida
|
||||
```
|
||||
|
||||
The original scripts should work as well.
|
||||
|
||||
```Shell
|
||||
python3 scripts/orig_scripts/txt2img.py --help
|
||||
python3 scripts/orig_scripts/txt2img.py --ddim_steps 100 --n_iter 1 --n_samples 1 --plms --prompt "new born baby kitten. Hyper Detail, Octane Rendering, Unreal Engine, V-Ray"
|
||||
python3 scripts/orig_scripts/txt2img.py --ddim_steps 5 --n_iter 1 --n_samples 1 --plms --prompt "ocean" # or --klms
|
||||
```
|
||||
- Q: I am running on Windows under WSL2, and am seeing a "no such file or directory" error.
|
||||
- A: Your `docker-entrypoint.sh` file likely has Windows (CRLF) as opposed to Unix (LF) line endings,
|
||||
and you may have cloned this repository before the issue was fixed. To solve this, please change
|
||||
the line endings in the `docker-entrypoint.sh` file to `LF`. You can do this in VSCode
|
||||
(`Ctrl+P` and search for "line endings"), or by using the `dos2unix` utility in WSL.
|
||||
Finally, you may delete `docker-entrypoint.sh` followed by `git pull; git checkout docker/docker-entrypoint.sh`
|
||||
to reset the file to its most recent version.
|
||||
For more information on this issue, please see the [Docker Desktop documentation](https://docs.docker.com/desktop/troubleshoot/topics/#avoid-unexpected-syntax-errors-use-unix-style-line-endings-for-files-in-containers)
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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".
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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`.
|
||||
|
||||
---
|
||||
|
||||
@ -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
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -4,11 +4,16 @@ These are nodes that have been developed by the community, for the community. If
|
||||
|
||||
If you'd like to submit a node for the community, please refer to the [node creation overview](contributingNodes.md).
|
||||
|
||||
To download a node, simply download the `.py` node file from the link and add it to the `invokeai/app/invocations` folder in your Invoke AI install location. If you used the automated installation, this can be found inside the `.venv` folder. Along with the node, an example node graph should be provided to help you get started with the node.
|
||||
To use a node, add the node to the `nodes` folder found in your InvokeAI install location.
|
||||
|
||||
The suggested method is to use `git clone` to clone the repository the node is found in. This allows for easy updates of the node in the future.
|
||||
|
||||
If you'd prefer, you can also just download the `.py` file from the linked repository and add it to the `nodes` folder.
|
||||
|
||||
To use a community workflow, download the the `.json` node graph file and load it into Invoke AI via the **Load Workflow** button in the Workflow Editor.
|
||||
|
||||
- Community Nodes
|
||||
+ [Average Images](#average-images)
|
||||
+ [Depth Map from Wavefront OBJ](#depth-map-from-wavefront-obj)
|
||||
+ [Film Grain](#film-grain)
|
||||
+ [Generative Grammar-Based Prompt Nodes](#generative-grammar-based-prompt-nodes)
|
||||
@ -33,6 +38,13 @@ To use a community workflow, download the the `.json` node graph file and load i
|
||||
- [Help](#help)
|
||||
|
||||
|
||||
--------------------------------
|
||||
### Average Images
|
||||
|
||||
**Description:** This node takes in a collection of images of the same size and averages them as output. It converts everything to RGB mode first.
|
||||
|
||||
**Node Link:** https://github.com/JPPhoto/average-images-node
|
||||
|
||||
--------------------------------
|
||||
### Depth Map from Wavefront OBJ
|
||||
|
||||
@ -177,12 +189,8 @@ This includes 15 Nodes:
|
||||
|
||||
**Node Link:** https://github.com/helix4u/load_video_frame
|
||||
|
||||
**Example Node Graph:** https://github.com/helix4u/load_video_frame/blob/main/Example_Workflow.json
|
||||
|
||||
**Output Example:**
|
||||
|
||||
<img src="https://github.com/helix4u/load_video_frame/blob/main/testmp4_embed_converted.gif" width="500" />
|
||||
[Full mp4 of Example Output test.mp4](https://github.com/helix4u/load_video_frame/blob/main/test.mp4)
|
||||
<img src="https://raw.githubusercontent.com/helix4u/load_video_frame/main/_git_assets/testmp4_embed_converted.gif" width="500" />
|
||||
|
||||
--------------------------------
|
||||
### Make 3D
|
||||
@ -325,9 +333,9 @@ See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/READ
|
||||
|
||||
**Description:** This node allows you to do super cool things with InvokeAI.
|
||||
|
||||
**Node Link:** https://github.com/invoke-ai/InvokeAI/fake_node.py
|
||||
**Node Link:** https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/app/invocations/prompt.py
|
||||
|
||||
**Example Node Graph:** https://github.com/invoke-ai/InvokeAI/fake_node_graph.json
|
||||
**Example Workflow:** https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json
|
||||
|
||||
**Output Examples**
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ To learn about the specifics of creating a new node, please visit our [Node crea
|
||||
|
||||
Once you’ve created a node and confirmed that it behaves as expected locally, follow these steps:
|
||||
|
||||
- Make sure the node is contained in a new Python (.py) file. Preferrably, the node is in a repo with a README detaling the nodes usage & examples to help others more easily use your node.
|
||||
- Make sure the node is contained in a new Python (.py) file. Preferably, the node is in a repo with a README detailing the nodes usage & examples to help others more easily use your node. Including the tag "invokeai-node" in your repository's README can also help other users find it more easily.
|
||||
- Submit a pull request with a link to your node(s) repo in GitHub against the `main` branch to add the node to the [Community Nodes](communityNodes.md) list
|
||||
- Make sure you are following the template below and have provided all relevant details about the node and what it does. Example output images and workflows are very helpful for other users looking to use your node.
|
||||
- A maintainer will review the pull request and node. If the node is aligned with the direction of the project, you may be asked for permission to include it in the core project.
|
||||
|
||||
@ -2,13 +2,17 @@
|
||||
|
||||
We've curated some example workflows for you to get started with Workflows in InvokeAI
|
||||
|
||||
To use them, right click on your desired workflow, press "Download Linked File". You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
|
||||
To use them, right click on your desired workflow, follow the link to GitHub and click the "⬇" button to download the raw file. You can then use the "Load Workflow" functionality in InvokeAI to load the workflow and start generating images!
|
||||
|
||||
If you're interested in finding more workflows, checkout the [#share-your-workflows](https://discord.com/channels/1020123559063990373/1130291608097661000) channel in the InvokeAI Discord.
|
||||
|
||||
* [SD1.5 / SD2 Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Text_to_Image.json)
|
||||
* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
|
||||
* [SDXL (with Refiner) Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
|
||||
* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale w_Canny_ControlNet.json)
|
||||
* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_Text_to_Image.json)
|
||||
* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
|
||||
* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Multi_ControlNet_Canny_and_Depth.json)
|
||||
* [Tiled Upscaling with ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/ESRGAN_img2img_upscale_w_Canny_ControlNet.json)
|
||||
* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Prompt_from_File.json)
|
||||
* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json.json)
|
||||
* [FaceMask](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceMask.json)
|
||||
* [FaceOff with 2x Face Scaling](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/FaceOff_FaceScale2x.json)
|
||||
* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/docs/main/docs/workflows/QR_Code_Monster.json)
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
2032
docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json
Normal file
2032
docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.json
Normal file
File diff suppressed because it is too large
Load Diff
985
docs/workflows/Multi_ControlNet_Canny_and_Depth.json
Normal file
985
docs/workflows/Multi_ControlNet_Canny_and_Depth.json
Normal file
@ -0,0 +1,985 @@
|
||||
{
|
||||
"name": "Multi ControlNet (Canny & Depth)",
|
||||
"author": "Millu",
|
||||
"description": "A sample workflow using canny & depth ControlNets to guide the generation process. ",
|
||||
"version": "0.1.0",
|
||||
"contact": "millun@invoke.ai",
|
||||
"tags": "ControlNet, canny, depth",
|
||||
"notes": "",
|
||||
"exposedFields": [
|
||||
{
|
||||
"nodeId": "54486974-835b-4d81-8f82-05f9f32ce9e9",
|
||||
"fieldName": "model"
|
||||
},
|
||||
{
|
||||
"nodeId": "7ce68934-3419-42d4-ac70-82cfc9397306",
|
||||
"fieldName": "prompt"
|
||||
},
|
||||
{
|
||||
"nodeId": "273e3f96-49ea-4dc5-9d5b-9660390f14e1",
|
||||
"fieldName": "prompt"
|
||||
},
|
||||
{
|
||||
"nodeId": "c4b23e64-7986-40c4-9cad-46327b12e204",
|
||||
"fieldName": "image"
|
||||
},
|
||||
{
|
||||
"nodeId": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
|
||||
"fieldName": "image"
|
||||
}
|
||||
],
|
||||
"meta": {
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"nodes": [
|
||||
{
|
||||
"id": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "8e860e51-5045-456e-bf04-9a62a2a5c49e",
|
||||
"type": "image",
|
||||
"inputs": {
|
||||
"image": {
|
||||
"id": "189c8adf-68cc-4774-a729-49da89f6fdf1",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": "Depth Input Image"
|
||||
}
|
||||
},
|
||||
"outputs": {
|
||||
"image": {
|
||||
"id": "1a31cacd-9d19-4f32-b558-c5e4aa39ce73",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"width": {
|
||||
"id": "12f298fd-1d11-4cca-9426-01240f7ec7cf",
|
||||
"name": "width",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
},
|
||||
"height": {
|
||||
"id": "c47dabcb-44e8-40c9-992d-81dca59f598e",
|
||||
"name": "height",
|
||||
"type": "integer",
|
||||
"fieldKind": "output"
|
||||
}
|
||||
},
|
||||
"label": "",
|
||||
"isOpen": true,
|
||||
"notes": "",
|
||||
"embedWorkflow": false,
|
||||
"isIntermediate": true,
|
||||
"useCache": true,
|
||||
"version": "1.0.0"
|
||||
},
|
||||
"width": 320,
|
||||
"height": 225,
|
||||
"position": {
|
||||
"x": 3617.163483500202,
|
||||
"y": 40.5529847930888
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
|
||||
"type": "invocation",
|
||||
"data": {
|
||||
"id": "a33199c2-8340-401e-b8a2-42ffa875fc1c",
|
||||
"type": "controlnet",
|
||||
"inputs": {
|
||||
"image": {
|
||||
"id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0",
|
||||
"name": "image",
|
||||
"type": "ImageField",
|
||||
"fieldKind": "input",
|
||||
"label": ""
|
||||
},
|
||||
"control_model": {
|
||||
"id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3",
|
||||
"name": "control_model",
|
||||
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||||
719
docs/workflows/Prompt_from_File.json
Normal file
719
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Normal file
@ -0,0 +1,719 @@
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||||
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||||
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||||
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||||
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||||
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||||
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758
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758
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@ -0,0 +1,758 @@
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|
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File diff suppressed because it is too large
Load Diff
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@ -309,20 +235,21 @@
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@ -352,51 +279,66 @@
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@ -412,71 +354,71 @@
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|
||||
"target": "58c957f5-0d01-41fc-a803-b2bbf0413d4f",
|
||||
"targetHandle": "vae",
|
||||
"id": "reactflow__edge-c8d55139-f380-4695-b7f2-8b3d1e1e3db8vae-58c957f5-0d01-41fc-a803-b2bbf0413d4fvae",
|
||||
"type": "default"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
@ -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
|
||||
)
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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(
|
||||
|
||||
@ -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:
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -2,6 +2,7 @@
|
||||
|
||||
from logging import Logger
|
||||
|
||||
from invokeai.app.services.workflow_image_records.workflow_image_records_sqlite import SqliteWorkflowImageRecordsStorage
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from invokeai.version.invokeai_version import __version__
|
||||
|
||||
@ -30,6 +31,7 @@ from ..services.shared.default_graphs import create_system_graphs
|
||||
from ..services.shared.graph import GraphExecutionState, LibraryGraph
|
||||
from ..services.shared.sqlite import SqliteDatabase
|
||||
from ..services.urls.urls_default import LocalUrlService
|
||||
from ..services.workflow_records.workflow_records_sqlite import SqliteWorkflowRecordsStorage
|
||||
from .events import FastAPIEventService
|
||||
|
||||
|
||||
@ -90,6 +92,8 @@ class ApiDependencies:
|
||||
session_processor = DefaultSessionProcessor()
|
||||
session_queue = SqliteSessionQueue(db=db)
|
||||
urls = LocalUrlService()
|
||||
workflow_image_records = SqliteWorkflowImageRecordsStorage(db=db)
|
||||
workflow_records = SqliteWorkflowRecordsStorage(db=db)
|
||||
|
||||
services = InvocationServices(
|
||||
board_image_records=board_image_records,
|
||||
@ -114,6 +118,8 @@ class ApiDependencies:
|
||||
session_processor=session_processor,
|
||||
session_queue=session_queue,
|
||||
urls=urls,
|
||||
workflow_image_records=workflow_image_records,
|
||||
workflow_records=workflow_records,
|
||||
)
|
||||
|
||||
create_system_graphs(services.graph_library)
|
||||
|
||||
@ -1,13 +1,14 @@
|
||||
import io
|
||||
import traceback
|
||||
from typing import Optional
|
||||
|
||||
from fastapi import Body, HTTPException, Path, Query, Request, Response, UploadFile
|
||||
from fastapi.responses import FileResponse
|
||||
from fastapi.routing import APIRouter
|
||||
from PIL import Image
|
||||
from pydantic import BaseModel, Field
|
||||
from pydantic import BaseModel, Field, ValidationError
|
||||
|
||||
from invokeai.app.invocations.metadata import ImageMetadata
|
||||
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator, WorkflowFieldValidator
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
|
||||
from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
|
||||
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
|
||||
@ -45,17 +46,38 @@ async def upload_image(
|
||||
if not file.content_type or not file.content_type.startswith("image"):
|
||||
raise HTTPException(status_code=415, detail="Not an image")
|
||||
|
||||
contents = await file.read()
|
||||
metadata = None
|
||||
workflow = None
|
||||
|
||||
contents = await file.read()
|
||||
try:
|
||||
pil_image = Image.open(io.BytesIO(contents))
|
||||
if crop_visible:
|
||||
bbox = pil_image.getbbox()
|
||||
pil_image = pil_image.crop(bbox)
|
||||
except Exception:
|
||||
# Error opening the image
|
||||
ApiDependencies.invoker.services.logger.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=415, detail="Failed to read image")
