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.github/workflows/style-checks.yml vendored
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@ -6,7 +6,7 @@ on:
branches: main
jobs:
black:
ruff:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3

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Makefile Normal file
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@ -0,0 +1,21 @@
# simple Makefile with scripts that are otherwise hard to remember
# to use, run from the repo root `make <command>`
# Runs ruff, fixing any safely-fixable errors and formatting
ruff:
ruff check . --fix
ruff format .
# Runs ruff, fixing all errors it can fix and formatting
ruff-unsafe:
ruff check . --fix --unsafe-fixes
ruff format .
# Runs mypy, using the config in pyproject.toml
mypy:
mypy scripts/invokeai-web.py
# Runs mypy, ignoring the config in pyproject.toml but still ignoring missing (untyped) imports
# (many files are ignored by the config, so this is useful for checking all files)
mypy-all:
mypy scripts/invokeai-web.py --config-file= --ignore-missing-imports

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README.md
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@ -161,7 +161,7 @@ the command `npm install -g yarn` if needed)
_For Windows/Linux with an NVIDIA GPU:_
```terminal
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
```
_For Linux with an AMD GPU:_
@ -395,7 +395,7 @@ Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
### Troubleshooting
Please check out our **[Q&A](https://invoke-ai.github.io/InvokeAI/help/TROUBLESHOOT/#faq)** to get solutions for common installation
Please check out our **[Troubleshooting Guide](https://invoke-ai.github.io/InvokeAI/installation/010_INSTALL_AUTOMATED/#troubleshooting)** to get solutions for common installation
problems and other issues. For more help, please join our [Discord][discord link]
## Contributing

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docs/contributing/INVOCATIONS.md
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@ -1,6 +1,6 @@
# Invocations
# Nodes
Features in InvokeAI are added in the form of modular node-like systems called
Features in InvokeAI are added in the form of modular nodes systems called
**Invocations**.
An Invocation is simply a single operation that takes in some inputs and gives
@ -9,13 +9,34 @@ complex functionality.
## Invocations Directory
InvokeAI Invocations can be found in the `invokeai/app/invocations` directory.
InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
You can add your new functionality to one of the existing Invocations in this
directory or create a new file in this directory as per your needs.
New nodes should be added to a subfolder in `nodes` direction found at the root level of the InvokeAI installation location. Nodes added to this folder will be able to be used upon application startup.
Example `nodes` subfolder 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
```
Each node folder must have an `__init__.py` file that imports its nodes. Only nodes imported in the `__init__.py` file are loaded.
See the README in the nodes folder for more examples:
```py
from .cool_node import CoolInvocation
```
**Note:** _All Invocations must be inside this directory for InvokeAI to
recognize them as valid Invocations._
## Creating A New Invocation
@ -44,7 +65,7 @@ The first set of things we need to do when creating a new Invocation are -
So let us do that.
```python
from .baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
@invocation('resize')
class ResizeInvocation(BaseInvocation):
@ -78,8 +99,8 @@ create your own custom field types later in this guide. For now, let's go ahead
and use it.
```python
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
from invokeai.app.invocations.primitives import ImageField
@invocation('resize')
class ResizeInvocation(BaseInvocation):
@ -103,8 +124,8 @@ image: ImageField = InputField(description="The input image")
Great. Now let us create our other inputs for `width` and `height`
```python
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation
from invokeai.app.invocations.primitives import ImageField
@invocation('resize')
class ResizeInvocation(BaseInvocation):
@ -139,8 +160,8 @@ that are provided by it by InvokeAI.
Let us create this function first.
```python
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
from invokeai.app.invocations.primitives import ImageField
@invocation('resize')
class ResizeInvocation(BaseInvocation):
@ -168,9 +189,9 @@ all the necessary info related to image outputs. So let us use that.
We will cover how to create your own output types later in this guide.
```python
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
from .image import ImageOutput
from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.image import ImageOutput
@invocation('resize')
class ResizeInvocation(BaseInvocation):
@ -195,9 +216,9 @@ Perfect. Now that we have our Invocation setup, let us do what we want to do.
So let's do that.
```python
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
from .image import ImageOutput
from invokeai.app.invocations.baseinvocation import BaseInvocation, InputField, invocation, InvocationContext
from invokeai.app.invocations.primitives import ImageField
from invokeai.app.invocations.image import ImageOutput, ResourceOrigin, ImageCategory
@invocation("resize")
class ResizeInvocation(BaseInvocation):

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@ -0,0 +1,53 @@
---
title: LoRAs & LCM-LoRAs
---
# :material-library-shelves: LoRAs & LCM-LoRAs
With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model.
## LoRAs
Low-Rank Adaptation (LoRA) files are models that customize the output of Stable Diffusion
image generation. Larger than embeddings, but much smaller than full
models, they augment SD with improved understanding of subjects and
artistic styles.
Unlike TI files, LoRAs do not introduce novel vocabulary into the
model's known tokens. Instead, LoRAs augment the model's weights that
are applied to generate imagery. LoRAs may be supplied with a
"trigger" word that they have been explicitly trained on, or may
simply apply their effect without being triggered.
LoRAs are typically stored in .safetensors files, which are the most
secure way to store and transmit these types of weights. You may
install any number of `.safetensors` LoRA files simply by copying them
into the `autoimport/lora` directory of the corresponding InvokeAI models
directory (usually `invokeai` in your home directory).
To use these when generating, open the LoRA menu item in the options
panel, select the LoRAs you want to apply and ensure that they have
the appropriate weight recommended by the model provider. Typically,
most LoRAs perform best at a weight of .75-1.
## LCM-LoRAs
Latent Consistency Models (LCMs) allowed a reduced number of steps to be used to generate images with Stable Diffusion. These are created by distilling base models, creating models that only require a small number of steps to generate images. However, LCMs require that any fine-tune of a base model be distilled to be used as an LCM.
LCM-LoRAs are models that provide the benefit of LCMs but are able to be used as LoRAs and applied to any fine tune of a base model. LCM-LoRAs are created by training a small number of adapters, rather than distilling the entire fine-tuned base model. The resulting LoRA can be used the same way as a standard LoRA, but with a greatly reduced step count. This enables SDXL images to be generated up to 10x faster than without the use of LCM-LoRAs.
**Using LCM-LoRAs**
LCM-LoRAs are natively supported in InvokeAI throughout the application. To get started, install any diffusers format LCM-LoRAs using the model manager and select it in the LoRA field.
There are a number parameter differences when using LCM-LoRAs and standard generation:
- When using LCM-LoRAs, the LoRA strength should be lower than if using a standard LoRA, with 0.35 recommended as a starting point.
- The LCM scheduler should be used for generation
- CFG-Scale should be reduced to ~1
- Steps should be reduced in the range of 4-8
Standard LoRAs can also be used alongside LCM-LoRAs, but will also require a lower strength, with 0.45 being recommended as a starting point.
More information can be found here: https://huggingface.co/blog/lcm_lora#fast-inference-with-sdxl-lcm-loras

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@ -1,12 +1,3 @@
---
title: Textual Inversion Embeddings and LoRAs
---
# :material-library-shelves: Textual Inversions and LoRAs
With the advances in research, many new capabilities are available to customize the knowledge and understanding of novel concepts not originally contained in the base model.
## Using Textual Inversion Files
Textual inversion (TI) files are small models that customize the output of
@ -61,29 +52,4 @@ files it finds there for compatible models. At startup you will see a message si
>> Current embedding manager terms: <HOI4-Leader>, <princess-knight>
```
To use these when generating, simply type the `<` key in your prompt to open the Textual Inversion WebUI and
select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.
## Using LoRAs
LoRA files are models that customize the output of Stable Diffusion
image generation. Larger than embeddings, but much smaller than full
models, they augment SD with improved understanding of subjects and
artistic styles.
Unlike TI files, LoRAs do not introduce novel vocabulary into the
model's known tokens. Instead, LoRAs augment the model's weights that
are applied to generate imagery. LoRAs may be supplied with a
"trigger" word that they have been explicitly trained on, or may
simply apply their effect without being triggered.
LoRAs are typically stored in .safetensors files, which are the most
secure way to store and transmit these types of weights. You may
install any number of `.safetensors` LoRA files simply by copying them
into the `autoimport/lora` directory of the corresponding InvokeAI models
directory (usually `invokeai` in your home directory).
To use these when generating, open the LoRA menu item in the options
panel, select the LoRAs you want to apply and ensure that they have
the appropriate weight recommended by the model provider. Typically,
most LoRAs perform best at a weight of .75-1.
select the embedding you'd like to use. This UI has type-ahead support, so you can easily find supported embeddings.