|
||||
|
||||
# TODO: retain non-invokeai metadata on upload?
|
||||
# attempt to parse metadata from image
|
||||
metadata_raw = pil_image.info.get("invokeai_metadata", None)
|
||||
if metadata_raw:
|
||||
try:
|
||||
metadata = MetadataFieldValidator.validate_json(metadata_raw)
|
||||
except ValidationError:
|
||||
ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
|
||||
pass
|
||||
|
||||
# attempt to parse workflow from image
|
||||
workflow_raw = pil_image.info.get("invokeai_workflow", None)
|
||||
if workflow_raw is not None:
|
||||
try:
|
||||
workflow = WorkflowFieldValidator.validate_json(workflow_raw)
|
||||
except ValidationError:
|
||||
ApiDependencies.invoker.services.logger.warn("Failed to parse metadata for uploaded image")
|
||||
pass
|
||||
|
||||
try:
|
||||
image_dto = ApiDependencies.invoker.services.images.create(
|
||||
image=pil_image,
|
||||
@ -63,6 +85,8 @@ async def upload_image(
|
||||
image_category=image_category,
|
||||
session_id=session_id,
|
||||
board_id=board_id,
|
||||
metadata=metadata,
|
||||
workflow=workflow,
|
||||
is_intermediate=is_intermediate,
|
||||
)
|
||||
|
||||
@ -71,6 +95,7 @@ async def upload_image(
|
||||
|
||||
return image_dto
|
||||
except Exception:
|
||||
ApiDependencies.invoker.services.logger.error(traceback.format_exc())
|
||||
raise HTTPException(status_code=500, detail="Failed to create image")
|
||||
|
||||
|
||||
@ -87,7 +112,7 @@ async def delete_image(
|
||||
pass
|
||||
|
||||
|
||||
@images_router.post("/clear-intermediates", operation_id="clear_intermediates")
|
||||
@images_router.delete("/intermediates", operation_id="clear_intermediates")
|
||||
async def clear_intermediates() -> int:
|
||||
"""Clears all intermediates"""
|
||||
|
||||
@ -99,6 +124,17 @@ async def clear_intermediates() -> int:
|
||||
pass
|
||||
|
||||
|
||||
@images_router.get("/intermediates", operation_id="get_intermediates_count")
|
||||
async def get_intermediates_count() -> int:
|
||||
"""Gets the count of intermediate images"""
|
||||
|
||||
try:
|
||||
return ApiDependencies.invoker.services.images.get_intermediates_count()
|
||||
except Exception:
|
||||
raise HTTPException(status_code=500, detail="Failed to get intermediates")
|
||||
pass
|
||||
|
||||
|
||||
@images_router.patch(
|
||||
"/i/{image_name}",
|
||||
operation_id="update_image",
|
||||
@ -135,11 +171,11 @@ async def get_image_dto(
|
||||
@images_router.get(
|
||||
"/i/{image_name}/metadata",
|
||||
operation_id="get_image_metadata",
|
||||
response_model=ImageMetadata,
|
||||
response_model=Optional[MetadataField],
|
||||
)
|
||||
async def get_image_metadata(
|
||||
image_name: str = Path(description="The name of image to get"),
|
||||
) -> ImageMetadata:
|
||||
) -> Optional[MetadataField]:
|
||||
"""Gets an image's metadata"""
|
||||
|
||||
try:
|
||||
|
||||
@ -23,13 +23,13 @@ from ..dependencies import ApiDependencies
|
||||
models_router = APIRouter(prefix="/v1/models", tags=["models"])
|
||||
|
||||
UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
update_models_response_adapter = TypeAdapter(UpdateModelResponse)
|
||||
UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse)
|
||||
|
||||
ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
import_models_response_adapter = TypeAdapter(ImportModelResponse)
|
||||
ImportModelResponseValidator = TypeAdapter(ImportModelResponse)
|
||||
|
||||
ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
convert_models_response_adapter = TypeAdapter(ConvertModelResponse)
|
||||
ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse)
|
||||
|
||||
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
|
||||
@ -41,7 +41,7 @@ class ModelsList(BaseModel):
|
||||
model_config = ConfigDict(use_enum_values=True)
|
||||
|
||||
|
||||
models_list_adapter = TypeAdapter(ModelsList)
|
||||
ModelsListValidator = TypeAdapter(ModelsList)
|
||||
|
||||
|
||||
@models_router.get(
|
||||
@ -60,7 +60,7 @@ async def list_models(
|
||||
models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
|
||||
else:
|
||||
models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
|
||||
models = models_list_adapter.validate_python({"models": models_raw})
|
||||
models = ModelsListValidator.validate_python({"models": models_raw})
|
||||
return models
|
||||
|
||||
|
||||
@ -131,7 +131,7 @@ async def update_model(
|
||||
base_model=base_model,
|
||||
model_type=model_type,
|
||||
)
|
||||
model_response = update_models_response_adapter.validate_python(model_raw)
|
||||
model_response = UpdateModelResponseValidator.validate_python(model_raw)
|
||||
except ModelNotFoundException as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except ValueError as e:
|
||||
@ -186,7 +186,7 @@ async def import_model(
|
||||
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
model_name=info.name, base_model=info.base_model, model_type=info.model_type
|
||||
)
|
||||
return import_models_response_adapter.validate_python(model_raw)
|
||||
return ImportModelResponseValidator.validate_python(model_raw)
|
||||
|
||||
except ModelNotFoundException as e:
|
||||
logger.error(str(e))
|
||||
@ -231,7 +231,7 @@ async def add_model(
|
||||
base_model=info.base_model,
|
||||
model_type=info.model_type,
|
||||
)
|
||||
return import_models_response_adapter.validate_python(model_raw)
|
||||
return ImportModelResponseValidator.validate_python(model_raw)
|
||||
except ModelNotFoundException as e:
|
||||
logger.error(str(e))
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
@ -302,7 +302,7 @@ async def convert_model(
|
||||
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
|
||||
model_name, base_model=base_model, model_type=model_type
|
||||
)
|
||||
response = convert_models_response_adapter.validate_python(model_raw)
|
||||
response = ConvertModelResponseValidator.validate_python(model_raw)
|
||||
except ModelNotFoundException as e:
|
||||
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
|
||||
except ValueError as e:
|
||||
@ -417,7 +417,7 @@ async def merge_models(
|
||||
base_model=base_model,
|
||||
model_type=ModelType.Main,
|
||||
)
|
||||
response = convert_models_response_adapter.validate_python(model_raw)
|
||||
response = ConvertModelResponseValidator.validate_python(model_raw)
|
||||
except ModelNotFoundException:
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
|
||||
@ -12,13 +12,11 @@ from invokeai.app.services.session_queue.session_queue_common import (
|
||||
CancelByBatchIDsResult,
|
||||
ClearResult,
|
||||
EnqueueBatchResult,
|
||||
EnqueueGraphResult,
|
||||
PruneResult,
|
||||
SessionQueueItem,
|
||||
SessionQueueItemDTO,
|
||||
SessionQueueStatus,
|
||||
)
|
||||
from invokeai.app.services.shared.graph import Graph
|
||||
from invokeai.app.services.shared.pagination import CursorPaginatedResults
|
||||
|
||||
from ..dependencies import ApiDependencies
|
||||
@ -33,23 +31,6 @@ class SessionQueueAndProcessorStatus(BaseModel):
|
||||
processor: SessionProcessorStatus
|
||||
|
||||
|
||||
@session_queue_router.post(
|
||||
"/{queue_id}/enqueue_graph",
|
||||
operation_id="enqueue_graph",
|
||||
responses={
|
||||
201: {"model": EnqueueGraphResult},
|
||||
},
|
||||
)
|
||||
async def enqueue_graph(
|
||||
queue_id: str = Path(description="The queue id to perform this operation on"),
|
||||
graph: Graph = Body(description="The graph to enqueue"),
|
||||
prepend: bool = Body(default=False, description="Whether or not to prepend this batch in the queue"),
|
||||
) -> EnqueueGraphResult:
|
||||
"""Enqueues a graph for single execution."""
|
||||
|
||||
return ApiDependencies.invoker.services.session_queue.enqueue_graph(queue_id=queue_id, graph=graph, prepend=prepend)
|
||||
|
||||
|
||||
@session_queue_router.post(
|
||||
"/{queue_id}/enqueue_batch",
|
||||
operation_id="enqueue_batch",
|
||||
|
||||
20
invokeai/app/api/routers/workflows.py
Normal file
20
invokeai/app/api/routers/workflows.py
Normal file
@ -0,0 +1,20 @@
|
||||
from fastapi import APIRouter, Path
|
||||
|
||||
from invokeai.app.api.dependencies import ApiDependencies
|
||||
from invokeai.app.invocations.baseinvocation import WorkflowField
|
||||
|
||||
workflows_router = APIRouter(prefix="/v1/workflows", tags=["workflows"])
|
||||
|
||||
|
||||
@workflows_router.get(
|
||||
"/i/{workflow_id}",
|
||||
operation_id="get_workflow",
|
||||
responses={
|
||||
200: {"model": WorkflowField},
|
||||
},
|
||||
)
|
||||
async def get_workflow(
|
||||
workflow_id: str = Path(description="The workflow to get"),
|
||||
) -> WorkflowField:
|
||||
"""Gets a workflow"""
|
||||
return ApiDependencies.invoker.services.workflow_records.get(workflow_id)
|
||||
@ -1,3 +1,7 @@
|
||||
from typing import Any
|
||||
|
||||
from fastapi.responses import HTMLResponse
|
||||
|
||||
from .services.config import InvokeAIAppConfig
|
||||
|
||||
# parse_args() must be called before any other imports. if it is not called first, consumers of the config
|
||||
@ -13,17 +17,20 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
|
||||
from inspect import signature
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import uvicorn
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.middleware.gzip import GZipMiddleware
|
||||
from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
|
||||
from fastapi.openapi.utils import get_openapi
|
||||
from fastapi.responses import FileResponse
|
||||
from fastapi.staticfiles import StaticFiles
|
||||
from fastapi_events.handlers.local import local_handler
|
||||
from fastapi_events.middleware import EventHandlerASGIMiddleware
|
||||
from pydantic.json_schema import models_json_schema
|
||||
from torch.backends.mps import is_available as is_mps_available
|
||||
|
||||
# for PyCharm:
|
||||
# noinspection PyUnresolvedReferences
|
||||
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
|
||||
import invokeai.frontend.web as web_dir
|
||||
@ -31,19 +38,27 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
|
||||
|
||||
from ..backend.util.logging import InvokeAILogger
|
||||
from .api.dependencies import ApiDependencies
|
||||
from .api.routers import app_info, board_images, boards, images, models, session_queue, sessions, utilities
|
||||
from .api.routers import (
|
||||
app_info,
|
||||
board_images,
|
||||
boards,
|
||||
images,
|
||||
models,
|
||||
session_queue,
|
||||
sessions,
|
||||
utilities,
|
||||
workflows,
|
||||
)
|
||||
from .api.sockets import SocketIO
|
||||
from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
|
||||
|
||||
if torch.backends.mps.is_available():
|
||||
# noinspection PyUnresolvedReferences
|
||||
if is_mps_available():
|
||||
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
|
||||
|
||||
|
||||
app_config = InvokeAIAppConfig.get_config()
|
||||
app_config.parse_args()
|
||||
logger = InvokeAILogger.get_logger(config=app_config)
|
||||
|
||||
# fix for windows mimetypes registry entries being borked
|
||||
# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
|
||||
mimetypes.add_type("application/javascript", ".js")
|
||||
@ -71,16 +86,18 @@ app.add_middleware(
|
||||
allow_headers=app_config.allow_headers,
|
||||
)
|
||||
|
||||
app.add_middleware(GZipMiddleware, minimum_size=1000)
|
||||
|
||||
|
||||
# Add startup event to load dependencies
|
||||
@app.on_event("startup")
|
||||
async def startup_event():
|
||||
async def startup_event() -> None:
|
||||
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
|
||||
|
||||
|
||||
# Shut down threads
|
||||
@app.on_event("shutdown")
|
||||
async def shutdown_event():
|
||||
async def shutdown_event() -> None:
|
||||
ApiDependencies.shutdown()
|
||||
|
||||
|
||||
@ -88,23 +105,18 @@ async def shutdown_event():
|
||||
app.include_router(sessions.session_router, prefix="/api")
|
||||
|
||||
app.include_router(utilities.utilities_router, prefix="/api")
|
||||
|
||||
app.include_router(models.models_router, prefix="/api")
|
||||
|
||||
app.include_router(images.images_router, prefix="/api")
|
||||
|
||||
app.include_router(boards.boards_router, prefix="/api")
|
||||
|
||||
app.include_router(board_images.board_images_router, prefix="/api")
|
||||
|
||||
app.include_router(app_info.app_router, prefix="/api")
|
||||
|
||||
app.include_router(session_queue.session_queue_router, prefix="/api")
|
||||
app.include_router(workflows.workflows_router, prefix="/api")
|
||||
|
||||
|
||||
# Build a custom OpenAPI to include all outputs
|
||||
# TODO: can outputs be included on metadata of invocation schemas somehow?
|
||||
def custom_openapi():
|
||||
def custom_openapi() -> dict[str, Any]:
|
||||
if app.openapi_schema:
|
||||
return app.openapi_schema
|
||||
openapi_schema = get_openapi(
|
||||
@ -159,7 +171,6 @@ def custom_openapi():
|
||||
# print(f"Config with name {name} already defined")
|
||||
continue
|
||||
|
||||
# "BaseModelType":{"title":"BaseModelType","description":"An enumeration.","enum":["sd-1","sd-2"],"type":"string"}
|
||||
openapi_schema["components"]["schemas"][name] = dict(
|
||||
title=name,
|
||||
description="An enumeration.",
|
||||
@ -173,34 +184,43 @@ def custom_openapi():
|
||||
|
||||
app.openapi = custom_openapi # type: ignore [method-assign] # this is a valid assignment
|
||||
|
||||
# Override API doc favicons
|
||||
app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
|
||||
|
||||
|
||||
@app.get("/docs", include_in_schema=False)
|
||||
def overridden_swagger():
|
||||
def overridden_swagger() -> HTMLResponse:
|
||||
return get_swagger_ui_html(
|
||||
openapi_url=app.openapi_url,
|
||||
openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
|
||||
title=app.title,
|
||||
swagger_favicon_url="/static/favicon.ico",
|
||||
swagger_favicon_url="/static/docs/favicon.ico",
|
||||
)
|
||||
|
||||
|
||||
@app.get("/redoc", include_in_schema=False)
|
||||
def overridden_redoc():
|
||||
def overridden_redoc() -> HTMLResponse:
|
||||
return get_redoc_html(
|
||||
openapi_url=app.openapi_url,
|
||||
openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
|
||||
title=app.title,
|
||||
redoc_favicon_url="/static/favicon.ico",
|
||||
redoc_favicon_url="/static/docs/favicon.ico",
|
||||
)
|
||||
|
||||
|
||||
# Must mount *after* the other routes else it borks em
|
||||
app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui")
|
||||
web_root_path = Path(list(web_dir.__path__)[0])
|
||||
|
||||
|
||||
def invoke_api():
|
||||
def find_port(port: int):
|
||||
# Cannot add headers to StaticFiles, so we must serve index.html with a custom route
|
||||
# Add cache-control: no-store header to prevent caching of index.html, which leads to broken UIs at release
|
||||
@app.get("/", include_in_schema=False, name="ui_root")
|
||||
def get_index() -> FileResponse:
|
||||
return FileResponse(Path(web_root_path, "dist/index.html"), headers={"Cache-Control": "no-store"})
|
||||
|
||||
|
||||
# # Must mount *after* the other routes else it borks em
|
||||
app.mount("/static", StaticFiles(directory=Path(web_root_path, "static/")), name="static") # docs favicon is in here
|
||||
app.mount("/assets", StaticFiles(directory=Path(web_root_path, "dist/assets/")), name="assets")
|
||||
app.mount("/locales", StaticFiles(directory=Path(web_root_path, "dist/locales/")), name="locales")
|
||||
|
||||
|
||||
def invoke_api() -> None:
|
||||
def find_port(port: int) -> int:
|
||||
"""Find a port not in use starting at given port"""
|
||||
# Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
|
||||
# https://github.com/WaylonWalker
|
||||
@ -235,7 +255,7 @@ def invoke_api():
|
||||
app=app,
|
||||
host=app_config.host,
|
||||
port=port,
|
||||
loop=loop,
|
||||
loop="asyncio",
|
||||
log_level=app_config.log_level,
|
||||
)
|
||||
server = uvicorn.Server(config)
|
||||
|
||||
@ -1,312 +0,0 @@
|
||||
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
|
||||
|
||||
import argparse
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, Callable, Iterable, Literal, Union, get_args, get_origin, get_type_hints
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import networkx as nx
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
import invokeai.backend.util.logging as logger
|
||||
|
||||
from ..invocations.baseinvocation import BaseInvocation
|
||||
from ..invocations.image import ImageField
|
||||
from ..services.graph import Edge, GraphExecutionState, LibraryGraph
|
||||
from ..services.invoker import Invoker
|
||||
|
||||
|
||||
def add_field_argument(command_parser, name: str, field, default_override=None):
|
||||
default = (
|
||||
default_override
|
||||
if default_override is not None
|
||||
else field.default
|
||||
if field.default_factory is None
|
||||
else field.default_factory()
|
||||
)
|
||||
if get_origin(field.annotation) == Literal:
|
||||
allowed_values = get_args(field.annotation)
|
||||
allowed_types = set()
|
||||
for val in allowed_values:
|
||||
allowed_types.add(type(val))
|
||||
allowed_types_list = list(allowed_types)
|
||||
field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list] # type: ignore
|
||||
|
||||
command_parser.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field_type,
|
||||
default=default,
|
||||
choices=allowed_values,
|
||||
help=field.description,
|
||||
)
|
||||
else:
|
||||
command_parser.add_argument(
|
||||
f"--{name}",
|
||||
dest=name,
|
||||
type=field.annotation,
|
||||
default=default,
|
||||
help=field.description,
|
||||
)
|
||||
|
||||
|
||||
def add_parsers(
|
||||
subparsers,
|
||||
commands: list[type],
|
||||
command_field: str = "type",
|
||||
exclude_fields: list[str] = ["id", "type"],
|
||||
add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None,
|
||||
):
|
||||
"""Adds parsers for each command to the subparsers"""
|
||||
|
||||
# Create subparsers for each command
|
||||
for command in commands:
|
||||
hints = get_type_hints(command)
|
||||
cmd_name = get_args(hints[command_field])[0]
|
||||
command_parser = subparsers.add_parser(cmd_name, help=command.__doc__)
|
||||
|
||||
if add_arguments is not None:
|
||||
add_arguments(command_parser)
|
||||
|
||||
# Convert all fields to arguments
|
||||
fields = command.__fields__ # type: ignore
|
||||
for name, field in fields.items():
|
||||
if name in exclude_fields:
|
||||
continue
|
||||
|
||||
add_field_argument(command_parser, name, field)
|
||||
|
||||
|
||||
def add_graph_parsers(
|
||||
subparsers, graphs: list[LibraryGraph], add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
|
||||
):
|
||||
for graph in graphs:
|
||||
command_parser = subparsers.add_parser(graph.name, help=graph.description)
|
||||
|
||||
if add_arguments is not None:
|
||||
add_arguments(command_parser)
|
||||
|
||||
# Add arguments for inputs
|
||||
for exposed_input in graph.exposed_inputs:
|
||||
node = graph.graph.get_node(exposed_input.node_path)
|
||||
field = node.__fields__[exposed_input.field]
|
||||
default_override = getattr(node, exposed_input.field)
|
||||
add_field_argument(command_parser, exposed_input.alias, field, default_override)
|
||||
|
||||
|
||||
class CliContext:
|
||||
invoker: Invoker
|
||||
session: GraphExecutionState
|
||||
parser: argparse.ArgumentParser
|
||||
defaults: dict[str, Any]
|
||||
graph_nodes: dict[str, str]
|
||||
nodes_added: list[str]
|
||||
|
||||
def __init__(self, invoker: Invoker, session: GraphExecutionState, parser: argparse.ArgumentParser):
|
||||
self.invoker = invoker
|
||||
self.session = session
|
||||
self.parser = parser
|
||||
self.defaults = dict()
|
||||
self.graph_nodes = dict()
|
||||
self.nodes_added = list()
|
||||
|
||||
def get_session(self):
|
||||
self.session = self.invoker.services.graph_execution_manager.get(self.session.id)
|
||||
return self.session
|
||||
|
||||
def reset(self):
|
||||
self.session = self.invoker.create_execution_state()
|
||||
self.graph_nodes = dict()
|
||||
self.nodes_added = list()
|
||||
# Leave defaults unchanged
|
||||
|
||||
def add_node(self, node: BaseInvocation):
|
||||
self.get_session()
|
||||
self.session.graph.add_node(node)
|
||||
self.nodes_added.append(node.id)
|
||||
self.invoker.services.graph_execution_manager.set(self.session)
|
||||
|
||||
def add_edge(self, edge: Edge):
|
||||
self.get_session()
|
||||
self.session.add_edge(edge)
|
||||
self.invoker.services.graph_execution_manager.set(self.session)
|
||||
|
||||
|
||||
class ExitCli(Exception):
|
||||
"""Exception to exit the CLI"""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
class BaseCommand(ABC, BaseModel):
|
||||
"""A CLI command"""
|
||||
|
||||
# All commands must include a type name like this:
|
||||
|
||||
@classmethod
|
||||
def get_all_subclasses(cls):
|
||||
subclasses = []
|
||||
toprocess = [cls]
|
||||
while len(toprocess) > 0:
|
||||
next = toprocess.pop(0)
|
||||
next_subclasses = next.__subclasses__()
|
||||
subclasses.extend(next_subclasses)
|
||||
toprocess.extend(next_subclasses)
|
||||
return subclasses
|
||||
|
||||
@classmethod
|
||||
def get_commands(cls):
|
||||
return tuple(BaseCommand.get_all_subclasses())
|
||||
|
||||
@classmethod
|
||||
def get_commands_map(cls):
|
||||
# Get the type strings out of the literals and into a dictionary
|
||||
return dict(map(lambda t: (get_args(get_type_hints(t)["type"])[0], t), BaseCommand.get_all_subclasses()))
|
||||
|
||||
@abstractmethod
|
||||
def run(self, context: CliContext) -> None:
|
||||
"""Run the command. Raise ExitCli to exit."""
|
||||
pass
|
||||
|
||||
|
||||
class ExitCommand(BaseCommand):
|
||||
"""Exits the CLI"""
|
||||
|
||||
type: Literal["exit"] = "exit"
|
||||
|
||||
def run(self, context: CliContext) -> None:
|
||||
raise ExitCli()
|
||||
|
||||
|
||||
class HelpCommand(BaseCommand):
|
||||
"""Shows help"""
|
||||
|
||||
type: Literal["help"] = "help"
|
||||
|
||||
def run(self, context: CliContext) -> None:
|
||||
context.parser.print_help()
|
||||
|
||||
|
||||
def get_graph_execution_history(
|
||||
graph_execution_state: GraphExecutionState,
|
||||
) -> Iterable[str]:
|
||||
"""Gets the history of fully-executed invocations for a graph execution"""
|
||||
return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes)
|
||||
|
||||
|
||||
def get_invocation_command(invocation) -> str:
|
||||
fields = invocation.__fields__.items()
|
||||
type_hints = get_type_hints(type(invocation))
|
||||
command = [invocation.type]
|
||||
for name, field in fields:
|
||||
if name in ["id", "type"]:
|
||||
continue
|
||||
|
||||
# TODO: add links
|
||||
|
||||
# Skip image fields when serializing command
|
||||
type_hint = type_hints.get(name) or None
|
||||
if type_hint is ImageField or ImageField in get_args(type_hint):
|
||||
continue
|
||||
|
||||
field_value = getattr(invocation, name)
|
||||
field_default = field.default
|
||||
if field_value != field_default:
|
||||
if type_hint is str or str in get_args(type_hint):
|
||||
command.append(f'--{name} "{field_value}"')
|
||||
else:
|
||||
command.append(f"--{name} {field_value}")
|
||||
|
||||
return " ".join(command)
|
||||
|
||||
|
||||
class HistoryCommand(BaseCommand):
|
||||
"""Shows the invocation history"""
|
||||
|
||||
type: Literal["history"] = "history"
|
||||
|
||||
# Inputs
|
||||
# fmt: off
|
||||
count: int = Field(default=5, gt=0, description="The number of history entries to show")
|
||||
# fmt: on
|
||||
|
||||
def run(self, context: CliContext) -> None:
|
||||
history = list(get_graph_execution_history(context.get_session()))
|
||||
for i in range(min(self.count, len(history))):
|
||||
entry_id = history[-1 - i]
|
||||
entry = context.get_session().graph.get_node(entry_id)
|
||||
logger.info(f"{entry_id}: {get_invocation_command(entry)}")
|
||||
|
||||
|
||||
class SetDefaultCommand(BaseCommand):
|
||||
"""Sets a default value for a field"""
|
||||
|
||||
type: Literal["default"] = "default"
|
||||
|
||||
# Inputs
|
||||
# fmt: off
|
||||
field: str = Field(description="The field to set the default for")
|
||||
value: str = Field(description="The value to set the default to, or None to clear the default")
|
||||
# fmt: on
|
||||
|
||||
def run(self, context: CliContext) -> None:
|
||||
if self.value is None:
|
||||
if self.field in context.defaults:
|
||||
del context.defaults[self.field]
|
||||
else:
|
||||
context.defaults[self.field] = self.value
|
||||
|
||||
|
||||
class DrawGraphCommand(BaseCommand):
|
||||
"""Debugs a graph"""
|
||||
|
||||
type: Literal["draw_graph"] = "draw_graph"
|
||||
|
||||
def run(self, context: CliContext) -> None:
|
||||
session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
|
||||
nxgraph = session.graph.nx_graph_flat()
|
||||
|
||||
# Draw the networkx graph
|
||||
plt.figure(figsize=(20, 20))
|
||||
pos = nx.spectral_layout(nxgraph)
|
||||
nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
|
||||
nx.draw_networkx_edges(nxgraph, pos, width=2)
|
||||
nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
|
||||
plt.axis("off")
|
||||
plt.show()
|
||||
|
||||
|
||||
class DrawExecutionGraphCommand(BaseCommand):
|
||||
"""Debugs an execution graph"""
|
||||
|
||||
type: Literal["draw_xgraph"] = "draw_xgraph"
|
||||
|
||||
def run(self, context: CliContext) -> None:
|
||||
session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
|
||||
nxgraph = session.execution_graph.nx_graph_flat()
|
||||
|
||||
# Draw the networkx graph
|
||||
plt.figure(figsize=(20, 20))
|
||||
pos = nx.spectral_layout(nxgraph)
|
||||
nx.draw_networkx_nodes(nxgraph, pos, node_size=1000)
|
||||
nx.draw_networkx_edges(nxgraph, pos, width=2)
|
||||
nx.draw_networkx_labels(nxgraph, pos, font_size=20, font_family="sans-serif")
|
||||
plt.axis("off")
|
||||
plt.show()
|
||||
|
||||
|
||||
class SortedHelpFormatter(argparse.HelpFormatter):
|
||||
def _iter_indented_subactions(self, action):
|
||||
try:
|
||||
get_subactions = action._get_subactions
|
||||
except AttributeError:
|
||||
pass
|
||||
else:
|
||||
self._indent()
|
||||
if isinstance(action, argparse._SubParsersAction):
|
||||
for subaction in sorted(get_subactions(), key=lambda x: x.dest):
|
||||
yield subaction
|
||||
else:
|
||||
for subaction in get_subactions():
|
||||
yield subaction
|
||||
self._dedent()
|
||||
@ -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)
|
||||
@ -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()
|
||||
@ -1,8 +1,28 @@
|
||||
import os
|
||||
import shutil
|
||||
import sys
|
||||
from importlib.util import module_from_spec, spec_from_file_location
|
||||
from pathlib import Path
|
||||
|
||||
__all__ = []
|
||||
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
||||
|
||||
dirname = os.path.dirname(os.path.abspath(__file__))
|
||||
for f in os.listdir(dirname):
|
||||
if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
|
||||
__all__.append(f[:-3])
|
||||
custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute())
|
||||
custom_nodes_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
custom_nodes_init_path = str(custom_nodes_path / "__init__.py")
|
||||
custom_nodes_readme_path = str(custom_nodes_path / "README.md")
|
||||
|
||||
# copy our custom nodes __init__.py to the custom nodes directory
|
||||
shutil.copy(Path(__file__).parent / "custom_nodes/init.py", custom_nodes_init_path)
|
||||
shutil.copy(Path(__file__).parent / "custom_nodes/README.md", custom_nodes_readme_path)
|
||||
|
||||
# Import custom nodes, see https://docs.python.org/3/library/importlib.html#importing-programmatically
|
||||
spec = spec_from_file_location("custom_nodes", custom_nodes_init_path)
|
||||
if spec is None or spec.loader is None:
|
||||
raise RuntimeError(f"Could not load custom nodes from {custom_nodes_init_path}")
|
||||
module = module_from_spec(spec)
|
||||
sys.modules[spec.name] = module
|
||||
spec.loader.exec_module(module)
|
||||
|
||||
# add core nodes to __all__
|
||||
python_files = filter(lambda f: not f.name.startswith("_"), Path(__file__).parent.glob("*.py"))
|
||||
__all__ = list(f.stem for f in python_files) # type: ignore
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import inspect
|
||||
import re
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
@ -11,8 +11,8 @@ from types import UnionType
|
||||
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
|
||||
|
||||
import semver
|
||||
from pydantic import BaseModel, ConfigDict, Field, create_model, field_validator
|
||||
from pydantic.fields import _Unset
|
||||
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
|
||||
from pydantic.fields import FieldInfo, _Unset
|
||||
from pydantic_core import PydanticUndefined
|
||||
|
||||
from invokeai.app.services.config.config_default import InvokeAIAppConfig
|
||||
@ -26,6 +26,10 @@ class InvalidVersionError(ValueError):
|
||||
pass
|
||||
|
||||
|
||||
class InvalidFieldError(TypeError):
|
||||
pass
|
||||
|
||||
|
||||
class FieldDescriptions:
|
||||
denoising_start = "When to start denoising, expressed a percentage of total steps"
|
||||
denoising_end = "When to stop denoising, expressed a percentage of total steps"
|
||||
@ -60,7 +64,12 @@ class FieldDescriptions:
|
||||
denoised_latents = "Denoised latents tensor"
|
||||
latents = "Latents tensor"
|
||||
strength = "Strength of denoising (proportional to steps)"
|
||||
core_metadata = "Optional core metadata to be written to image"
|
||||
metadata = "Optional metadata to be saved with the image"
|
||||
metadata_collection = "Collection of Metadata"
|
||||
metadata_item_polymorphic = "A single metadata item or collection of metadata items"
|
||||
metadata_item_label = "Label for this metadata item"
|
||||
metadata_item_value = "The value for this metadata item (may be any type)"
|
||||
workflow = "Optional workflow to be saved with the image"
|
||||
interp_mode = "Interpolation mode"
|
||||
torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
|
||||
fp32 = "Whether or not to use full float32 precision"
|
||||
@ -171,8 +180,12 @@ class UIType(str, Enum):
|
||||
Scheduler = "Scheduler"
|
||||
WorkflowField = "WorkflowField"
|
||||
IsIntermediate = "IsIntermediate"
|
||||
MetadataField = "MetadataField"
|
||||
BoardField = "BoardField"
|
||||
Any = "Any"
|
||||
MetadataItem = "MetadataItem"
|
||||
MetadataItemCollection = "MetadataItemCollection"
|
||||
MetadataItemPolymorphic = "MetadataItemPolymorphic"
|
||||
MetadataDict = "MetadataDict"
|
||||
# endregion
|
||||
|
||||
|
||||
@ -298,6 +311,7 @@ def InputField(
|
||||
ui_order=ui_order,
|
||||
item_default=item_default,
|
||||
ui_choice_labels=ui_choice_labels,
|
||||
_field_kind="input",
|
||||
)
|
||||
|
||||
field_args = dict(
|
||||
@ -440,6 +454,7 @@ def OutputField(
|
||||
ui_type=ui_type,
|
||||
ui_hidden=ui_hidden,
|
||||
ui_order=ui_order,
|
||||
_field_kind="output",
|
||||
),
|
||||
)
|
||||
|
||||
@ -523,6 +538,7 @@ class BaseInvocationOutput(BaseModel):
|
||||
schema["required"].extend(["type"])
|
||||
|
||||
model_config = ConfigDict(
|
||||
protected_namespaces=(),
|
||||
validate_assignment=True,
|
||||
json_schema_serialization_defaults_required=True,
|
||||
json_schema_extra=json_schema_extra,
|
||||
@ -545,9 +561,6 @@ class MissingInputException(Exception):
|
||||
|
||||
class BaseInvocation(ABC, BaseModel):
|
||||
"""
|
||||
A node to process inputs and produce outputs.
|
||||
May use dependency injection in __init__ to receive providers.
|
||||
|
||||
All invocations must use the `@invocation` decorator to provide their unique type.
|
||||
"""
|
||||
|
||||
@ -663,46 +676,93 @@ class BaseInvocation(ABC, BaseModel):
|
||||
id: str = Field(
|
||||
default_factory=uuid_string,
|
||||
description="The id of this instance of an invocation. Must be unique among all instances of invocations.",
|
||||
json_schema_extra=dict(_field_kind="internal"),
|
||||
)
|
||||
is_intermediate: Optional[bool] = Field(
|
||||
is_intermediate: bool = Field(
|
||||
default=False,
|
||||
description="Whether or not this is an intermediate invocation.",
|
||||
json_schema_extra=dict(ui_type=UIType.IsIntermediate),
|
||||
json_schema_extra=dict(ui_type=UIType.IsIntermediate, _field_kind="internal"),
|
||||
)
|
||||
workflow: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The workflow to save with the image",
|
||||
json_schema_extra=dict(ui_type=UIType.WorkflowField),
|
||||
use_cache: bool = Field(
|
||||
default=True, description="Whether or not to use the cache", json_schema_extra=dict(_field_kind="internal")
|
||||
)
|
||||
use_cache: Optional[bool] = Field(
|
||||
default=True,
|
||||
description="Whether or not to use the cache",
|
||||
)
|
||||
|
||||
@field_validator("workflow", mode="before")
|
||||
@classmethod
|
||||
def validate_workflow_is_json(cls, v):
|
||||
"""We don't have a workflow schema in the backend, so we just check that it's valid JSON"""
|
||||
if v is None:
|
||||
return None
|
||||
try:
|
||||
json.loads(v)
|
||||
except json.decoder.JSONDecodeError:
|
||||
raise ValueError("Workflow must be valid JSON")
|
||||
return v
|
||||
|
||||
UIConfig: ClassVar[Type[UIConfigBase]]
|
||||
|
||||
model_config = ConfigDict(
|
||||
protected_namespaces=(),
|
||||
validate_assignment=True,
|
||||
json_schema_extra=json_schema_extra,
|
||||
json_schema_serialization_defaults_required=True,
|
||||
coerce_numbers_to_str=True,
|
||||
)
|
||||
|
||||
|
||||
TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
|
||||
|
||||
|
||||
RESERVED_INPUT_FIELD_NAMES = {
|
||||
"id",
|
||||
"is_intermediate",
|
||||
"use_cache",
|
||||
"type",
|
||||
"workflow",
|
||||
"metadata",
|
||||
}
|
||||
|
||||
RESERVED_OUTPUT_FIELD_NAMES = {"type"}
|
||||
|
||||
|
||||
class _Model(BaseModel):
|
||||
pass
|
||||
|
||||
|
||||
# Get all pydantic model attrs, methods, etc
|
||||
RESERVED_PYDANTIC_FIELD_NAMES = set(map(lambda m: m[0], inspect.getmembers(_Model())))
|
||||
|
||||
|
||||
def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None:
|
||||
"""
|
||||
Validates the fields of an invocation or invocation output:
|
||||
- must not override any pydantic reserved fields
|
||||
- must be created via `InputField`, `OutputField`, or be an internal field defined in this file
|
||||
"""
|
||||
for name, field in model_fields.items():
|
||||
if name in RESERVED_PYDANTIC_FIELD_NAMES:
|
||||
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved by pydantic)')
|
||||
|
||||
field_kind = (
|
||||
# _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
|
||||
field.json_schema_extra.get("_field_kind", None)
|
||||
if field.json_schema_extra
|
||||
else None
|
||||
)
|
||||
|
||||
# must have a field_kind
|
||||
if field_kind is None or field_kind not in {"input", "output", "internal"}:
|
||||
raise InvalidFieldError(
|
||||
f'Invalid field definition for "{name}" on "{model_type}" (maybe it\'s not an InputField or OutputField?)'