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@ -20,7 +20,7 @@ a single convenient digital artist-optimized user interface.
### * [Prompt Engineering](PROMPTS.md)
Get the images you want with the InvokeAI prompt engineering language.
### * The [LoRA, LyCORIS and Textual Inversion Models](CONCEPTS.md)
### * The [LoRA, LyCORIS, LCM-LoRA Models](CONCEPTS.md)
Add custom subjects and styles using a variety of fine-tuned models.
### * [ControlNet](CONTROLNET.md)
@ -40,7 +40,7 @@ guide also covers optimizing models to load quickly.
Teach an old model new tricks. Merge 2-3 models together to create a
new model that combines characteristics of the originals.
### * [Textual Inversion](TRAINING.md)
### * [Textual Inversion](TEXTUAL_INVERSIONS.md)
Personalize models by adding your own style or subjects.
## Other Features

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@ -0,0 +1,43 @@
# FAQs
**Where do I get started? How can I install Invoke?**
- You can download the latest installers [here](https://github.com/invoke-ai/InvokeAI/releases) - Note that any releases marked as *pre-release* are in a beta state. You may experience some issues, but we appreciate your help testing those! For stable/reliable installations, please install the **[Latest Release](https://github.com/invoke-ai/InvokeAI/releases/latest)**
**How can I download models? Can I use models I already have downloaded?**
- Models can be downloaded through the model manager, or through option [4] in the invoke.bat/invoke.sh launcher script. To download a model through the Model Manager, use the HuggingFace Repo ID by pressing the “Copy” button next to the repository name. Alternatively, to download a model from CivitAi, use the download link in the Model Manager.
- Models that are already downloaded can be used by creating a symlink to the model location in the `autoimport` folder or by using the Model Manger’s “Scan for Models” function.
**My images are taking a long time to generate. How can I speed up generation?**
- A common solution is to reduce the size of your RAM & VRAM cache to 0.25. This ensures your system has enough memory to generate images.
- Additionally, check the [hardware requirements](https://invoke-ai.github.io/InvokeAI/#hardware-requirements) to ensure that your system is capable of generating images.
- Lastly, double check your generations are happening on your GPU (if you have one). InvokeAI will log what is being used for generation upon startup.
**I’ve installed Python on Windows but the installer says it can’t find it?**
- Then ensure that you checked **'Add python.exe to PATH'** when installing Python. This can be found at the bottom of the Python Installer window. If you already have Python installed, this can be done with the modify / repair feature of the installer.
**I’ve installed everything successfully but I still get an error about Triton when starting Invoke?**
- This can be safely ignored. InvokeAI doesn't use Triton, but if you are on Linux and wish to dismiss the error, you can install Triton.
**I updated to 3.4.0 and now xFormers can’t load C++/CUDA?**
- An issue occurred with your PyTorch update. Follow these steps to fix :
1. Launch your invoke.bat / invoke.sh and select the option to open the developer console
2. Run:`pip install ".[xformers]" --upgrade --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121`
- If you run into an error with `typing_extensions`, re-open the developer console and run: `pip install -U typing-extensions`
**It says my pip is out of date - is that why my install isn't working?**
- An out of date won't cause an installation to fail. The cause of the error can likely be found above the message that says pip is out of date.
- If you saw that warning but the install went well, don't worry about it (but you can update pip afterwards if you'd like).
**How can I generate the exact same that I found on the internet?**
Most example images with prompts that you'll find on the internet have been generated using different software, so you can't expect to get identical results. In order to reproduce an image, you need to replicate the exact settings and processing steps, including (but not limited to) the model, the positive and negative prompts, the seed, the sampler, the exact image size, any upscaling steps, etc.
**Where can I get more help?**
- Create an issue on [GitHub](https://github.com/invoke-ai/InvokeAI/issues) or post in the [#help channel](https://discord.com/channels/1020123559063990373/1149510134058471514) of the InvokeAI Discord

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@ -101,16 +101,13 @@ Mac and Linux machines, and runs on GPU cards with as little as 4 GB of RAM.
<div align="center"><img src="assets/invoke-web-server-1.png" width=640></div>
!!! Note
This project is rapidly evolving. Please use the [Issues tab](https://github.com/invoke-ai/InvokeAI/issues) to report bugs and make feature requests. Be sure to use the provided templates as it will help aid response time.
## :octicons-link-24: Quick Links
<div class="button-container">
<a href="installation/INSTALLATION"> <button class="button">Installation</button> </a>
<a href="features/"> <button class="button">Features</button> </a>
<a href="help/gettingStartedWithAI/"> <button class="button">Getting Started</button> </a>
<a href="help/FAQ/"> <button class="button">FAQ</button> </a>
<a href="contributing/CONTRIBUTING/"> <button class="button">Contributing</button> </a>
<a href="https://github.com/invoke-ai/InvokeAI/"> <button class="button">Code and Downloads</button> </a>
<a href="https://github.com/invoke-ai/InvokeAI/issues"> <button class="button">Bug Reports </button> </a>