|
||||
)
|
||||
|
||||
if field_kind == "input" and name in RESERVED_INPUT_FIELD_NAMES:
|
||||
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved input field name)')
|
||||
|
||||
if field_kind == "output" and name in RESERVED_OUTPUT_FIELD_NAMES:
|
||||
raise InvalidFieldError(f'Invalid field name "{name}" on "{model_type}" (reserved output field name)')
|
||||
|
||||
# internal fields *must* be in the reserved list
|
||||
if (
|
||||
field_kind == "internal"
|
||||
and name not in RESERVED_INPUT_FIELD_NAMES
|
||||
and name not in RESERVED_OUTPUT_FIELD_NAMES
|
||||
):
|
||||
raise InvalidFieldError(
|
||||
f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)'
|
||||
)
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def invocation(
|
||||
invocation_type: str,
|
||||
title: Optional[str] = None,
|
||||
@ -712,7 +772,7 @@ def invocation(
|
||||
use_cache: Optional[bool] = True,
|
||||
) -> Callable[[Type[TBaseInvocation]], Type[TBaseInvocation]]:
|
||||
"""
|
||||
Adds metadata to an invocation.
|
||||
Registers an invocation.
|
||||
|
||||
:param str invocation_type: The type of the invocation. Must be unique among all invocations.
|
||||
:param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None.
|
||||
@ -731,6 +791,8 @@ def invocation(
|
||||
if invocation_type in BaseInvocation.get_invocation_types():
|
||||
raise ValueError(f'Invocation type "{invocation_type}" already exists')
|
||||
|
||||
validate_fields(cls.model_fields, invocation_type)
|
||||
|
||||
# Add OpenAPI schema extras
|
||||
uiconf_name = cls.__qualname__ + ".UIConfig"
|
||||
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
|
||||
@ -761,8 +823,7 @@ def invocation(
|
||||
|
||||
invocation_type_annotation = Literal[invocation_type] # type: ignore
|
||||
invocation_type_field = Field(
|
||||
title="type",
|
||||
default=invocation_type,
|
||||
title="type", default=invocation_type, json_schema_extra=dict(_field_kind="internal")
|
||||
)
|
||||
|
||||
docstring = cls.__doc__
|
||||
@ -803,13 +864,12 @@ def invocation_output(
|
||||
if output_type in BaseInvocationOutput.get_output_types():
|
||||
raise ValueError(f'Invocation type "{output_type}" already exists')
|
||||
|
||||
validate_fields(cls.model_fields, output_type)
|
||||
|
||||
# Add the output type to the model.
|
||||
|
||||
output_type_annotation = Literal[output_type] # type: ignore
|
||||
output_type_field = Field(
|
||||
title="type",
|
||||
default=output_type,
|
||||
)
|
||||
output_type_field = Field(title="type", default=output_type, json_schema_extra=dict(_field_kind="internal"))
|
||||
|
||||
docstring = cls.__doc__
|
||||
cls = create_model(
|
||||
@ -827,4 +887,37 @@ def invocation_output(
|
||||
return wrapper
|
||||
|
||||
|
||||
GenericBaseModel = TypeVar("GenericBaseModel", bound=BaseModel)
|
||||
class WorkflowField(RootModel):
|
||||
"""
|
||||
Pydantic model for workflows with custom root of type dict[str, Any].
|
||||
Workflows are stored without a strict schema.
|
||||
"""
|
||||
|
||||
root: dict[str, Any] = Field(description="The workflow")
|
||||
|
||||
|
||||
WorkflowFieldValidator = TypeAdapter(WorkflowField)
|
||||
|
||||
|
||||
class WithWorkflow(BaseModel):
|
||||
workflow: Optional[WorkflowField] = Field(
|
||||
default=None, description=FieldDescriptions.workflow, json_schema_extra=dict(_field_kind="internal")
|
||||
)
|
||||
|
||||
|
||||
class MetadataField(RootModel):
|
||||
"""
|
||||
Pydantic model for metadata with custom root of type dict[str, Any].
|
||||
Metadata is stored without a strict schema.
|
||||
"""
|
||||
|
||||
root: dict[str, Any] = Field(description="The metadata")
|
||||
|
||||
|
||||
MetadataFieldValidator = TypeAdapter(MetadataField)
|
||||
|
||||
|
||||
class WithMetadata(BaseModel):
|
||||
metadata: Optional[MetadataField] = Field(
|
||||
default=None, description=FieldDescriptions.metadata, json_schema_extra=dict(_field_kind="internal")
|
||||
)
|
||||
|
||||
@ -108,13 +108,14 @@ class CompelInvocation(BaseInvocation):
|
||||
print(f'Warn: trigger: "{trigger}" not found')
|
||||
|
||||
with (
|
||||
ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
),
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),
|
||||
text_encoder_info as text_encoder,
|
||||
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
|
||||
ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
|
||||
):
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
@ -229,13 +230,14 @@ class SDXLPromptInvocationBase:
|
||||
print(f'Warn: trigger: "{trigger}" not found')
|
||||
|
||||
with (
|
||||
ModelPatcher.apply_lora(text_encoder_info.context.model, _lora_loader(), lora_prefix),
|
||||
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
|
||||
tokenizer,
|
||||
ti_manager,
|
||||
),
|
||||
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),
|
||||
text_encoder_info as text_encoder,
|
||||
# Apply the LoRA after text_encoder has been moved to its target device for faster patching.
|
||||
ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
|
||||
):
|
||||
compel = Compel(
|
||||
tokenizer=tokenizer,
|
||||
|
||||
@ -38,6 +38,8 @@ from .baseinvocation import (
|
||||
InputField,
|
||||
InvocationContext,
|
||||
OutputField,
|
||||
WithMetadata,
|
||||
WithWorkflow,
|
||||
invocation,
|
||||
invocation_output,
|
||||
)
|
||||
@ -127,12 +129,12 @@ class ControlNetInvocation(BaseInvocation):
|
||||
|
||||
|
||||
# This invocation exists for other invocations to subclass it - do not register with @invocation!
|
||||
class ImageProcessorInvocation(BaseInvocation):
|
||||
class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Base class for invocations that preprocess images for ControlNet"""
|
||||
|
||||
image: ImageField = InputField(description="The image to process")
|
||||
|
||||
def run_processor(self, image):
|
||||
def run_processor(self, image: Image.Image) -> Image.Image:
|
||||
# superclass just passes through image without processing
|
||||
return image
|
||||
|
||||
@ -150,6 +152,7 @@ class ImageProcessorInvocation(BaseInvocation):
|
||||
session_id=context.graph_execution_state_id,
|
||||
node_id=self.id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
|
||||
51
invokeai/app/invocations/custom_nodes/README.md
Normal file
51
invokeai/app/invocations/custom_nodes/README.md
Normal file
@ -0,0 +1,51 @@
|
||||
# Custom Nodes / Node Packs
|
||||
|
||||
Copy your node packs to this directory.
|
||||
|
||||
When nodes are added or changed, you must restart the app to see the changes.
|
||||
|
||||
## Directory Structure
|
||||
|
||||
For a node pack to be loaded, it must be placed in a directory alongside this
|
||||
file. Here's an example structure:
|
||||
|
||||
```py
|
||||
.
|
||||
├── __init__.py # Invoke-managed custom node loader
|
||||
│
|
||||
├── cool_node
|
||||
│ ├── __init__.py # see example below
|
||||
│ └── cool_node.py
|
||||
│
|
||||
└── my_node_pack
|
||||
├── __init__.py # see example below
|
||||
├── tasty_node.py
|
||||
├── bodacious_node.py
|
||||
├── utils.py
|
||||
└── extra_nodes
|
||||
└── fancy_node.py
|
||||
```
|
||||
|
||||
## Node Pack `__init__.py`
|
||||
|
||||
Each node pack must have an `__init__.py` file that imports its nodes.
|
||||
|
||||
The structure of each node or node pack is otherwise not important.
|
||||
|
||||
Here are examples, based on the example directory structure.
|
||||
|
||||
### `cool_node/__init__.py`
|
||||
|
||||
```py
|
||||
from .cool_node import CoolInvocation
|
||||
```
|
||||
|
||||
### `my_node_pack/__init__.py`
|
||||
|
||||
```py
|
||||
from .tasty_node import TastyInvocation
|
||||
from .bodacious_node import BodaciousInvocation
|
||||
from .extra_nodes.fancy_node import FancyInvocation
|
||||
```
|
||||
|
||||
Only nodes imported in the `__init__.py` file are loaded.
|
||||
51
invokeai/app/invocations/custom_nodes/init.py
Normal file
51
invokeai/app/invocations/custom_nodes/init.py
Normal file
@ -0,0 +1,51 @@
|
||||
"""
|
||||
Invoke-managed custom node loader. See README.md for more information.
|
||||
"""
|
||||
|
||||
import sys
|
||||
from importlib.util import module_from_spec, spec_from_file_location
|
||||
from pathlib import Path
|
||||
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
|
||||
logger = InvokeAILogger.get_logger()
|
||||
loaded_count = 0
|
||||
|
||||
|
||||
for d in Path(__file__).parent.iterdir():
|
||||
# skip files
|
||||
if not d.is_dir():
|
||||
continue
|
||||
|
||||
# skip hidden directories
|
||||
if d.name.startswith("_") or d.name.startswith("."):
|
||||
continue
|
||||
|
||||
# skip directories without an `__init__.py`
|
||||
init = d / "__init__.py"
|
||||
if not init.exists():
|
||||
continue
|
||||
|
||||
module_name = init.parent.stem
|
||||
|
||||
# skip if already imported
|
||||
if module_name in globals():
|
||||
continue
|
||||
|
||||
# we have a legit module to import
|
||||
spec = spec_from_file_location(module_name, init.absolute())
|
||||
|
||||
if spec is None or spec.loader is None:
|
||||
logger.warn(f"Could not load {init}")
|
||||
continue
|
||||
|
||||
module = module_from_spec(spec)
|
||||
sys.modules[spec.name] = module
|
||||
spec.loader.exec_module(module)
|
||||
|
||||
loaded_count += 1
|
||||
|
||||
del init, module_name
|
||||
|
||||
|
||||
logger.info(f"Loaded {loaded_count} modules from {Path(__file__).parent}")
|
||||
@ -8,11 +8,11 @@ from PIL import Image, ImageOps
|
||||
from invokeai.app.invocations.primitives import ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
|
||||
|
||||
|
||||
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
|
||||
class CvInpaintInvocation(BaseInvocation):
|
||||
class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Simple inpaint using opencv."""
|
||||
|
||||
image: ImageField = InputField(description="The image to inpaint")
|
||||
|
||||
@ -16,6 +16,8 @@ from invokeai.app.invocations.baseinvocation import (
|
||||
InputField,
|
||||
InvocationContext,
|
||||
OutputField,
|
||||
WithMetadata,
|
||||
WithWorkflow,
|
||||
invocation,
|
||||
invocation_output,
|
||||
)
|
||||
@ -437,7 +439,7 @@ def get_faces_list(
|
||||
|
||||
|
||||
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
|
||||
class FaceOffInvocation(BaseInvocation):
|
||||
class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Bound, extract, and mask a face from an image using MediaPipe detection"""
|
||||
|
||||
image: ImageField = InputField(description="Image for face detection")
|
||||
@ -531,7 +533,7 @@ class FaceOffInvocation(BaseInvocation):
|
||||
|
||||
|
||||
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
|
||||
class FaceMaskInvocation(BaseInvocation):
|
||||
class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Face mask creation using mediapipe face detection"""
|
||||
|
||||
image: ImageField = InputField(description="Image to face detect")
|
||||
@ -650,7 +652,7 @@ class FaceMaskInvocation(BaseInvocation):
|
||||
@invocation(
|
||||
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
|
||||
)
|
||||
class FaceIdentifierInvocation(BaseInvocation):
|
||||
class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
|
||||
|
||||
image: ImageField = InputField(description="Image to face detect")
|
||||
|
||||
@ -7,13 +7,21 @@ import cv2
|
||||
import numpy
|
||||
from PIL import Image, ImageChops, ImageFilter, ImageOps
|
||||
|
||||
from invokeai.app.invocations.metadata import CoreMetadata
|
||||
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
|
||||
from invokeai.backend.image_util.safety_checker import SafetyChecker
|
||||
|
||||
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, invocation
|
||||
from .baseinvocation import (
|
||||
BaseInvocation,
|
||||
FieldDescriptions,
|
||||
Input,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
WithMetadata,
|
||||
WithWorkflow,
|
||||
invocation,
|
||||
)
|
||||
|
||||
|
||||
@invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")
|
||||
@ -36,14 +44,8 @@ class ShowImageInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"blank_image",
|
||||
title="Blank Image",
|
||||
tags=["image"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class BlankImageInvocation(BaseInvocation):
|
||||
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
|
||||
class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Creates a blank image and forwards it to the pipeline"""
|
||||
|
||||
width: int = InputField(default=512, description="The width of the image")
|
||||
@ -61,6 +63,7 @@ class BlankImageInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -71,14 +74,8 @@ class BlankImageInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_crop",
|
||||
title="Crop Image",
|
||||
tags=["image", "crop"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageCropInvocation(BaseInvocation):
|
||||
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
|
||||
class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Crops an image to a specified box. The box can be outside of the image."""
|
||||
|
||||
image: ImageField = InputField(description="The image to crop")
|
||||
@ -100,6 +97,7 @@ class ImageCropInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -110,14 +108,8 @@ class ImageCropInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_paste",
|
||||
title="Paste Image",
|
||||
tags=["image", "paste"],
|
||||
category="image",
|
||||
version="1.0.1",
|
||||
)
|
||||
class ImagePasteInvocation(BaseInvocation):
|
||||
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
|
||||
class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Pastes an image into another image."""
|
||||
|
||||
base_image: ImageField = InputField(description="The base image")
|
||||
@ -159,6 +151,7 @@ class ImagePasteInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -169,14 +162,8 @@ class ImagePasteInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"tomask",
|
||||
title="Mask from Alpha",
|
||||
tags=["image", "mask"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class MaskFromAlphaInvocation(BaseInvocation):
|
||||
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
|
||||
class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Extracts the alpha channel of an image as a mask."""
|
||||
|
||||
image: ImageField = InputField(description="The image to create the mask from")
|
||||
@ -196,6 +183,7 @@ class MaskFromAlphaInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -206,14 +194,8 @@ class MaskFromAlphaInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_mul",
|
||||
title="Multiply Images",
|
||||
tags=["image", "multiply"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageMultiplyInvocation(BaseInvocation):
|
||||
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
|
||||
class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
|
||||
|
||||
image1: ImageField = InputField(description="The first image to multiply")
|
||||
@ -232,6 +214,7 @@ class ImageMultiplyInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -245,14 +228,8 @@ class ImageMultiplyInvocation(BaseInvocation):
|
||||
IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_chan",
|
||||
title="Extract Image Channel",
|
||||
tags=["image", "channel"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageChannelInvocation(BaseInvocation):
|
||||
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
|
||||
class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Gets a channel from an image."""
|
||||
|
||||
image: ImageField = InputField(description="The image to get the channel from")
|
||||
@ -270,6 +247,7 @@ class ImageChannelInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -283,14 +261,8 @@ class ImageChannelInvocation(BaseInvocation):
|
||||
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_conv",
|
||||
title="Convert Image Mode",
|
||||
tags=["image", "convert"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageConvertInvocation(BaseInvocation):
|
||||
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
|
||||
class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Converts an image to a different mode."""