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@ -471,7 +471,7 @@ Then type the following commands:
=== "NVIDIA System"
```bash
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu118
pip install torch torchvision --force-reinstall --extra-index-url https://download.pytorch.org/whl/cu121
pip install xformers
```

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@ -148,7 +148,7 @@ manager, please follow these steps:
=== "CUDA (NVidia)"
```bash
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
pip install "InvokeAI[xformers]" --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
```
=== "ROCm (AMD)"
@ -327,7 +327,7 @@ installation protocol (important!)
=== "CUDA (NVidia)"
```bash
pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
pip install -e .[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
```
=== "ROCm (AMD)"
@ -375,7 +375,7 @@ you can do so using this unsupported recipe:
mkdir ~/invokeai
conda create -n invokeai python=3.10
conda activate invokeai
pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu118
pip install InvokeAI[xformers] --use-pep517 --extra-index-url https://download.pytorch.org/whl/cu121
invokeai-configure --root ~/invokeai
invokeai --root ~/invokeai --web
```

2
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@ -85,7 +85,7 @@ You can find which version you should download from [this link](https://docs.nvi
When installing torch and torchvision manually with `pip`, remember to provide
the argument `--extra-index-url
https://download.pytorch.org/whl/cu118` as described in the [Manual
https://download.pytorch.org/whl/cu121` as described in the [Manual
Installation Guide](020_INSTALL_MANUAL.md).
## :simple-amd: ROCm

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docs/installation/070_INSTALL_XFORMERS.md
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@ -28,7 +28,7 @@ command line, then just be sure to activate it's virtual environment.
Then run the following three commands:
```sh
pip install xformers~=0.0.19
pip install xformers~=0.0.22
pip install triton # WON'T WORK ON WINDOWS
python -m xformers.info output
```
@ -42,7 +42,7 @@ If all goes well, you'll see a report like the
following:
```sh
xFormers 0.0.20
xFormers 0.0.22
memory_efficient_attention.cutlassF: available
memory_efficient_attention.cutlassB: available
memory_efficient_attention.flshattF: available
@ -59,14 +59,14 @@ swiglu.gemm_fused_operand_sum: available
swiglu.fused.p.cpp: available
is_triton_available: True
is_functorch_available: False
pytorch.version: 2.0.1+cu118
pytorch.version: 2.1.0+cu121
pytorch.cuda: available
gpu.compute_capability: 8.9
gpu.name: NVIDIA GeForce RTX 4070
build.info: available
build.cuda_version: 1108
build.python_version: 3.10.11
build.torch_version: 2.0.1+cu118
build.torch_version: 2.1.0+cu121
build.env.TORCH_CUDA_ARCH_LIST: 5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6
build.env.XFORMERS_BUILD_TYPE: Release
build.env.XFORMERS_ENABLE_DEBUG_ASSERTIONS: None
@ -92,33 +92,22 @@ installed from source. These instructions were written for a system
running Ubuntu 22.04, but other Linux distributions should be able to
adapt this recipe.
#### 1. Install CUDA Toolkit 11.8
#### 1. Install CUDA Toolkit 12.1
You will need the CUDA developer's toolkit in order to compile and
install xFormers. **Do not try to install Ubuntu's nvidia-cuda-toolkit
package.** It is out of date and will cause conflicts among the NVIDIA
driver and binaries. Instead install the CUDA Toolkit package provided
by NVIDIA itself. Go to [CUDA Toolkit 11.8
Downloads](https://developer.nvidia.com/cuda-11-8-0-download-archive)
by NVIDIA itself. Go to [CUDA Toolkit 12.1
Downloads](https://developer.nvidia.com/cuda-12-1-0-download-archive)
and use the target selection wizard to choose your platform and Linux
distribution. Select an installer type of "runfile (local)" at the
last step.
This will provide you with a recipe for downloading and running a
install shell script that will install the toolkit and drivers. For
example, the install script recipe for Ubuntu 22.04 running on a
x86_64 system is:
install shell script that will install the toolkit and drivers.
```
wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
sudo sh cuda_11.8.0_520.61.05_linux.run
```
Rather than cut-and-paste this example, We recommend that you walk
through the toolkit wizard in order to get the most up to date
installer for your system.
#### 2. Confirm/Install pyTorch 2.01 with CUDA 11.8 support
#### 2. Confirm/Install pyTorch 2.1.0 with CUDA 12.1 support
If you are using InvokeAI 3.0.2 or higher, these will already be
installed. If not, you can check whether you have the needed libraries
@ -133,7 +122,7 @@ Then run the command:
python -c 'exec("import torch\nprint(torch.__version__)")'
```
If it prints __1.13.1+cu118__ you're good. If not, you can install the
If it prints __2.1.0+cu121__ you're good. If not, you can install the
most up to date libraries with this command:
```sh

86
docs/nodes/communityNodes.md
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@ -8,7 +8,7 @@ To use a node, add the node to the `nodes` folder found in your InvokeAI install
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.
If you'd prefer, you can also just download the whole node folder 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.
@ -26,12 +26,15 @@ To use a community workflow, download the the `.json` node graph file and load i
+ [Image Picker](#image-picker)
+ [Load Video Frame](#load-video-frame)
+ [Make 3D](#make-3d)
+ [Match Histogram](#match-histogram)
+ [Oobabooga](#oobabooga)
+ [Prompt Tools](#prompt-tools)
+ [Remote Image](#remote-image)
+ [Retroize](#retroize)
+ [Size Stepper Nodes](#size-stepper-nodes)
+ [Text font to Image](#text-font-to-image)
+ [Thresholding](#thresholding)
+ [Unsharp Mask](#unsharp-mask)
+ [XY Image to Grid and Images to Grids nodes](#xy-image-to-grid-and-images-to-grids-nodes)
- [Example Node Template](#example-node-template)
- [Disclaimer](#disclaimer)
@ -206,6 +209,23 @@ This includes 15 Nodes:
<img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-1.png" width="300" />
<img src="https://gitlab.com/srcrr/shift3d/-/raw/main/example-2.png" width="300" />
--------------------------------
### Match Histogram
**Description:** An InvokeAI node to match a histogram from one image to another. This is a bit like the `color correct` node in the main InvokeAI but this works in the YCbCr colourspace and can handle images of different sizes. Also does not require a mask input.
- Option to only transfer luminance channel.
- Option to save output as grayscale
A good use case for this node is to normalize the colors of an image that has been through the tiled scaling workflow of my XYGrid Nodes.
See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
**Node Link:** https://github.com/skunkworxdark/match_histogram
**Output Examples**
<img src="https://github.com/skunkworxdark/match_histogram/assets/21961335/ed12f329-a0ef-444a-9bae-129ed60d6097" width="300" />
--------------------------------
### Oobabooga
@ -235,22 +255,41 @@ This node works best with SDXL models, especially as the style can be described
--------------------------------
### Prompt Tools
**Description:** A set of InvokeAI nodes that add general prompt manipulation tools. These were written to accompany the PromptsFromFile node and other prompt generation nodes.
**Description:** A set of InvokeAI nodes that add general prompt (string) manipulation tools. Designed to accompany the `Prompts From File` node and other prompt generation nodes.
1. `Prompt To File` - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
2. `PTFields Collect` - Converts image generation fields into a Json format string that can be passed to Prompt to file.
3. `PTFields Expand` - Takes Json string and converts it to individual generation parameters. This can be fed from the Prompts to file node.
4. `Prompt Strength` - Formats prompt with strength like the weighted format of compel
5. `Prompt Strength Combine` - Combines weighted prompts for .and()/.blend()
6. `CSV To Index String` - Gets a string from a CSV by index. Includes a Random index option
The following Nodes are now included in v3.2 of Invoke and are nolonger in this set of tools.<br>
- `Prompt Join` -> `String Join`
- `Prompt Join Three` -> `String Join Three`
- `Prompt Replace` -> `String Replace`
- `Prompt Split Neg` -> `String Split Neg`
1. PromptJoin - Joins to prompts into one.
2. PromptReplace - performs a search and replace on a prompt. With the option of using regex.
3. PromptSplitNeg - splits a prompt into positive and negative using the old V2 method of [] for negative.
4. PromptToFile - saves a prompt or collection of prompts to a file. one per line. There is an append/overwrite option.
5. PTFieldsCollect - Converts image generation fields into a Json format string that can be passed to Prompt to file.
6. PTFieldsExpand - Takes Json string and converts it to individual generation parameters This can be fed from the Prompts to file node.
7. PromptJoinThree - Joins 3 prompt together.
8. PromptStrength - This take a string and float and outputs another string in the format of (string)strength like the weighted format of compel.
9. PromptStrengthCombine - This takes a collection of prompt strength strings and outputs a string in the .and() or .blend() format that can be fed into a proper prompt node.
See full docs here: https://github.com/skunkworxdark/Prompt-tools-nodes/edit/main/README.md
**Node Link:** https://github.com/skunkworxdark/Prompt-tools-nodes
**Workflow Examples**
<img src="https://github.com/skunkworxdark/prompt-tools/blob/main/images/CSVToIndexStringNode.png" width="300" />
--------------------------------
### Remote Image
**Description:** This is a pack of nodes to interoperate with other services, be they public websites or bespoke local servers. The pack consists of these nodes:
- *Load Remote Image* - Lets you load remote images such as a realtime webcam image, an image of the day, or dynamically created images.
- *Post Image to Remote Server* - Lets you upload an image to a remote server using an HTTP POST request, eg for storage, display or further processing.
**Node Link:** https://github.com/fieldOfView/InvokeAI-remote_image
--------------------------------
### Retroize
@ -316,18 +355,37 @@ Highlights/Midtones/Shadows (with LUT blur enabled):
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0a440e43-697f-4d17-82ee-f287467df0a5" width="300" />
<img src="https://github.com/invoke-ai/InvokeAI/assets/34005131/0701fd0f-2ca7-4fe2-8613-2b52547bafce" width="300" />
--------------------------------
### Unsharp Mask
**Description:** Applies an unsharp mask filter to an image, preserving its alpha channel in the process.
**Node Link:** https://github.com/JPPhoto/unsharp-mask-node
--------------------------------
### XY Image to Grid and Images to Grids nodes
**Description:** Image to grid nodes and supporting tools.
**Description:** These nodes add the following to InvokeAI:
- Generate grids of images from multiple input images
- Create XY grid images with labels from parameters
- Split images into overlapping tiles for processing (for super-resolution workflows)
- Recombine image tiles into a single output image blending the seams
1. "Images To Grids" node - Takes a collection of images and creates a grid(s) of images. If there are more images than the size of a single grid then multiple grids will be created until it runs out of images.
2. "XYImage To Grid" node - Converts a collection of XYImages into a labeled Grid of images. The XYImages collection has to be built using the supporting nodes. See example node setups for more details.
The nodes include:
1. `Images To Grids` - Combine multiple images into a grid of images
2. `XYImage To Grid` - Take X & Y params and creates a labeled image grid.
3. `XYImage Tiles` - Super-resolution (embiggen) style tiled resizing
4. `Image Tot XYImages` - Takes an image and cuts it up into a number of columns and rows.
5. Multiple supporting nodes - Helper nodes for data wrangling and building `XYImage` collections
See full docs here: https://github.com/skunkworxdark/XYGrid_nodes/edit/main/README.md
**Node Link:** https://github.com/skunkworxdark/XYGrid_nodes
**Output Examples**
<img src="https://github.com/skunkworxdark/XYGrid_nodes/blob/main/images/collage.png" width="300" />
--------------------------------
### Example Node Template

12
docs/nodes/exampleWorkflows.md
View File

@ -7,12 +7,12 @@ To use them, right click on your desired workflow, follow the link to GitHub and
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/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)
* [SDXL Text to Image](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_Text_to_Image.json)
* [SDXL Text to Image with Refiner](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/SDXL_w_Refiner_Text_to_Image.json)
* [Multi ControlNet (Canny & Depth)](https://github.com/invoke-ai/InvokeAI/blob/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)
* [Prompt From File](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Prompt_from_File.json)
* [Face Detailer with IP-Adapter & ControlNet](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/Face_Detailer_with_IP-Adapter_and_Canny.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)
* [QR Code Monster](https://github.com/invoke-ai/InvokeAI/blob/main/docs/workflows/QR_Code_Monster.json)