|
||||
|
||||
image: ImageField = InputField(description="The image to convert")
|
||||
@ -308,6 +280,7 @@ class ImageConvertInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -318,14 +291,8 @@ class ImageConvertInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_blur",
|
||||
title="Blur Image",
|
||||
tags=["image", "blur"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageBlurInvocation(BaseInvocation):
|
||||
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
|
||||
class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Blurs an image"""
|
||||
|
||||
image: ImageField = InputField(description="The image to blur")
|
||||
@ -348,6 +315,7 @@ class ImageBlurInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -378,23 +346,14 @@ PIL_RESAMPLING_MAP = {
|
||||
}
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_resize",
|
||||
title="Resize Image",
|
||||
tags=["image", "resize"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageResizeInvocation(BaseInvocation):
|
||||
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
|
||||
class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Resizes an image to specific dimensions"""
|
||||
|
||||
image: ImageField = InputField(description="The image to resize")
|
||||
width: int = InputField(default=512, gt=0, description="The width to resize to (px)")
|
||||
height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
|
||||
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
|
||||
metadata: Optional[CoreMetadata] = InputField(
|
||||
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
@ -413,7 +372,7 @@ class ImageResizeInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata.model_dump() if self.metadata else None,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -424,14 +383,8 @@ class ImageResizeInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_scale",
|
||||
title="Scale Image",
|
||||
tags=["image", "scale"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageScaleInvocation(BaseInvocation):
|
||||
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
|
||||
class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Scales an image by a factor"""
|
||||
|
||||
image: ImageField = InputField(description="The image to scale")
|
||||
@ -461,6 +414,7 @@ class ImageScaleInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -471,14 +425,8 @@ class ImageScaleInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_lerp",
|
||||
title="Lerp Image",
|
||||
tags=["image", "lerp"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageLerpInvocation(BaseInvocation):
|
||||
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
|
||||
class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Linear interpolation of all pixels of an image"""
|
||||
|
||||
image: ImageField = InputField(description="The image to lerp")
|
||||
@ -500,6 +448,7 @@ class ImageLerpInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -510,14 +459,8 @@ class ImageLerpInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_ilerp",
|
||||
title="Inverse Lerp Image",
|
||||
tags=["image", "ilerp"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageInverseLerpInvocation(BaseInvocation):
|
||||
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
|
||||
class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Inverse linear interpolation of all pixels of an image"""
|
||||
|
||||
image: ImageField = InputField(description="The image to lerp")
|
||||
@ -539,6 +482,7 @@ class ImageInverseLerpInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -549,20 +493,11 @@ class ImageInverseLerpInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_nsfw",
|
||||
title="Blur NSFW Image",
|
||||
tags=["image", "nsfw"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageNSFWBlurInvocation(BaseInvocation):
|
||||
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
|
||||
class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Add blur to NSFW-flagged images"""
|
||||
|
||||
image: ImageField = InputField(description="The image to check")
|
||||
metadata: Optional[CoreMetadata] = InputField(
|
||||
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
@ -583,7 +518,7 @@ class ImageNSFWBlurInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata.model_dump() if self.metadata else None,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -607,14 +542,11 @@ class ImageNSFWBlurInvocation(BaseInvocation):
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageWatermarkInvocation(BaseInvocation):
|
||||
class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Add an invisible watermark to an image"""
|
||||
|
||||
image: ImageField = InputField(description="The image to check")
|
||||
text: str = InputField(default="InvokeAI", description="Watermark text")
|
||||
metadata: Optional[CoreMetadata] = InputField(
|
||||
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
@ -626,7 +558,7 @@ class ImageWatermarkInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata.model_dump() if self.metadata else None,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -637,14 +569,8 @@ class ImageWatermarkInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"mask_edge",
|
||||
title="Mask Edge",
|
||||
tags=["image", "mask", "inpaint"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class MaskEdgeInvocation(BaseInvocation):
|
||||
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
|
||||
class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Applies an edge mask to an image"""
|
||||
|
||||
image: ImageField = InputField(description="The image to apply the mask to")
|
||||
@ -678,6 +604,7 @@ class MaskEdgeInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -695,7 +622,7 @@ class MaskEdgeInvocation(BaseInvocation):
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class MaskCombineInvocation(BaseInvocation):
|
||||
class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
|
||||
|
||||
mask1: ImageField = InputField(description="The first mask to combine")
|
||||
@ -714,6 +641,7 @@ class MaskCombineInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -724,14 +652,8 @@ class MaskCombineInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"color_correct",
|
||||
title="Color Correct",
|
||||
tags=["image", "color"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ColorCorrectInvocation(BaseInvocation):
|
||||
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
|
||||
class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""
|
||||
Shifts the colors of a target image to match the reference image, optionally
|
||||
using a mask to only color-correct certain regions of the target image.
|
||||
@ -830,6 +752,7 @@ class ColorCorrectInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -840,14 +763,8 @@ class ColorCorrectInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation(
|
||||
"img_hue_adjust",
|
||||
title="Adjust Image Hue",
|
||||
tags=["image", "hue"],
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageHueAdjustmentInvocation(BaseInvocation):
|
||||
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
|
||||
class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Adjusts the Hue of an image."""
|
||||
|
||||
image: ImageField = InputField(description="The image to adjust")
|
||||
@ -875,6 +792,7 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
session_id=context.graph_execution_state_id,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -950,7 +868,7 @@ CHANNEL_FORMATS = {
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageChannelOffsetInvocation(BaseInvocation):
|
||||
class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Add or subtract a value from a specific color channel of an image."""
|
||||
|
||||
image: ImageField = InputField(description="The image to adjust")
|
||||
@ -984,6 +902,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
session_id=context.graph_execution_state_id,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -1020,7 +939,7 @@ class ImageChannelOffsetInvocation(BaseInvocation):
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ImageChannelMultiplyInvocation(BaseInvocation):
|
||||
class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Scale a specific color channel of an image."""
|
||||
|
||||
image: ImageField = InputField(description="The image to adjust")
|
||||
@ -1060,6 +979,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
|
||||
is_intermediate=self.is_intermediate,
|
||||
session_id=context.graph_execution_state_id,
|
||||
workflow=self.workflow,
|
||||
metadata=self.metadata,
|
||||
)
|
||||
|
||||
return ImageOutput(
|
||||
@ -1079,16 +999,11 @@ class ImageChannelMultiplyInvocation(BaseInvocation):
|
||||
version="1.0.1",
|
||||
use_cache=False,
|
||||
)
|
||||
class SaveImageInvocation(BaseInvocation):
|
||||
class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Saves an image. Unlike an image primitive, this invocation stores a copy of the image."""
|
||||
|
||||
image: ImageField = InputField(description=FieldDescriptions.image)
|
||||
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
|
||||
metadata: Optional[CoreMetadata] = InputField(
|
||||
default=None,
|
||||
description=FieldDescriptions.core_metadata,
|
||||
ui_hidden=True,
|
||||
)
|
||||
board: BoardField = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
image = context.services.images.get_pil_image(self.image.image_name)
|
||||
@ -1101,7 +1016,7 @@ class SaveImageInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata.model_dump() if self.metadata else None,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
|
||||
@ -13,7 +13,7 @@ from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
|
||||
from invokeai.backend.image_util.lama import LaMA
|
||||
from invokeai.backend.image_util.patchmatch import PatchMatch
|
||||
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
|
||||
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
|
||||
|
||||
|
||||
@ -119,7 +119,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
|
||||
|
||||
|
||||
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
|
||||
class InfillColorInvocation(BaseInvocation):
|
||||
class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Infills transparent areas of an image with a solid color"""
|
||||
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
@ -143,6 +143,7 @@ class InfillColorInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -154,7 +155,7 @@ class InfillColorInvocation(BaseInvocation):
|
||||
|
||||
|
||||
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
|
||||
class InfillTileInvocation(BaseInvocation):
|
||||
class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Infills transparent areas of an image with tiles of the image"""
|
||||
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
@ -179,6 +180,7 @@ class InfillTileInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -192,7 +194,7 @@ class InfillTileInvocation(BaseInvocation):
|
||||
@invocation(
|
||||
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
|
||||
)
|
||||
class InfillPatchMatchInvocation(BaseInvocation):
|
||||
class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Infills transparent areas of an image using the PatchMatch algorithm"""
|
||||
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
@ -232,6 +234,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
@ -243,7 +246,7 @@ class InfillPatchMatchInvocation(BaseInvocation):
|
||||
|
||||
|
||||
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
|
||||
class LaMaInfillInvocation(BaseInvocation):
|
||||
class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Infills transparent areas of an image using the LaMa model"""
|
||||
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
@ -260,6 +263,8 @@ class LaMaInfillInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
return ImageOutput(
|
||||
@ -269,8 +274,8 @@ class LaMaInfillInvocation(BaseInvocation):
|
||||
)
|
||||
|
||||
|
||||
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
|
||||
class CV2InfillInvocation(BaseInvocation):
|
||||
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
|
||||
class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Infills transparent areas of an image using OpenCV Inpainting"""
|
||||
|
||||
image: ImageField = InputField(description="The image to infill")
|
||||
@ -287,6 +292,8 @@ class CV2InfillInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
return ImageOutput(
|
||||
|
||||
@ -36,7 +36,7 @@ class CLIPVisionModelField(BaseModel):
|
||||
|
||||
|
||||
class IPAdapterField(BaseModel):
|
||||
image: ImageField = Field(description="The IP-Adapter image prompt.")
|
||||
image: Union[ImageField, List[ImageField]] = Field(description="The IP-Adapter image prompt(s).")
|
||||
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
|
||||
image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
|
||||
weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
|
||||
@ -55,19 +55,19 @@ class IPAdapterOutput(BaseInvocationOutput):
|
||||
ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
|
||||
|
||||
|
||||
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0")
|
||||
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.0")
|
||||
class IPAdapterInvocation(BaseInvocation):
|
||||
"""Collects IP-Adapter info to pass to other nodes."""
|
||||
|
||||
# Inputs
|
||||
image: ImageField = InputField(description="The IP-Adapter image prompt.")
|
||||
image: Union[ImageField, List[ImageField]] = InputField(description="The IP-Adapter image prompt(s).")
|
||||
ip_adapter_model: IPAdapterModelField = InputField(
|
||||
description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
|
||||
)
|
||||
|
||||
# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
|
||||
weight: Union[float, List[float]] = InputField(
|
||||
default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
|
||||
default=1, ge=-1, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
|
||||
)
|
||||
|
||||
begin_step_percent: float = InputField(
|
||||
|
||||
@ -23,7 +23,6 @@ from pydantic import field_validator
|
||||
from torchvision.transforms.functional import resize as tv_resize
|
||||
|
||||
from invokeai.app.invocations.ip_adapter import IPAdapterField
|
||||
from invokeai.app.invocations.metadata import CoreMetadata
|
||||
from invokeai.app.invocations.primitives import (
|
||||
DenoiseMaskField,
|
||||
DenoiseMaskOutput,
|
||||
@ -64,6 +63,8 @@ from .baseinvocation import (
|
||||
InvocationContext,
|
||||
OutputField,
|
||||
UIType,
|
||||
WithMetadata,
|
||||
WithWorkflow,
|
||||
invocation,
|
||||
invocation_output,
|
||||
)
|
||||
@ -214,7 +215,7 @@ def get_scheduler(
|
||||
title="Denoise Latents",
|
||||
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
|
||||
category="latents",
|
||||
version="1.3.0",
|
||||
version="1.4.0",
|
||||
)
|
||||
class DenoiseLatentsInvocation(BaseInvocation):
|
||||
"""Denoises noisy latents to decodable images"""
|
||||
@ -491,16 +492,21 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
context=context,
|
||||
)
|
||||
|
||||
input_image = context.services.images.get_pil_image(single_ip_adapter.image.image_name)
|
||||
# `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
|
||||
single_ipa_images = single_ip_adapter.image
|
||||
if not isinstance(single_ipa_images, list):
|
||||
single_ipa_images = [single_ipa_images]
|
||||
|
||||
single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images]
|
||||
|
||||
# TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
|
||||
# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
|
||||
with image_encoder_model_info as image_encoder_model:
|
||||
# Get image embeddings from CLIP and ImageProjModel.
|
||||
(
|
||||
image_prompt_embeds,
|
||||
uncond_image_prompt_embeds,
|
||||
) = ip_adapter_model.get_image_embeds(input_image, image_encoder_model)
|
||||
image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
|
||||
single_ipa_images, image_encoder_model
|
||||
)
|
||||
|
||||
conditioning_data.ip_adapter_conditioning.append(
|
||||
IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
|
||||
)
|
||||
@ -708,6 +714,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
|
||||
set_seamless(unet_info.context.model, self.unet.seamless_axes),
|
||||
unet_info as unet,
|
||||
# Apply the LoRA after unet has been moved to its target device for faster patching.
|
||||
ModelPatcher.apply_lora_unet(unet, _lora_loader()),
|
||||
):
|
||||
latents = latents.to(device=unet.device, dtype=unet.dtype)
|
||||
if noise is not None:
|
||||
@ -788,7 +796,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
|
||||
category="latents",
|
||||
version="1.0.0",
|
||||
)
|
||||
class LatentsToImageInvocation(BaseInvocation):
|
||||
class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Generates an image from latents."""
|
||||
|
||||
latents: LatentsField = InputField(
|
||||
@ -801,11 +809,6 @@ class LatentsToImageInvocation(BaseInvocation):
|
||||
)
|
||||
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
|
||||
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
|
||||
metadata: Optional[CoreMetadata] = InputField(
|
||||
default=None,
|
||||
description=FieldDescriptions.core_metadata,
|
||||
ui_hidden=True,
|
||||
)
|
||||
|
||||
@torch.no_grad()
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
@ -874,7 +877,7 @@ class LatentsToImageInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata.model_dump() if self.metadata else None,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
from typing import Literal
|
||||
|
||||
import numpy as np
|
||||
from pydantic import field_validator
|
||||
from pydantic import ValidationInfo, field_validator
|
||||
|
||||
from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
|
||||
|
||||
@ -186,12 +186,12 @@ class IntegerMathInvocation(BaseInvocation):
|
||||
b: int = InputField(default=0, description=FieldDescriptions.num_2)
|
||||
|
||||
@field_validator("b")
|
||||
def no_unrepresentable_results(cls, v, values):
|
||||
if values["operation"] == "DIV" and v == 0:
|
||||
def no_unrepresentable_results(cls, v: int, info: ValidationInfo):
|
||||
if info.data["operation"] == "DIV" and v == 0:
|
||||
raise ValueError("Cannot divide by zero")
|
||||
elif values["operation"] == "MOD" and v == 0:
|
||||
elif info.data["operation"] == "MOD" and v == 0:
|
||||
raise ValueError("Cannot divide by zero")
|
||||
elif values["operation"] == "EXP" and v < 0:
|
||||
elif info.data["operation"] == "EXP" and v < 0:
|
||||
raise ValueError("Result of exponentiation is not an integer")
|
||||
return v
|
||||
|
||||
@ -260,12 +260,12 @@ class FloatMathInvocation(BaseInvocation):
|
||||
b: float = InputField(default=0, description=FieldDescriptions.num_2)
|
||||
|
||||
@field_validator("b")
|
||||
def no_unrepresentable_results(cls, v, values):
|
||||
if values["operation"] == "DIV" and v == 0:
|
||||
def no_unrepresentable_results(cls, v: float, info: ValidationInfo):
|
||||
if info.data["operation"] == "DIV" and v == 0:
|
||||
raise ValueError("Cannot divide by zero")
|
||||
elif values["operation"] == "EXP" and values["a"] == 0 and v < 0:
|
||||
elif info.data["operation"] == "EXP" and info.data["a"] == 0 and v < 0:
|
||||
raise ValueError("Cannot raise zero to a negative power")
|
||||
elif values["operation"] == "EXP" and type(values["a"] ** v) is complex:
|
||||
elif info.data["operation"] == "EXP" and type(info.data["a"] ** v) is complex:
|
||||
raise ValueError("Root operation resulted in a complex number")
|
||||
return v
|
||||
|
||||
|
||||
@ -1,13 +1,16 @@
|
||||
from typing import Optional
|
||||
from typing import Any, Literal, Optional, Union
|
||||
|
||||
from pydantic import Field
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
|
||||
from invokeai.app.invocations.baseinvocation import (
|
||||
BaseInvocation,
|
||||
BaseInvocationOutput,
|
||||
FieldDescriptions,
|
||||
InputField,
|
||||
InvocationContext,
|
||||
MetadataField,
|
||||
OutputField,
|
||||
UIType,
|
||||
invocation,
|
||||
invocation_output,
|
||||
)
|
||||
@ -16,116 +19,104 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField
|
||||
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
|
||||
from invokeai.app.invocations.primitives import ImageField
|
||||
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
|
||||
from invokeai.app.util.model_exclude_null import BaseModelExcludeNull
|
||||
|
||||
from ...version import __version__
|
||||
|
||||
|
||||
class LoRAMetadataField(BaseModelExcludeNull):
|
||||
"""LoRA metadata for an image generated in InvokeAI."""