6
installer/lib/installer.py
View File

@ -244,7 +244,7 @@ class InvokeAiInstance:
"numpy~=1.24.0", # choose versions that won't be uninstalled during phase 2
"urllib3~=1.26.0",
"requests~=2.28.0",
"torch~=2.1.0",
"torch==2.1.0",
"torchmetrics==0.11.4",
"torchvision>=0.14.1",
"--force-reinstall",
@ -460,10 +460,10 @@ def get_torch_source() -> (Union[str, None], str):
url = "https://download.pytorch.org/whl/cpu"
if device == "cuda":
url = "https://download.pytorch.org/whl/cu118"
url = "https://download.pytorch.org/whl/cu121"
optional_modules = "[xformers,onnx-cuda]"
if device == "cuda_and_dml":
url = "https://download.pytorch.org/whl/cu118"
url = "https://download.pytorch.org/whl/cu121"
optional_modules = "[xformers,onnx-directml]"
# in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13

39
invokeai/app/api_app.py
View File

@ -1,14 +1,17 @@
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
# 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.
import sys
from invokeai.version.invokeai_version import __version__
from .services.config import InvokeAIAppConfig
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
if app_config.version:
print(f"InvokeAI version {__version__}")
sys.exit(0)
if True: # hack to make flake8 happy with imports coming after setting up the config
import asyncio
@ -16,6 +19,7 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
import socket
from inspect import signature
from pathlib import Path
from typing import Any
import uvicorn
from fastapi import FastAPI
@ -23,7 +27,7 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
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.responses import FileResponse, HTMLResponse
from fastapi.staticfiles import StaticFiles
from fastapi_events.handlers.local import local_handler
from fastapi_events.middleware import EventHandlerASGIMiddleware
@ -34,7 +38,6 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
# noinspection PyUnresolvedReferences
import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
import invokeai.frontend.web as web_dir
from invokeai.version.invokeai_version import __version__
from ..backend.util.logging import InvokeAILogger
from .api.dependencies import ApiDependencies
@ -51,7 +54,12 @@ if True: # hack to make flake8 happy with imports coming after setting up the c
workflows,
)
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation, UIConfigBase, _InputField, _OutputField
from .invocations.baseinvocation import (
BaseInvocation,
InputFieldJSONSchemaExtra,
OutputFieldJSONSchemaExtra,
UIConfigBase,
)
if is_mps_available():
import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
@ -147,7 +155,11 @@ def custom_openapi() -> dict[str, Any]:
# Add Node Editor UI helper schemas
ui_config_schemas = models_json_schema(
[(UIConfigBase, "serialization"), (_InputField, "serialization"), (_OutputField, "serialization")],
[
(UIConfigBase, "serialization"),
(InputFieldJSONSchemaExtra, "serialization"),
(OutputFieldJSONSchemaExtra, "serialization"),
],
ref_template="#/components/schemas/{model}",
)
for schema_key, ui_config_schema in ui_config_schemas[1]["$defs"].items():
@ -155,7 +167,7 @@ def custom_openapi() -> dict[str, Any]:
# Add a reference to the output type to additionalProperties of the invoker schema
for invoker in all_invocations:
invoker_name = invoker.__name__
invoker_name = invoker.__name__ # type: ignore [attr-defined] # this is a valid attribute
output_type = signature(obj=invoker.invoke).return_annotation
output_type_title = output_type_titles[output_type.__name__]
invoker_schema = openapi_schema["components"]["schemas"][f"{invoker_name}"]
@ -273,7 +285,4 @@ def invoke_api() -> None:
if __name__ == "__main__":
if app_config.version:
print(f"InvokeAI version {__version__}")
else:
invoke_api()
invoke_api()

2
invokeai/app/invocations/__init__.py
View File

@ -5,7 +5,7 @@ from pathlib import Path
from invokeai.app.services.config.config_default import InvokeAIAppConfig
custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.absolute())
custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.resolve())
custom_nodes_path.mkdir(parents=True, exist_ok=True)
custom_nodes_init_path = str(custom_nodes_path / "__init__.py")