|
||||
|
||||
lora: LoRAModelField = Field(description="The LoRA model")
|
||||
weight: float = Field(description="The weight of the LoRA model")
|
||||
class MetadataItemField(BaseModel):
|
||||
label: str = Field(description=FieldDescriptions.metadata_item_label)
|
||||
value: Any = Field(description=FieldDescriptions.metadata_item_value)
|
||||
|
||||
|
||||
class IPAdapterMetadataField(BaseModelExcludeNull):
|
||||
class LoRAMetadataField(BaseModel):
|
||||
"""LoRA Metadata Field"""
|
||||
|
||||
lora: LoRAModelField = Field(description=FieldDescriptions.lora_model)
|
||||
weight: float = Field(description=FieldDescriptions.lora_weight)
|
||||
|
||||
|
||||
class IPAdapterMetadataField(BaseModel):
|
||||
"""IP Adapter Field, minus the CLIP Vision Encoder model"""
|
||||
|
||||
image: ImageField = Field(description="The IP-Adapter image prompt.")
|
||||
ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
|
||||
weight: float = Field(description="The weight of the IP-Adapter model")
|
||||
begin_step_percent: float = Field(
|
||||
default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
|
||||
ip_adapter_model: IPAdapterModelField = Field(
|
||||
description="The IP-Adapter model.",
|
||||
)
|
||||
end_step_percent: float = Field(
|
||||
default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
|
||||
weight: Union[float, list[float]] = Field(
|
||||
description="The weight given to the IP-Adapter",
|
||||
)
|
||||
begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
|
||||
end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
|
||||
|
||||
|
||||
@invocation_output("metadata_item_output")
|
||||
class MetadataItemOutput(BaseInvocationOutput):
|
||||
"""Metadata Item Output"""
|
||||
|
||||
item: MetadataItemField = OutputField(description="Metadata Item")
|
||||
|
||||
|
||||
@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.0")
|
||||
class MetadataItemInvocation(BaseInvocation):
|
||||
"""Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value."""
|
||||
|
||||
label: str = InputField(description=FieldDescriptions.metadata_item_label)
|
||||
value: Any = InputField(description=FieldDescriptions.metadata_item_value, ui_type=UIType.Any)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> MetadataItemOutput:
|
||||
return MetadataItemOutput(item=MetadataItemField(label=self.label, value=self.value))
|
||||
|
||||
|
||||
@invocation_output("metadata_output")
|
||||
class MetadataOutput(BaseInvocationOutput):
|
||||
metadata: MetadataField = OutputField(description="Metadata Dict")
|
||||
|
||||
|
||||
@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.0")
|
||||
class MetadataInvocation(BaseInvocation):
|
||||
"""Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict."""
|
||||
|
||||
items: Union[list[MetadataItemField], MetadataItemField] = InputField(
|
||||
description=FieldDescriptions.metadata_item_polymorphic
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> MetadataOutput:
|
||||
if isinstance(self.items, MetadataItemField):
|
||||
# single metadata item
|
||||
data = {self.items.label: self.items.value}
|
||||
else:
|
||||
# collection of metadata items
|
||||
data = {item.label: item.value for item in self.items}
|
||||
|
||||
class CoreMetadata(BaseModelExcludeNull):
|
||||
"""Core generation metadata for an image generated in InvokeAI."""
|
||||
|
||||
app_version: str = Field(default=__version__, description="The version of InvokeAI used to generate this image")
|
||||
generation_mode: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The generation mode that output this image",
|
||||
)
|
||||
created_by: Optional[str] = Field(default=None, description="The name of the creator of the image")
|
||||
positive_prompt: Optional[str] = Field(default=None, description="The positive prompt parameter")
|
||||
negative_prompt: Optional[str] = Field(default=None, description="The negative prompt parameter")
|
||||
width: Optional[int] = Field(default=None, description="The width parameter")
|
||||
height: Optional[int] = Field(default=None, description="The height parameter")
|
||||
seed: Optional[int] = Field(default=None, description="The seed used for noise generation")
|
||||
rand_device: Optional[str] = Field(default=None, description="The device used for random number generation")
|
||||
cfg_scale: Optional[float] = Field(default=None, description="The classifier-free guidance scale parameter")
|
||||
steps: Optional[int] = Field(default=None, description="The number of steps used for inference")
|
||||
scheduler: Optional[str] = Field(default=None, description="The scheduler used for inference")
|
||||
clip_skip: Optional[int] = Field(
|
||||
default=None,
|
||||
description="The number of skipped CLIP layers",
|
||||
)
|
||||
model: Optional[MainModelField] = Field(default=None, description="The main model used for inference")
|
||||
controlnets: Optional[list[ControlField]] = Field(default=None, description="The ControlNets used for inference")
|
||||
ipAdapters: Optional[list[IPAdapterMetadataField]] = Field(
|
||||
default=None, description="The IP Adapters used for inference"
|
||||
)
|
||||
t2iAdapters: Optional[list[T2IAdapterField]] = Field(default=None, description="The IP Adapters used for inference")
|
||||
loras: Optional[list[LoRAMetadataField]] = Field(default=None, description="The LoRAs used for inference")
|
||||
vae: Optional[VAEModelField] = Field(
|
||||
default=None,
|
||||
description="The VAE used for decoding, if the main model's default was not used",
|
||||
)
|
||||
|
||||
# Latents-to-Latents
|
||||
strength: Optional[float] = Field(
|
||||
default=None,
|
||||
description="The strength used for latents-to-latents",
|
||||
)
|
||||
init_image: Optional[str] = Field(default=None, description="The name of the initial image")
|
||||
|
||||
# SDXL
|
||||
positive_style_prompt: Optional[str] = Field(default=None, description="The positive style prompt parameter")
|
||||
negative_style_prompt: Optional[str] = Field(default=None, description="The negative style prompt parameter")
|
||||
|
||||
# SDXL Refiner
|
||||
refiner_model: Optional[MainModelField] = Field(default=None, description="The SDXL Refiner model used")
|
||||
refiner_cfg_scale: Optional[float] = Field(
|
||||
default=None,
|
||||
description="The classifier-free guidance scale parameter used for the refiner",
|
||||
)
|
||||
refiner_steps: Optional[int] = Field(default=None, description="The number of steps used for the refiner")
|
||||
refiner_scheduler: Optional[str] = Field(default=None, description="The scheduler used for the refiner")
|
||||
refiner_positive_aesthetic_score: Optional[float] = Field(
|
||||
default=None, description="The aesthetic score used for the refiner"
|
||||
)
|
||||
refiner_negative_aesthetic_score: Optional[float] = Field(
|
||||
default=None, description="The aesthetic score used for the refiner"
|
||||
)
|
||||
refiner_start: Optional[float] = Field(default=None, description="The start value used for refiner denoising")
|
||||
# add app version
|
||||
data.update({"app_version": __version__})
|
||||
return MetadataOutput(metadata=MetadataField.model_validate(data))
|
||||
|
||||
|
||||
class ImageMetadata(BaseModelExcludeNull):
|
||||
"""An image's generation metadata"""
|
||||
@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0")
|
||||
class MergeMetadataInvocation(BaseInvocation):
|
||||
"""Merged a collection of MetadataDict into a single MetadataDict."""
|
||||
|
||||
metadata: Optional[dict] = Field(
|
||||
default=None,
|
||||
description="The image's core metadata, if it was created in the Linear or Canvas UI",
|
||||
)
|
||||
graph: Optional[dict] = Field(default=None, description="The graph that created the image")
|
||||
collection: list[MetadataField] = InputField(description=FieldDescriptions.metadata_collection)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> MetadataOutput:
|
||||
data = {}
|
||||
for item in self.collection:
|
||||
data.update(item.model_dump())
|
||||
|
||||
return MetadataOutput(metadata=MetadataField.model_validate(data))
|
||||
|
||||
|
||||
@invocation_output("metadata_accumulator_output")
|
||||
class MetadataAccumulatorOutput(BaseInvocationOutput):
|
||||
"""The output of the MetadataAccumulator node"""
|
||||
|
||||
metadata: CoreMetadata = OutputField(description="The core metadata for the image")
|
||||
GENERATION_MODES = Literal[
|
||||
"txt2img", "img2img", "inpaint", "outpaint", "sdxl_txt2img", "sdxl_img2img", "sdxl_inpaint", "sdxl_outpaint"
|
||||
]
|
||||
|
||||
|
||||
@invocation(
|
||||
"metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata", version="1.0.0"
|
||||
)
|
||||
class MetadataAccumulatorInvocation(BaseInvocation):
|
||||
"""Outputs a Core Metadata Object"""
|
||||
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0")
|
||||
class CoreMetadataInvocation(BaseInvocation):
|
||||
"""Collects core generation metadata into a MetadataField"""
|
||||
|
||||
generation_mode: Optional[str] = InputField(
|
||||
generation_mode: Optional[GENERATION_MODES] = InputField(
|
||||
default=None,
|
||||
description="The generation mode that output this image",
|
||||
)
|
||||
@ -138,6 +129,8 @@ class MetadataAccumulatorInvocation(BaseInvocation):
|
||||
cfg_scale: Optional[float] = InputField(default=None, description="The classifier-free guidance scale parameter")
|
||||
steps: Optional[int] = InputField(default=None, description="The number of steps used for inference")
|
||||
scheduler: Optional[str] = InputField(default=None, description="The scheduler used for inference")
|
||||
seamless_x: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the X axis")
|
||||
seamless_y: Optional[bool] = InputField(default=None, description="Whether seamless tiling was used on the Y axis")
|
||||
clip_skip: Optional[int] = InputField(
|
||||
default=None,
|
||||
description="The number of skipped CLIP layers",
|
||||
@ -220,7 +213,13 @@ class MetadataAccumulatorInvocation(BaseInvocation):
|
||||
description="The start value used for refiner denoising",
|
||||
)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> MetadataAccumulatorOutput:
|
||||
def invoke(self, context: InvocationContext) -> MetadataOutput:
|
||||
"""Collects and outputs a CoreMetadata object"""
|
||||
|
||||
return MetadataAccumulatorOutput(metadata=CoreMetadata(**self.model_dump()))
|
||||
return MetadataOutput(
|
||||
metadata=MetadataField.model_validate(
|
||||
self.model_dump(exclude_none=True, exclude={"id", "type", "is_intermediate", "use_cache"})
|
||||
)
|
||||
)
|
||||
|
||||
model_config = ConfigDict(extra="allow")
|
||||
|
||||
@ -4,7 +4,7 @@ import inspect
|
||||
import re
|
||||
|
||||
# from contextlib import ExitStack
|
||||
from typing import List, Literal, Optional, Union
|
||||
from typing import List, Literal, Union
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
@ -12,7 +12,6 @@ from diffusers.image_processor import VaeImageProcessor
|
||||
from pydantic import BaseModel, ConfigDict, Field, field_validator
|
||||
from tqdm import tqdm
|
||||
|
||||
from invokeai.app.invocations.metadata import CoreMetadata
|
||||
from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.app.util.step_callback import stable_diffusion_step_callback
|
||||
@ -31,6 +30,8 @@ from .baseinvocation import (
|
||||
OutputField,
|
||||
UIComponent,
|
||||
UIType,
|
||||
WithMetadata,
|
||||
WithWorkflow,
|
||||
invocation,
|
||||
invocation_output,
|
||||
)
|
||||
@ -327,7 +328,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
|
||||
category="image",
|
||||
version="1.0.0",
|
||||
)
|
||||
class ONNXLatentsToImageInvocation(BaseInvocation):
|
||||
class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
|
||||
"""Generates an image from latents."""
|
||||
|
||||
latents: LatentsField = InputField(
|
||||
@ -338,11 +339,6 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
|
||||
description=FieldDescriptions.vae,
|
||||
input=Input.Connection,
|
||||
)
|
||||
metadata: Optional[CoreMetadata] = InputField(
|
||||
default=None,
|
||||
description=FieldDescriptions.core_metadata,
|
||||
ui_hidden=True,
|
||||
)
|
||||
# tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
|
||||
|
||||
def invoke(self, context: InvocationContext) -> ImageOutput:
|
||||
@ -381,7 +377,7 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata.model_dump() if self.metadata else None,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
|
||||
@ -251,7 +251,9 @@ class ImageCollectionOutput(BaseInvocationOutput):
|
||||
|
||||
|
||||
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
|
||||
class ImageInvocation(BaseInvocation):
|
||||
class ImageInvocation(
|
||||
BaseInvocation,
|
||||
):
|
||||
"""An image primitive value"""
|
||||
|
||||
image: ImageField = InputField(description="The image to load")
|
||||
@ -291,7 +293,7 @@ class DenoiseMaskField(BaseModel):
|
||||
"""An inpaint mask field"""
|
||||
|
||||
mask_name: str = Field(description="The name of the mask image")
|
||||
masked_latents_name: Optional[str] = Field(description="The name of the masked image latents")
|
||||
masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
|
||||
|
||||
|
||||
@invocation_output("denoise_mask_output")
|
||||
|
||||
@ -14,7 +14,7 @@ from invokeai.app.invocations.primitives import ImageField, ImageOutput
|
||||
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
|
||||
from invokeai.backend.util.devices import choose_torch_device
|
||||
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
|
||||
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
|
||||
|
||||
# TODO: Populate this from disk?
|
||||
# TODO: Use model manager to load?
|
||||
@ -30,7 +30,7 @@ if choose_torch_device() == torch.device("mps"):
|
||||
|
||||
|
||||
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
|
||||
class ESRGANInvocation(BaseInvocation):
|
||||
class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
|
||||
"""Upscales an image using RealESRGAN."""
|
||||
|
||||
image: ImageField = InputField(description="The input image")
|
||||
@ -123,6 +123,7 @@ class ESRGANInvocation(BaseInvocation):
|
||||
node_id=self.id,
|
||||
session_id=context.graph_execution_state_id,
|
||||
is_intermediate=self.is_intermediate,
|
||||
metadata=self.metadata,
|
||||
workflow=self.workflow,
|
||||
)
|
||||
|
||||
|
||||
@ -45,6 +45,7 @@ InvokeAI:
|
||||
ram: 13.5
|
||||
vram: 0.25
|
||||
lazy_offload: true
|
||||
log_memory_usage: false
|
||||
Device:
|
||||
device: auto
|
||||
precision: auto
|
||||
@ -243,6 +244,7 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
|
||||
outdir : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
|
||||
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', json_schema_extra=Categories.Paths)
|
||||
custom_nodes_dir : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes', json_schema_extra=Categories.Paths)
|
||||
from_file : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only)', json_schema_extra=Categories.Paths)
|
||||
|
||||
# LOGGING
|
||||
@ -260,6 +262,7 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
ram : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
|
||||
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, )
|
||||
log_memory_usage : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
|
||||
|
||||
# DEVICE
|
||||
device : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Generation device", json_schema_extra=Categories.Device)
|
||||
@ -410,6 +413,13 @@ class InvokeAIAppConfig(InvokeAISettings):
|
||||
"""
|
||||
return self._resolve(self.models_dir)
|
||||
|
||||
@property
|
||||
def custom_nodes_path(self) -> Path:
|
||||
"""
|
||||
Path to the custom nodes directory
|
||||
"""
|
||||
return self._resolve(self.custom_nodes_dir)
|
||||
|
||||
# the following methods support legacy calls leftover from the Globals era
|
||||
@property
|
||||
def full_precision(self) -> bool:
|
||||
|
||||
@ -4,6 +4,8 @@ from typing import Optional
|
||||
|
||||
from PIL.Image import Image as PILImageType
|
||||
|
||||
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
|
||||
|
||||
|
||||
class ImageFileStorageBase(ABC):
|
||||
"""Low-level service responsible for storing and retrieving image files."""
|
||||
@ -30,8 +32,8 @@ class ImageFileStorageBase(ABC):
|
||||
self,
|
||||
image: PILImageType,
|
||||
image_name: str,
|
||||
metadata: Optional[dict] = None,
|
||||
workflow: Optional[str] = None,
|
||||
metadata: Optional[MetadataField] = None,
|
||||
workflow: Optional[WorkflowField] = None,
|
||||
thumbnail_size: int = 256,
|
||||
) -> None:
|
||||
"""Saves an image and a 256x256 WEBP thumbnail. Returns a tuple of the image name, thumbnail name, and created timestamp."""
|
||||
|
||||
@ -1,5 +1,4 @@
|
||||
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
|
||||
import json
|
||||
from pathlib import Path
|
||||
from queue import Queue
|
||||
from typing import Dict, Optional, Union
|
||||
@ -8,6 +7,7 @@ from PIL import Image, PngImagePlugin
|
||||
from PIL.Image import Image as PILImageType
|
||||
from send2trash import send2trash
|
||||
|
||||
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
|
||||
from invokeai.app.services.invoker import Invoker
|
||||
from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail
|
||||
|
||||
@ -55,8 +55,8 @@ class DiskImageFileStorage(ImageFileStorageBase):
|
||||
self,
|
||||
image: PILImageType,
|
||||
image_name: str,
|
||||
metadata: Optional[dict] = None,
|
||||
workflow: Optional[str] = None,
|
||||
metadata: Optional[MetadataField] = None,
|
||||
workflow: Optional[WorkflowField] = None,
|
||||
thumbnail_size: int = 256,
|
||||
) -> None:
|
||||
try:
|
||||
@ -65,20 +65,10 @@ class DiskImageFileStorage(ImageFileStorageBase):
|
||||
|
||||
pnginfo = PngImagePlugin.PngInfo()
|
||||
|
||||
if metadata is not None or workflow is not None:
|
||||
if metadata is not None:
|
||||
pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
|
||||
if workflow is not None:
|
||||
pnginfo.add_text("invokeai_workflow", workflow)
|
||||
else:
|
||||
# For uploaded images, we want to retain metadata. PIL strips it on save; manually add it back
|
||||
# TODO: retain non-invokeai metadata on save...
|
||||
original_metadata = image.info.get("invokeai_metadata", None)
|
||||
if original_metadata is not None:
|
||||
pnginfo.add_text("invokeai_metadata", original_metadata)
|
||||
original_workflow = image.info.get("invokeai_workflow", None)
|
||||
if original_workflow is not None:
|
||||
pnginfo.add_text("invokeai_workflow", original_workflow)
|
||||
if metadata is not None:
|
||||
pnginfo.add_text("invokeai_metadata", metadata.model_dump_json())
|
||||
if workflow is not None:
|
||||
pnginfo.add_text("invokeai_workflow", workflow.model_dump_json())
|
||||
|
||||
image.save(
|
||||
image_path,
|
||||
|
||||
@ -2,6 +2,7 @@ from abc import ABC, abstractmethod
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
from invokeai.app.invocations.metadata import MetadataField
|
||||
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
|
||||
|
||||
from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin
|
||||
@ -18,7 +19,7 @@ class ImageRecordStorageBase(ABC):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_metadata(self, image_name: str) -> Optional[dict]:
|
||||
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
|
||||
"""Gets an image's metadata'."""
|
||||
pass
|
||||
|
||||
@ -61,6 +62,11 @@ class ImageRecordStorageBase(ABC):
|
||||
"""Deletes all intermediate image records, returning a list of deleted image names."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_intermediates_count(self) -> int:
|
||||
"""Gets a count of all intermediate images."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def save(
|
||||
self,
|
||||
@ -73,7 +79,7 @@ class ImageRecordStorageBase(ABC):
|
||||
starred: Optional[bool] = False,
|
||||
session_id: Optional[str] = None,
|
||||
node_id: Optional[str] = None,
|
||||
metadata: Optional[dict] = None,
|
||||
metadata: Optional[MetadataField] = None,
|
||||
) -> datetime:
|
||||
"""Saves an image record."""
|
||||
pass
|
||||
|
||||
@ -1,9 +1,9 @@
|
||||
import json
|
||||
import sqlite3
|
||||
import threading
|
||||
from datetime import datetime
|
||||
from typing import Optional, Union, cast
|
||||
|
||||
from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
|
||||
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
|
||||
from invokeai.app.services.shared.sqlite import SqliteDatabase
|
||||
|
||||
@ -141,22 +141,26 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
|
||||
|
||||
return deserialize_image_record(dict(result))
|
||||
|
||||
def get_metadata(self, image_name: str) -> Optional[dict]:
|
||||
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
SELECT images.metadata FROM images
|
||||
SELECT metadata FROM images
|
||||
WHERE image_name = ?;
|
||||
""",
|
||||
(image_name,),
|
||||
)
|
||||
|
||||
result = cast(Optional[sqlite3.Row], self._cursor.fetchone())
|
||||
if not result or not result[0]:
|
||||
return None
|
||||
return json.loads(result[0])
|
||||
|
||||
if not result:
|
||||
raise ImageRecordNotFoundException
|
||||
|
||||
as_dict = dict(result)
|
||||
metadata_raw = cast(Optional[str], as_dict.get("metadata", None))
|
||||
return MetadataFieldValidator.validate_json(metadata_raw) if metadata_raw is not None else None
|
||||
except sqlite3.Error as e:
|
||||
self._conn.rollback()
|
||||
raise ImageRecordNotFoundException from e
|
||||
@ -297,11 +301,8 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
|
||||
images_query += query_conditions + query_pagination + ";"
|
||||
# Add all the parameters
|
||||
images_params = query_params.copy()
|
||||
|
||||
if limit is not None:
|
||||
images_params.append(limit)
|
||||
if offset is not None:
|
||||
images_params.append(offset)
|
||||
# Add the pagination parameters
|
||||
images_params.extend([limit, offset])
|
||||
|
||||
# Build the list of images, deserializing each row
|
||||
self._cursor.execute(images_query, images_params)
|
||||
@ -357,6 +358,24 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
def get_intermediates_count(self) -> int:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
SELECT COUNT(*) FROM images
|
||||
WHERE is_intermediate = TRUE;
|
||||
"""
|
||||
)
|
||||
count = cast(int, self._cursor.fetchone()[0])
|
||||
self._conn.commit()
|
||||
return count
|
||||
except sqlite3.Error as e:
|
||||
self._conn.rollback()
|
||||
raise ImageRecordDeleteException from e
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
def delete_intermediates(self) -> list[str]:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
@ -393,10 +412,10 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
|
||||
starred: Optional[bool] = False,
|
||||
session_id: Optional[str] = None,
|
||||
node_id: Optional[str] = None,
|
||||
metadata: Optional[dict] = None,
|
||||
metadata: Optional[MetadataField] = None,
|
||||
) -> datetime:
|
||||
try:
|
||||
metadata_json = None if metadata is None else json.dumps(metadata)
|
||||
metadata_json = metadata.model_dump_json() if metadata is not None else None
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
|
||||
@ -3,7 +3,7 @@ from typing import Callable, Optional
|
||||
|
||||
from PIL.Image import Image as PILImageType
|
||||
|
||||
from invokeai.app.invocations.metadata import ImageMetadata
|
||||
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
|
||||
from invokeai.app.services.image_records.image_records_common import (
|
||||
ImageCategory,
|
||||
ImageRecord,
|
||||
@ -50,8 +50,8 @@ class ImageServiceABC(ABC):
|
||||
session_id: Optional[str] = None,
|
||||
board_id: Optional[str] = None,
|
||||
is_intermediate: Optional[bool] = False,
|
||||
metadata: Optional[dict] = None,
|
||||
workflow: Optional[str] = None,
|
||||
metadata: Optional[MetadataField] = None,
|
||||
workflow: Optional[WorkflowField] = None,
|
||||
) -> ImageDTO:
|
||||
"""Creates an image, storing the file and its metadata."""
|
||||
pass
|
||||
@ -81,7 +81,7 @@ class ImageServiceABC(ABC):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_metadata(self, image_name: str) -> ImageMetadata:
|
||||
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
|
||||
"""Gets an image's metadata."""
|
||||
pass
|
||||
|
||||
@ -123,6 +123,11 @@ class ImageServiceABC(ABC):
|
||||
"""Deletes all intermediate images."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_intermediates_count(self) -> int:
|
||||
"""Gets the number of intermediate images."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def delete_images_on_board(self, board_id: str):
|
||||
"""Deletes all images on a board."""
|
||||
|
||||
@ -24,8 +24,11 @@ class ImageDTO(ImageRecord, ImageUrlsDTO):
|
||||
default=None, description="The id of the board the image belongs to, if one exists."
|
||||
)
|
||||
"""The id of the board the image belongs to, if one exists."""
|
||||
|
||||
pass
|
||||
workflow_id: Optional[str] = Field(
|
||||
default=None,
|
||||
description="The workflow that generated this image.",
|
||||
)
|
||||
"""The workflow that generated this image."""
|
||||
|
||||
|
||||
def image_record_to_dto(
|
||||
@ -33,6 +36,7 @@ def image_record_to_dto(
|
||||
image_url: str,
|
||||
thumbnail_url: str,
|
||||
board_id: Optional[str],
|
||||
workflow_id: Optional[str],
|
||||
) -> ImageDTO:
|
||||
"""Converts an image record to an image DTO."""
|
||||
return ImageDTO(
|
||||
@ -40,4 +44,5 @@ def image_record_to_dto(
|
||||
image_url=image_url,
|
||||
thumbnail_url=thumbnail_url,
|
||||
board_id=board_id,
|
||||
workflow_id=workflow_id,
|
||||
)
|
||||
|
||||
@ -2,10 +2,9 @@ from typing import Optional
|
||||
|
||||
from PIL.Image import Image as PILImageType
|
||||
|
||||
from invokeai.app.invocations.metadata import ImageMetadata
|
||||
from invokeai.app.invocations.baseinvocation import MetadataField, WorkflowField
|
||||
from invokeai.app.services.invoker import Invoker
|
||||
from invokeai.app.services.shared.pagination import OffsetPaginatedResults
|
||||
from invokeai.app.util.metadata import get_metadata_graph_from_raw_session
|
||||
|
||||
from ..image_files.image_files_common import (
|
||||
ImageFileDeleteException,
|
||||
@ -42,8 +41,8 @@ class ImageService(ImageServiceABC):
|
||||
session_id: Optional[str] = None,
|
||||
board_id: Optional[str] = None,
|
||||
is_intermediate: Optional[bool] = False,
|
||||
metadata: Optional[dict] = None,
|
||||
workflow: Optional[str] = None,
|
||||
metadata: Optional[MetadataField] = None,
|
||||
workflow: Optional[WorkflowField] = None,
|
||||
) -> ImageDTO:
|
||||
if image_origin not in ResourceOrigin:
|
||||
raise InvalidOriginException
|
||||
@ -56,6 +55,12 @@ class ImageService(ImageServiceABC):
|
||||
(width, height) = image.size
|
||||
|
||||
try:
|
||||
if workflow is not None:
|
||||
created_workflow = self.__invoker.services.workflow_records.create(workflow)
|
||||
workflow_id = created_workflow.model_dump()["id"]
|
||||
else:
|
||||
workflow_id = None
|
||||
|
||||
# TODO: Consider using a transaction here to ensure consistency between storage and database
|
||||
self.__invoker.services.image_records.save(
|
||||
# Non-nullable fields
|
||||
@ -73,6 +78,8 @@ class ImageService(ImageServiceABC):
|
||||
)
|
||||
if board_id is not None:
|
||||
self.__invoker.services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
|
||||
if workflow_id is not None:
|
||||
self.__invoker.services.workflow_image_records.create(workflow_id=workflow_id, image_name=image_name)
|
||||
self.__invoker.services.image_files.save(
|
||||
image_name=image_name, image=image, metadata=metadata, workflow=workflow
|
||||
)
|
||||
@ -132,10 +139,11 @@ class ImageService(ImageServiceABC):
|
||||
image_record = self.__invoker.services.image_records.get(image_name)
|
||||
|
||||
image_dto = image_record_to_dto(
|
||||
image_record,
|
||||
self.__invoker.services.urls.get_image_url(image_name),
|
||||
self.__invoker.services.urls.get_image_url(image_name, True),
|
||||
self.__invoker.services.board_image_records.get_board_for_image(image_name),
|
||||
image_record=image_record,
|
||||
image_url=self.__invoker.services.urls.get_image_url(image_name),
|
||||
thumbnail_url=self.__invoker.services.urls.get_image_url(image_name, True),
|
||||
board_id=self.__invoker.services.board_image_records.get_board_for_image(image_name),
|
||||
workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name),
|
||||
)
|
||||
|
||||
return image_dto
|
||||
@ -146,25 +154,22 @@ class ImageService(ImageServiceABC):
|
||||
self.__invoker.services.logger.error("Problem getting image DTO")
|
||||
raise e
|
||||
|
||||
def get_metadata(self, image_name: str) -> ImageMetadata:
|
||||
def get_metadata(self, image_name: str) -> Optional[MetadataField]:
|
||||
try:
|
||||
image_record = self.__invoker.services.image_records.get(image_name)
|
||||
metadata = self.__invoker.services.image_records.get_metadata(image_name)
|
||||
return self.__invoker.services.image_records.get_metadata(image_name)
|
||||
except ImageRecordNotFoundException:
|
||||
self.__invoker.services.logger.error("Image record not found")
|
||||
raise
|
||||
except Exception as e:
|
||||
self.__invoker.services.logger.error("Problem getting image DTO")
|
||||
raise e
|
||||
|
||||
if not image_record.session_id:
|
||||
return ImageMetadata(metadata=metadata)
|
||||
|
||||
session_raw = self.__invoker.services.graph_execution_manager.get_raw(image_record.session_id)
|
||||
graph = None
|
||||
|
||||
if session_raw:
|
||||
try:
|
||||
graph = get_metadata_graph_from_raw_session(session_raw)
|
||||
except Exception as e:
|
||||
self.__invoker.services.logger.warn(f"Failed to parse session graph: {e}")
|
||||
graph = None
|
||||
|
||||
return ImageMetadata(graph=graph, metadata=metadata)
|
||||
def get_workflow(self, image_name: str) -> Optional[WorkflowField]:
|
||||
try:
|
||||
workflow_id = self.__invoker.services.workflow_image_records.get_workflow_for_image(image_name)
|
||||
if workflow_id is None:
|
||||
return None
|
||||
return self.__invoker.services.workflow_records.get(workflow_id)
|
||||
except ImageRecordNotFoundException:
|
||||
self.__invoker.services.logger.error("Image record not found")
|
||||
raise
|
||||
@ -215,10 +220,11 @@ class ImageService(ImageServiceABC):
|
||||
image_dtos = list(
|
||||
map(
|
||||
lambda r: image_record_to_dto(
|
||||
r,
|
||||
self.__invoker.services.urls.get_image_url(r.image_name),
|
||||
self.__invoker.services.urls.get_image_url(r.image_name, True),
|
||||
self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
|
||||
image_record=r,
|
||||
image_url=self.__invoker.services.urls.get_image_url(r.image_name),
|
||||
thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
|
||||
board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
|
||||
workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
|
||||
),
|
||||
results.items,
|
||||
)
|
||||
@ -284,3 +290,10 @@ class ImageService(ImageServiceABC):
|
||||
except Exception as e:
|
||||
self.__invoker.services.logger.error("Problem deleting image records and files")
|
||||
raise e
|
||||
|
||||
def get_intermediates_count(self) -> int:
|
||||
try:
|
||||
return self.__invoker.services.image_records.get_intermediates_count()
|
||||
except Exception as e:
|
||||
self.__invoker.services.logger.error("Problem getting intermediates count")
|
||||
raise e
|
||||
|
||||
@ -27,6 +27,8 @@ if TYPE_CHECKING:
|
||||
from .session_queue.session_queue_base import SessionQueueBase
|
||||
from .shared.graph import GraphExecutionState, LibraryGraph
|
||||
from .urls.urls_base import UrlServiceBase
|
||||
from .workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
|
||||
from .workflow_records.workflow_records_base import WorkflowRecordsStorageBase
|
||||
|
||||
|
||||
class InvocationServices:
|
||||
@ -55,6 +57,8 @@ class InvocationServices:
|
||||
invocation_cache: "InvocationCacheBase"
|
||||
names: "NameServiceBase"
|
||||
urls: "UrlServiceBase"
|
||||
workflow_image_records: "WorkflowImageRecordsStorageBase"
|
||||
workflow_records: "WorkflowRecordsStorageBase"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@ -80,6 +84,8 @@ class InvocationServices:
|
||||
invocation_cache: "InvocationCacheBase",
|
||||
names: "NameServiceBase",
|
||||
urls: "UrlServiceBase",
|
||||
workflow_image_records: "WorkflowImageRecordsStorageBase",
|
||||
workflow_records: "WorkflowRecordsStorageBase",
|
||||
):
|
||||
self.board_images = board_images
|
||||
self.board_image_records = board_image_records
|
||||
@ -103,3 +109,5 @@ class InvocationServices:
|
||||
self.invocation_cache = invocation_cache
|
||||
self.names = names
|
||||
self.urls = urls
|
||||
self.workflow_image_records = workflow_image_records
|
||||
self.workflow_records = workflow_records
|
||||
|
||||
@ -18,7 +18,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
|
||||
_cursor: sqlite3.Cursor
|
||||
_id_field: str
|
||||
_lock: threading.RLock
|
||||
_adapter: Optional[TypeAdapter[T]]
|
||||
_validator: Optional[TypeAdapter[T]]
|
||||
|
||||
def __init__(self, db: SqliteDatabase, table_name: str, id_field: str = "id"):
|
||||
super().__init__()
|
||||
@ -28,7 +28,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
|
||||
self._table_name = table_name
|
||||
self._id_field = id_field # TODO: validate that T has this field
|
||||
self._cursor = self._conn.cursor()
|
||||
self._adapter: Optional[TypeAdapter[T]] = None
|
||||
self._validator: Optional[TypeAdapter[T]] = None
|
||||
|
||||
self._create_table()
|
||||
|
||||
@ -47,14 +47,14 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
|
||||
self._lock.release()
|
||||
|
||||
def _parse_item(self, item: str) -> T:
|
||||
if self._adapter is None:
|
||||
if self._validator is None:
|
||||
"""
|
||||
We don't get access to `__orig_class__` in `__init__()`, and we need this before start(), so
|
||||
we can create it when it is first needed instead.