516
invokeai/app/invocations/baseinvocation.py
View File

@ -1,4 +1,4 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI team
from __future__ import annotations
@ -8,7 +8,7 @@ from abc import ABC, abstractmethod
from enum import Enum
from inspect import signature
from types import UnionType
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union
from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
import semver
from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
@ -17,11 +17,17 @@ from pydantic_core import PydanticUndefined
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.util.metaenum import MetaEnum
from invokeai.app.util.misc import uuid_string
from invokeai.backend.util.logging import InvokeAILogger
if TYPE_CHECKING:
from ..services.invocation_services import InvocationServices
logger = InvokeAILogger.get_logger()
CUSTOM_NODE_PACK_SUFFIX = "__invokeai-custom-node"
class InvalidVersionError(ValueError):
pass
@ -31,7 +37,7 @@ class InvalidFieldError(TypeError):
pass
class Input(str, Enum):
class Input(str, Enum, metaclass=MetaEnum):
"""
The type of input a field accepts.
- `Input.Direct`: The field must have its value provided directly, when the invocation and field \
@ -45,86 +51,124 @@ class Input(str, Enum):
Any = "any"
class UIType(str, Enum):
class FieldKind(str, Enum, metaclass=MetaEnum):
"""
Type hints for the UI.
If a field should be provided a data type that does not exactly match the python type of the field, \
use this to provide the type that should be used instead. See the node development docs for detail \
on adding a new field type, which involves client-side changes.
The kind of field.
- `Input`: An input field on a node.
- `Output`: An output field on a node.
- `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
"metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
allowing "metadata" for that field.
- `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
but which are used to store information about the node. For example, the `id` and `type` fields are node
attributes.
The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
startup, and when generating the OpenAPI schema for the workflow editor.
"""
# region Primitives
Boolean = "boolean"
Color = "ColorField"
Conditioning = "ConditioningField"
Control = "ControlField"
Float = "float"
Image = "ImageField"
Integer = "integer"
Latents = "LatentsField"
String = "string"
# endregion
Input = "input"
Output = "output"
Internal = "internal"
NodeAttribute = "node_attribute"
# region Collection Primitives
BooleanCollection = "BooleanCollection"
ColorCollection = "ColorCollection"
ConditioningCollection = "ConditioningCollection"
ControlCollection = "ControlCollection"
FloatCollection = "FloatCollection"
ImageCollection = "ImageCollection"
IntegerCollection = "IntegerCollection"
LatentsCollection = "LatentsCollection"
StringCollection = "StringCollection"
# endregion
# region Polymorphic Primitives
BooleanPolymorphic = "BooleanPolymorphic"
ColorPolymorphic = "ColorPolymorphic"
ConditioningPolymorphic = "ConditioningPolymorphic"
ControlPolymorphic = "ControlPolymorphic"
FloatPolymorphic = "FloatPolymorphic"
ImagePolymorphic = "ImagePolymorphic"
IntegerPolymorphic = "IntegerPolymorphic"
LatentsPolymorphic = "LatentsPolymorphic"
StringPolymorphic = "StringPolymorphic"
# endregion
class UIType(str, Enum, metaclass=MetaEnum):
"""
Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
# region Models
MainModel = "MainModelField"
- Model Fields
The most common node-author-facing use will be for model fields. Internally, there is no difference
between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
the field is an SDXL main model field.
- Any Field
We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
- Scheduler Field
Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
- Internal Fields
Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
should not be used by node authors.
- DEPRECATED Fields
These types are deprecated and should not be used by node authors. A warning will be logged if one is
used, and the type will be ignored. They are included here for backwards compatibility.
"""
# region Model Field Types
SDXLMainModel = "SDXLMainModelField"
SDXLRefinerModel = "SDXLRefinerModelField"
ONNXModel = "ONNXModelField"
VaeModel = "VaeModelField"
VaeModel = "VAEModelField"
LoRAModel = "LoRAModelField"
ControlNetModel = "ControlNetModelField"
IPAdapterModel = "IPAdapterModelField"
UNet = "UNetField"
Vae = "VaeField"
CLIP = "ClipField"
# endregion
# region Iterate/Collect
Collection = "Collection"
CollectionItem = "CollectionItem"
# region Misc Field Types
Scheduler = "SchedulerField"
Any = "AnyField"
# endregion
# region Misc
Enum = "enum"
Scheduler = "Scheduler"
WorkflowField = "WorkflowField"
IsIntermediate = "IsIntermediate"
BoardField = "BoardField"
Any = "Any"
MetadataItem = "MetadataItem"
MetadataItemCollection = "MetadataItemCollection"
MetadataItemPolymorphic = "MetadataItemPolymorphic"
MetadataDict = "MetadataDict"
# region Internal Field Types
_Collection = "CollectionField"
_CollectionItem = "CollectionItemField"
# endregion
# region DEPRECATED
Boolean = "DEPRECATED_Boolean"
Color = "DEPRECATED_Color"
Conditioning = "DEPRECATED_Conditioning"
Control = "DEPRECATED_Control"
Float = "DEPRECATED_Float"
Image = "DEPRECATED_Image"
Integer = "DEPRECATED_Integer"
Latents = "DEPRECATED_Latents"
String = "DEPRECATED_String"
BooleanCollection = "DEPRECATED_BooleanCollection"
ColorCollection = "DEPRECATED_ColorCollection"
ConditioningCollection = "DEPRECATED_ConditioningCollection"
ControlCollection = "DEPRECATED_ControlCollection"
FloatCollection = "DEPRECATED_FloatCollection"
ImageCollection = "DEPRECATED_ImageCollection"
IntegerCollection = "DEPRECATED_IntegerCollection"
LatentsCollection = "DEPRECATED_LatentsCollection"
StringCollection = "DEPRECATED_StringCollection"
BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
StringPolymorphic = "DEPRECATED_StringPolymorphic"
MainModel = "DEPRECATED_MainModel"
UNet = "DEPRECATED_UNet"
Vae = "DEPRECATED_Vae"
CLIP = "DEPRECATED_CLIP"
Collection = "DEPRECATED_Collection"
CollectionItem = "DEPRECATED_CollectionItem"
Enum = "DEPRECATED_Enum"
WorkflowField = "DEPRECATED_WorkflowField"
IsIntermediate = "DEPRECATED_IsIntermediate"
BoardField = "DEPRECATED_BoardField"
MetadataItem = "DEPRECATED_MetadataItem"
MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
MetadataDict = "DEPRECATED_MetadataDict"
# endregion
class UIComponent(str, Enum):
class UIComponent(str, Enum, metaclass=MetaEnum):
"""
The type of UI component to use for a field, used to override the default components, which are \
The type of UI component to use for a field, used to override the default components, which are
inferred from the field type.
"""
@ -133,21 +177,22 @@ class UIComponent(str, Enum):
Slider = "slider"
class _InputField(BaseModel):
class InputFieldJSONSchemaExtra(BaseModel):
"""
*DO NOT USE*
This helper class is used to tell the client about our custom field attributes via OpenAPI
schema generation, and Typescript type generation from that schema. It serves no functional
purpose in the backend.
Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
and by the workflow editor during schema parsing and UI rendering.
"""
input: Input
ui_hidden: bool
ui_type: Optional[UIType]
ui_component: Optional[UIComponent]
ui_order: Optional[int]
ui_choice_labels: Optional[dict[str, str]]
item_default: Optional[Any]
orig_required: bool
field_kind: FieldKind
default: Optional[Any] = None
orig_default: Optional[Any] = None
ui_hidden: bool = False
ui_type: Optional[UIType] = None
ui_component: Optional[UIComponent] = None
ui_order: Optional[int] = None
ui_choice_labels: Optional[dict[str, str]] = None
model_config = ConfigDict(
validate_assignment=True,
@ -155,14 +200,13 @@ class _InputField(BaseModel):
)
class _OutputField(BaseModel):
class OutputFieldJSONSchemaExtra(BaseModel):
"""
*DO NOT USE*
This helper class is used to tell the client about our custom field attributes via OpenAPI