|
||||
__orig_class__ is technically an implementation detail of the typing module, not a supported API
|
||||
"""
|
||||
self._adapter = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined]
|
||||
return self._adapter.validate_json(item)
|
||||
self._validator = TypeAdapter(get_args(self.__orig_class__)[0]) # type: ignore [attr-defined]
|
||||
return self._validator.validate_json(item)
|
||||
|
||||
def set(self, item: T):
|
||||
try:
|
||||
|
||||
@ -0,0 +1 @@
|
||||
from .model_manager_default import ModelManagerService # noqa F401
|
||||
@ -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."""
|
||||
|
||||
@ -147,20 +147,20 @@ DEFAULT_QUEUE_ID = "default"
|
||||
|
||||
QUEUE_ITEM_STATUS = Literal["pending", "in_progress", "completed", "failed", "canceled"]
|
||||
|
||||
adapter_NodeFieldValue = TypeAdapter(list[NodeFieldValue])
|
||||
NodeFieldValueValidator = TypeAdapter(list[NodeFieldValue])
|
||||
|
||||
|
||||
def get_field_values(queue_item_dict: dict) -> Optional[list[NodeFieldValue]]:
|
||||
field_values_raw = queue_item_dict.get("field_values", None)
|
||||
return adapter_NodeFieldValue.validate_json(field_values_raw) if field_values_raw is not None else None
|
||||
return NodeFieldValueValidator.validate_json(field_values_raw) if field_values_raw is not None else None
|
||||
|
||||
|
||||
adapter_GraphExecutionState = TypeAdapter(GraphExecutionState)
|
||||
GraphExecutionStateValidator = TypeAdapter(GraphExecutionState)
|
||||
|
||||
|
||||
def get_session(queue_item_dict: dict) -> GraphExecutionState:
|
||||
session_raw = queue_item_dict.get("session", "{}")
|
||||
session = adapter_GraphExecutionState.validate_json(session_raw, strict=False)
|
||||
session = GraphExecutionStateValidator.validate_json(session_raw, strict=False)
|
||||
return session
|
||||
|
||||
|
||||
@ -276,14 +276,6 @@ class EnqueueBatchResult(BaseModel):
|
||||
priority: int = Field(description="The priority of the enqueued batch")
|
||||
|
||||
|
||||
class EnqueueGraphResult(BaseModel):
|
||||
enqueued: int = Field(description="The total number of queue items enqueued")
|
||||
requested: int = Field(description="The total number of queue items requested to be enqueued")
|
||||
batch: Batch = Field(description="The batch that was enqueued")
|
||||
priority: int = Field(description="The priority of the enqueued batch")
|
||||
queue_item: SessionQueueItemDTO = Field(description="The queue item that was enqueued")
|
||||
|
||||
|
||||
class ClearResult(BaseModel):
|
||||
"""Result of clearing the session queue"""
|
||||
|
||||
|
||||
@ -17,7 +17,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
|
||||
CancelByQueueIDResult,
|
||||
ClearResult,
|
||||
EnqueueBatchResult,
|
||||
EnqueueGraphResult,
|
||||
IsEmptyResult,
|
||||
IsFullResult,
|
||||
PruneResult,
|
||||
@ -28,7 +27,6 @@ from invokeai.app.services.session_queue.session_queue_common import (
|
||||
calc_session_count,
|
||||
prepare_values_to_insert,
|
||||
)
|
||||
from invokeai.app.services.shared.graph import Graph
|
||||
from invokeai.app.services.shared.pagination import CursorPaginatedResults
|
||||
from invokeai.app.services.shared.sqlite import SqliteDatabase
|
||||
|
||||
@ -255,32 +253,6 @@ class SqliteSessionQueue(SessionQueueBase):
|
||||
)
|
||||
return cast(Union[int, None], self.__cursor.fetchone()[0]) or 0
|
||||
|
||||
def enqueue_graph(self, queue_id: str, graph: Graph, prepend: bool) -> EnqueueGraphResult:
|
||||
enqueue_result = self.enqueue_batch(queue_id=queue_id, batch=Batch(graph=graph), prepend=prepend)
|
||||
try:
|
||||
self.__lock.acquire()
|
||||
self.__cursor.execute(
|
||||
"""--sql
|
||||
SELECT *
|
||||
FROM session_queue
|
||||
WHERE queue_id = ?
|
||||
AND batch_id = ?
|
||||
""",
|
||||
(queue_id, enqueue_result.batch.batch_id),
|
||||
)
|
||||
result = cast(Union[sqlite3.Row, None], self.__cursor.fetchone())
|
||||
except Exception:
|
||||
self.__conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
self.__lock.release()
|
||||
if result is None:
|
||||
raise SessionQueueItemNotFoundError(f"No queue item with batch id {enqueue_result.batch.batch_id}")
|
||||
return EnqueueGraphResult(
|
||||
**enqueue_result.model_dump(),
|
||||
queue_item=SessionQueueItemDTO.queue_item_dto_from_dict(dict(result)),
|
||||
)
|
||||
|
||||
def enqueue_batch(self, queue_id: str, batch: Batch, prepend: bool) -> EnqueueBatchResult:
|
||||
try:
|
||||
self.__lock.acquire()
|
||||
|
||||
@ -193,7 +193,7 @@ class GraphInvocation(BaseInvocation):
|
||||
"""Execute a graph"""
|
||||
|
||||
# TODO: figure out how to create a default here
|
||||
graph: "Graph" = Field(description="The graph to run", default=None)
|
||||
graph: "Graph" = InputField(description="The graph to run", default=None)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> GraphInvocationOutput:
|
||||
"""Invoke with provided services and return outputs."""
|
||||
@ -439,6 +439,14 @@ class Graph(BaseModel):
|
||||
except Exception as e:
|
||||
raise UnknownGraphValidationError(f"Problem validating graph {e}") from e
|
||||
|
||||
def _is_destination_field_Any(self, edge: Edge) -> bool:
|
||||
"""Checks if the destination field for an edge is of type typing.Any"""
|
||||
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == Any
|
||||
|
||||
def _is_destination_field_list_of_Any(self, edge: Edge) -> bool:
|
||||
"""Checks if the destination field for an edge is of type typing.Any"""
|
||||
return get_input_field(self.get_node(edge.destination.node_id), edge.destination.field) == list[Any]
|
||||
|
||||
def _validate_edge(self, edge: Edge):
|
||||
"""Validates that a new edge doesn't create a cycle in the graph"""
|
||||
|
||||
@ -491,8 +499,19 @@ class Graph(BaseModel):
|
||||
f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
||||
)
|
||||
|
||||
# Validate if collector output type matches input type (if this edge results in both being set)
|
||||
if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
|
||||
# Validate that we are not connecting collector to iterator (currently unsupported)
|
||||
if isinstance(from_node, CollectInvocation) and isinstance(to_node, IterateInvocation):
|
||||
raise InvalidEdgeError(
|
||||
f"Cannot connect collector to iterator: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
||||
)
|
||||
|
||||
# Validate if collector output type matches input type (if this edge results in both being set) - skip if the destination field is not Any or list[Any]
|
||||
if (
|
||||
isinstance(from_node, CollectInvocation)
|
||||
and edge.source.field == "collection"
|
||||
and not self._is_destination_field_list_of_Any(edge)
|
||||
and not self._is_destination_field_Any(edge)
|
||||
):
|
||||
if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
|
||||
raise InvalidEdgeError(
|
||||
f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
|
||||
@ -725,16 +744,15 @@ class Graph(BaseModel):
|
||||
# Get the input root type
|
||||
input_root_type = next(t[0] for t in type_degrees if t[1] == 0) # type: ignore
|
||||
|
||||
# Verify that all outputs are lists
|
||||
# if not all((get_origin(f) == list for f in output_fields)):
|
||||
# return False
|
||||
|
||||
# Verify that all outputs are lists
|
||||
if not all(is_list_or_contains_list(f) for f in output_fields):
|
||||
return False
|
||||
|
||||
# Verify that all outputs match the input type (are a base class or the same class)
|
||||
if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)):
|
||||
if not all(
|
||||
is_union_subtype(input_root_type, get_args(f)[0]) or issubclass(input_root_type, get_args(f)[0])
|
||||
for f in output_fields
|
||||
):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
@ -0,0 +1,23 @@
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Optional
|
||||
|
||||
|
||||
class WorkflowImageRecordsStorageBase(ABC):
|
||||
"""Abstract base class for the one-to-many workflow-image relationship record storage."""
|
||||
|
||||
@abstractmethod
|
||||
def create(
|
||||
self,
|
||||
workflow_id: str,
|
||||
image_name: str,
|
||||
) -> None:
|
||||
"""Creates a workflow-image record."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_workflow_for_image(
|
||||
self,
|
||||
image_name: str,
|
||||
) -> Optional[str]:
|
||||
"""Gets an image's workflow id, if it has one."""
|
||||
pass
|
||||
@ -0,0 +1,122 @@
|
||||
import sqlite3
|
||||
import threading
|
||||
from typing import Optional, cast
|
||||
|
||||
from invokeai.app.services.shared.sqlite import SqliteDatabase
|
||||
from invokeai.app.services.workflow_image_records.workflow_image_records_base import WorkflowImageRecordsStorageBase
|
||||
|
||||
|
||||
class SqliteWorkflowImageRecordsStorage(WorkflowImageRecordsStorageBase):
|
||||
"""SQLite implementation of WorkflowImageRecordsStorageBase."""
|
||||
|
||||
_conn: sqlite3.Connection
|
||||
_cursor: sqlite3.Cursor
|
||||
_lock: threading.RLock
|
||||
|
||||
def __init__(self, db: SqliteDatabase) -> None:
|
||||
super().__init__()
|
||||
self._lock = db.lock
|
||||
self._conn = db.conn
|
||||
self._cursor = self._conn.cursor()
|
||||
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._create_tables()
|
||||
self._conn.commit()
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
def _create_tables(self) -> None:
|
||||
# Create the `workflow_images` junction table.
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
CREATE TABLE IF NOT EXISTS workflow_images (
|
||||
workflow_id TEXT NOT NULL,
|
||||
image_name TEXT NOT NULL,
|
||||
created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
|
||||
-- updated via trigger
|
||||
updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
|
||||
-- Soft delete, currently unused
|
||||
deleted_at DATETIME,
|
||||
-- enforce one-to-many relationship between workflows and images using PK
|
||||
-- (we can extend this to many-to-many later)
|
||||
PRIMARY KEY (image_name),
|
||||
FOREIGN KEY (workflow_id) REFERENCES workflows (workflow_id) ON DELETE CASCADE,
|
||||
FOREIGN KEY (image_name) REFERENCES images (image_name) ON DELETE CASCADE
|
||||
);
|
||||
"""
|
||||
)
|
||||
|
||||
# Add index for workflow id
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id ON workflow_images (workflow_id);
|
||||
"""
|
||||
)
|
||||
|
||||
# Add index for workflow id, sorted by created_at
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
CREATE INDEX IF NOT EXISTS idx_workflow_images_workflow_id_created_at ON workflow_images (workflow_id, created_at);
|
||||
"""
|
||||
)
|
||||
|
||||
# Add trigger for `updated_at`.
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
CREATE TRIGGER IF NOT EXISTS tg_workflow_images_updated_at
|
||||
AFTER UPDATE
|
||||
ON workflow_images FOR EACH ROW
|
||||
BEGIN
|
||||
UPDATE workflow_images SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
|
||||
WHERE workflow_id = old.workflow_id AND image_name = old.image_name;
|
||||
END;
|
||||
"""
|
||||
)
|
||||
|
||||
def create(
|
||||
self,
|
||||
workflow_id: str,
|
||||
image_name: str,
|
||||
) -> None:
|
||||
"""Creates a workflow-image record."""
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
INSERT INTO workflow_images (workflow_id, image_name)
|
||||
VALUES (?, ?);
|
||||
""",
|
||||
(workflow_id, image_name),
|
||||
)
|
||||
self._conn.commit()
|
||||
except sqlite3.Error as e:
|
||||
self._conn.rollback()
|
||||
raise e
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
def get_workflow_for_image(
|
||||
self,
|
||||
image_name: str,
|
||||
) -> Optional[str]:
|
||||
"""Gets an image's workflow id, if it has one."""
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
SELECT workflow_id
|
||||
FROM workflow_images
|
||||
WHERE image_name = ?;
|
||||
""",
|
||||
(image_name,),
|
||||
)
|
||||
result = self._cursor.fetchone()
|
||||
if result is None:
|
||||
return None
|
||||
return cast(str, result[0])
|
||||
except sqlite3.Error as e:
|
||||
self._conn.rollback()
|
||||
raise e
|
||||
finally:
|
||||
self._lock.release()
|
||||
0
invokeai/app/services/workflow_records/__init__.py
Normal file
0
invokeai/app/services/workflow_records/__init__.py
Normal file
@ -0,0 +1,17 @@
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
from invokeai.app.invocations.baseinvocation import WorkflowField
|
||||
|
||||
|
||||
class WorkflowRecordsStorageBase(ABC):
|
||||
"""Base class for workflow storage services."""
|
||||
|
||||
@abstractmethod
|
||||
def get(self, workflow_id: str) -> WorkflowField:
|
||||
"""Get workflow by id."""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def create(self, workflow: WorkflowField) -> WorkflowField:
|
||||
"""Creates a workflow."""
|
||||
pass
|
||||
@ -0,0 +1,2 @@
|
||||
class WorkflowNotFoundError(Exception):
|
||||
"""Raised when a workflow is not found"""
|
||||
@ -0,0 +1,102 @@
|
||||
import sqlite3
|
||||
import threading
|
||||
|
||||
from invokeai.app.invocations.baseinvocation import WorkflowField, WorkflowFieldValidator
|
||||
from invokeai.app.services.invoker import Invoker
|
||||
from invokeai.app.services.shared.sqlite import SqliteDatabase
|
||||
from invokeai.app.services.workflow_records.workflow_records_base import WorkflowRecordsStorageBase
|
||||
from invokeai.app.services.workflow_records.workflow_records_common import WorkflowNotFoundError
|
||||
from invokeai.app.util.misc import uuid_string
|
||||
|
||||
|
||||
class SqliteWorkflowRecordsStorage(WorkflowRecordsStorageBase):
|
||||
_invoker: Invoker
|
||||
_conn: sqlite3.Connection
|
||||
_cursor: sqlite3.Cursor
|
||||
_lock: threading.RLock
|
||||
|
||||
def __init__(self, db: SqliteDatabase) -> None:
|
||||
super().__init__()
|
||||
self._lock = db.lock
|
||||
self._conn = db.conn
|
||||
self._cursor = self._conn.cursor()
|
||||
self._create_tables()
|
||||
|
||||
def start(self, invoker: Invoker) -> None:
|
||||
self._invoker = invoker
|
||||
|
||||
def get(self, workflow_id: str) -> WorkflowField:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
SELECT workflow
|
||||
FROM workflows
|
||||
WHERE workflow_id = ?;
|
||||
""",
|
||||
(workflow_id,),
|
||||
)
|
||||
row = self._cursor.fetchone()
|
||||
if row is None:
|
||||
raise WorkflowNotFoundError(f"Workflow with id {workflow_id} not found")
|
||||
return WorkflowFieldValidator.validate_json(row[0])
|
||||
except Exception:
|
||||
self._conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
def create(self, workflow: WorkflowField) -> WorkflowField:
|
||||
try:
|
||||
# workflows do not have ids until they are saved
|
||||
workflow_id = uuid_string()
|
||||
workflow.root["id"] = workflow_id
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
INSERT INTO workflows(workflow)
|
||||
VALUES (?);
|
||||
""",
|
||||
(workflow.model_dump_json(),),
|
||||
)
|
||||
self._conn.commit()
|
||||
except Exception:
|
||||
self._conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
self._lock.release()
|
||||
return self.get(workflow_id)
|
||||
|
||||
def _create_tables(self) -> None:
|
||||
try:
|
||||
self._lock.acquire()
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
CREATE TABLE IF NOT EXISTS workflows (
|
||||
workflow TEXT NOT NULL,
|
||||
workflow_id TEXT GENERATED ALWAYS AS (json_extract(workflow, '$.id')) VIRTUAL NOT NULL UNIQUE, -- gets implicit index
|
||||
created_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')),
|
||||
updated_at DATETIME NOT NULL DEFAULT(STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')) -- updated via trigger
|
||||
);
|
||||
"""
|
||||
)
|
||||
|
||||
self._cursor.execute(
|
||||
"""--sql
|
||||
CREATE TRIGGER IF NOT EXISTS tg_workflows_updated_at
|
||||
AFTER UPDATE
|
||||
ON workflows FOR EACH ROW
|
||||
BEGIN
|
||||
UPDATE workflows
|
||||
SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
|
||||
WHERE workflow_id = old.workflow_id;
|
||||
END;
|
||||
"""
|
||||
)
|
||||
|
||||
self._conn.commit()
|
||||
except Exception:
|
||||
self._conn.rollback()
|
||||
raise
|
||||
finally:
|
||||
self._lock.release()
|
||||
@ -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)
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user