schema generation, and Typescript type generation from that schema. It serves no functional
purpose in the backend.
Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
during schema parsing and UI rendering.
"""
field_kind: FieldKind
ui_hidden: bool
ui_type: Optional[UIType]
ui_order: Optional[int]
@ -173,13 +217,9 @@ class _OutputField(BaseModel):
)
def get_type(klass: BaseModel) -> str:
"""Helper function to get an invocation or invocation output's type. This is the default value of the `type` field."""
return klass.model_fields["type"].default
def InputField(
# copied from pydantic's Field
# TODO: Can we support default_factory?
default: Any = _Unset,
default_factory: Callable[[], Any] | None = _Unset,
title: str | None = _Unset,
@ -203,12 +243,11 @@ def InputField(
ui_hidden: bool = False,
ui_order: Optional[int] = None,
ui_choice_labels: Optional[dict[str, str]] = None,
item_default: Optional[Any] = None,
) -> Any:
"""
Creates an input field for an invocation.
This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
that adds a few extra parameters to support graph execution and the node editor UI.
:param Input input: [Input.Any] The kind of input this field requires. \
@ -228,28 +267,58 @@ def InputField(
For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
For this case, you could provide `UIComponent.Textarea`.
: param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
: param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
: param bool item_default: [None] Specifies the default item value, if this is a collection input. \
Ignored for non-collection fields.
:param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
"""
json_schema_extra_: dict[str, Any] = {
"input": input,
"ui_type": ui_type,
"ui_component": ui_component,
"ui_hidden": ui_hidden,
"ui_order": ui_order,
"item_default": item_default,
"ui_choice_labels": ui_choice_labels,
"_field_kind": "input",
}
json_schema_extra_ = InputFieldJSONSchemaExtra(
input=input,
ui_type=ui_type,
ui_component=ui_component,
ui_hidden=ui_hidden,
ui_order=ui_order,
ui_choice_labels=ui_choice_labels,
field_kind=FieldKind.Input,
orig_required=True,
)
"""
There is a conflict between the typing of invocation definitions and the typing of an invocation's
`invoke()` function.
On instantiation of a node, the invocation definition is used to create the python class. At this time,
any number of fields may be optional, because they may be provided by connections.
On calling of `invoke()`, however, those fields may be required.
For example, consider an ResizeImageInvocation with an `image: ImageField` field.
`image` is required during the call to `invoke()`, but when the python class is instantiated,
the field may not be present. This is fine, because that image field will be provided by a
connection from an ancestor node, which outputs an image.
This means we want to type the `image` field as optional for the node class definition, but required
for the `invoke()` function.
If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
`invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
any static type analysis tools will complain.
To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
but secretly make them optional in `InputField()`. We also store the original required bool and/or default
value. When we call `invoke()`, we use this stored information to do an additional check on the class.
"""
if default_factory is not _Unset and default_factory is not None:
default = default_factory()
logger.warn('"default_factory" is not supported, calling it now to set "default"')
# These are the args we may wish pass to the pydantic `Field()` function
field_args = {
"default": default,
"default_factory": default_factory,
"title": title,
"description": description,
"pattern": pattern,
@ -266,70 +335,34 @@ def InputField(
"max_length": max_length,
}
"""
Invocation definitions have their fields typed correctly for their `invoke()` functions.
This typing is often more specific than the actual invocation definition requires, because
fields may have values provided only by connections.
For example, consider an ResizeImageInvocation with an `image: ImageField` field.
`image` is required during the call to `invoke()`, but when the python class is instantiated,
the field may not be present. This is fine, because that image field will be provided by a
an ancestor node that outputs the image.
So we'd like to type that `image` field as `Optional[ImageField]`. If we do that, however, then
we need to handle a lot of extra logic in the `invoke()` function to check if the field has a
value or not. This is very tedious.
Ideally, the invocation definition would be able to specify that the field is required during
invocation, but optional during instantiation. So the field would be typed as `image: ImageField`,
but when calling the `invoke()` function, we raise an error if the field is not present.
To do this, we need to do a bit of fanagling to make the pydantic field optional, and then do
extra validation when calling `invoke()`.
There is some additional logic here to cleaning create the pydantic field via the wrapper.
"""
# Filter out field args not provided
# We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
if (default is not PydanticUndefined) and (default_factory is not PydanticUndefined):
raise ValueError("Cannot specify both default and default_factory")
# Because we are manually making fields optional, we need to store the original required bool for reference later
json_schema_extra_.orig_required = default is PydanticUndefined
# because we are manually making fields optional, we need to store the original required bool for reference later
if default is PydanticUndefined and default_factory is PydanticUndefined:
json_schema_extra_.update({"orig_required": True})
else:
json_schema_extra_.update({"orig_required": False})
# make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
if (input is Input.Any or input is Input.Connection) and default_factory is PydanticUndefined:
# Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
if input is Input.Any or input is Input.Connection:
default_ = None if default is PydanticUndefined else default
provided_args.update({"default": default_})
if default is not PydanticUndefined:
# before invoking, we'll grab the original default value and set it on the field if the field wasn't provided a value
json_schema_extra_.update({"default": default})
json_schema_extra_.update({"orig_default": default})
elif default is not PydanticUndefined and default_factory is PydanticUndefined:
# Before invoking, we'll check for the original default value and set it on the field if the field has no value
json_schema_extra_.default = default
json_schema_extra_.orig_default = default
elif default is not PydanticUndefined:
default_ = default
provided_args.update({"default": default_})
json_schema_extra_.update({"orig_default": default_})
elif default_factory is not PydanticUndefined:
provided_args.update({"default_factory": default_factory})
# TODO: cannot serialize default_factory...
# json_schema_extra_.update(dict(orig_default_factory=default_factory))
json_schema_extra_.orig_default = default_
return Field(
**provided_args,
json_schema_extra=json_schema_extra_,
json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
)
def OutputField(
# copied from pydantic's Field
default: Any = _Unset,
default_factory: Callable[[], Any] | None = _Unset,
title: str | None = _Unset,
description: str | None = _Unset,
pattern: str | None = _Unset,
@ -362,13 +395,12 @@ def OutputField(
`MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
`UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
: param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
:param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
: param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
:param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
"""
return Field(
default=default,
default_factory=default_factory,
title=title,
description=description,
pattern=pattern,
@ -383,12 +415,12 @@ def OutputField(
decimal_places=decimal_places,
min_length=min_length,
max_length=max_length,
json_schema_extra={
"ui_type": ui_type,
"ui_hidden": ui_hidden,
"ui_order": ui_order,
"_field_kind": "output",
},
json_schema_extra=OutputFieldJSONSchemaExtra(
ui_type=ui_type,
ui_hidden=ui_hidden,
ui_order=ui_order,
field_kind=FieldKind.Output,
).model_dump(exclude_none=True),
)
@ -401,10 +433,10 @@ class UIConfigBase(BaseModel):
tags: Optional[list[str]] = Field(default_factory=None, description="The node's tags")
title: Optional[str] = Field(default=None, description="The node's display name")
category: Optional[str] = Field(default=None, description="The node's category")
version: Optional[str] = Field(
default=None,
version: str = Field(
description='The node\'s version. Should be a valid semver string e.g. "1.0.0" or "3.8.13".',
)
node_pack: Optional[str] = Field(default=None, description="Whether or not this is a custom node")
model_config = ConfigDict(
validate_assignment=True,
@ -447,29 +479,39 @@ class BaseInvocationOutput(BaseModel):
@classmethod
def register_output(cls, output: BaseInvocationOutput) -> None:
"""Registers an invocation output."""
cls._output_classes.add(output)
@classmethod
def get_outputs(cls) -> Iterable[BaseInvocationOutput]:
"""Gets all invocation outputs."""
return cls._output_classes
@classmethod
def get_outputs_union(cls) -> UnionType:
"""Gets a union of all invocation outputs."""
outputs_union = Union[tuple(cls._output_classes)] # type: ignore [valid-type]
return outputs_union # type: ignore [return-value]
@classmethod
def get_output_types(cls) -> Iterable[str]:
return (get_type(i) for i in BaseInvocationOutput.get_outputs())
"""Gets all invocation output types."""
return (i.get_type() for i in BaseInvocationOutput.get_outputs())
@staticmethod
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
"""Adds various UI-facing attributes to the invocation output's OpenAPI schema."""
# Because we use a pydantic Literal field with default value for the invocation type,
# it will be typed as optional in the OpenAPI schema. Make it required manually.
if "required" not in schema or not isinstance(schema["required"], list):
schema["required"] = []
schema["required"].extend(["type"])
@classmethod
def get_type(cls) -> str:
"""Gets the invocation output's type, as provided by the `@invocation_output` decorator."""
return cls.model_fields["type"].default
model_config = ConfigDict(
protected_namespaces=(),
validate_assignment=True,
@ -499,21 +541,29 @@ class BaseInvocation(ABC, BaseModel):
_invocation_classes: ClassVar[set[BaseInvocation]] = set()
@classmethod
def get_type(cls) -> str:
"""Gets the invocation's type, as provided by the `@invocation` decorator."""
return cls.model_fields["type"].default
@classmethod
def register_invocation(cls, invocation: BaseInvocation) -> None:
"""Registers an invocation."""
cls._invocation_classes.add(invocation)
@classmethod
def get_invocations_union(cls) -> UnionType:
"""Gets a union of all invocation types."""
invocations_union = Union[tuple(cls._invocation_classes)] # type: ignore [valid-type]
return invocations_union # type: ignore [return-value]
@classmethod
def get_invocations(cls) -> Iterable[BaseInvocation]:
"""Gets all invocations, respecting the allowlist and denylist."""
app_config = InvokeAIAppConfig.get_config()
allowed_invocations: set[BaseInvocation] = set()
for sc in cls._invocation_classes:
invocation_type = get_type(sc)
invocation_type = sc.get_type()
is_in_allowlist = (
invocation_type in app_config.allow_nodes if isinstance(app_config.allow_nodes, list) else True
)
@ -526,28 +576,32 @@ class BaseInvocation(ABC, BaseModel):
@classmethod
def get_invocations_map(cls) -> dict[str, BaseInvocation]:
# Get the type strings out of the literals and into a dictionary
return {get_type(i): i for i in BaseInvocation.get_invocations()}
"""Gets a map of all invocation types to their invocation classes."""
return {i.get_type(): i for i in BaseInvocation.get_invocations()}
@classmethod
def get_invocation_types(cls) -> Iterable[str]:
return (get_type(i) for i in BaseInvocation.get_invocations())
"""Gets all invocation types."""
return (i.get_type() for i in BaseInvocation.get_invocations())
@classmethod
def get_output_type(cls) -> BaseInvocationOutput:
def get_output_annotation(cls) -> BaseInvocationOutput:
"""Gets the invocation's output annotation (i.e. the return annotation of its `invoke()` method)."""
return signature(cls.invoke).return_annotation
@staticmethod
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel]) -> None:
# Add the various UI-facing attributes to the schema. These are used to build the invocation templates.
uiconfig = getattr(model_class, "UIConfig", None)
if uiconfig and hasattr(uiconfig, "title"):
schema["title"] = uiconfig.title
if uiconfig and hasattr(uiconfig, "tags"):
schema["tags"] = uiconfig.tags
if uiconfig and hasattr(uiconfig, "category"):
schema["category"] = uiconfig.category
if uiconfig and hasattr(uiconfig, "version"):
def json_schema_extra(schema: dict[str, Any], model_class: Type[BaseModel], *args, **kwargs) -> None:
"""Adds various UI-facing attributes to the invocation's OpenAPI schema."""
uiconfig = cast(UIConfigBase | None, getattr(model_class, "UIConfig", None))
if uiconfig is not None:
if uiconfig.title is not None:
schema["title"] = uiconfig.title
if uiconfig.tags is not None:
schema["tags"] = uiconfig.tags
if uiconfig.category is not None:
schema["category"] = uiconfig.category
if uiconfig.node_pack is not None:
schema["node_pack"] = uiconfig.node_pack
schema["version"] = uiconfig.version
if "required" not in schema or not isinstance(schema["required"], list):
schema["required"] = []
@ -559,6 +613,10 @@ class BaseInvocation(ABC, BaseModel):
pass
def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
"""
Internal invoke method, calls `invoke()` after some prep.
Handles optional fields that are required to call `invoke()` and invocation cache.
"""
for field_name, field in self.model_fields.items():
if not field.json_schema_extra or callable(field.json_schema_extra):
# something has gone terribly awry, we should always have this and it should be a dict
@ -598,21 +656,20 @@ class BaseInvocation(ABC, BaseModel):
context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
return self.invoke(context)
def get_type(self) -> str:
return self.model_fields["type"].default
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={"_field_kind": "internal"},
json_schema_extra={"field_kind": FieldKind.NodeAttribute},
)
is_intermediate: bool = Field(
default=False,
description="Whether or not this is an intermediate invocation.",
json_schema_extra={"ui_type": UIType.IsIntermediate, "_field_kind": "internal"},
json_schema_extra={"ui_type": "IsIntermediate", "field_kind": FieldKind.NodeAttribute},
)
use_cache: bool = Field(
default=True, description="Whether or not to use the cache", json_schema_extra={"_field_kind": "internal"}
default=True,
description="Whether or not to use the cache",
json_schema_extra={"field_kind": FieldKind.NodeAttribute},
)
UIConfig: ClassVar[Type[UIConfigBase]]
@ -629,12 +686,15 @@ class BaseInvocation(ABC, BaseModel):
TBaseInvocation = TypeVar("TBaseInvocation", bound=BaseInvocation)
RESERVED_INPUT_FIELD_NAMES = {
RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
"id",
"is_intermediate",
"use_cache",
"type",
"workflow",
}
RESERVED_INPUT_FIELD_NAMES = {
"metadata",
}
@ -652,40 +712,59 @@ RESERVED_PYDANTIC_FIELD_NAMES = {m[0] for m in 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
- Must not override any pydantic reserved fields
- Must have a type annotation
- Must have a json_schema_extra dict
- Must have field_kind in json_schema_extra
- Field name must not be reserved, according to its field_kind
"""
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
)
if not field.annotation:
raise InvalidFieldError(f'Invalid field type "{name}" on "{model_type}" (missing annotation)')
if not isinstance(field.json_schema_extra, dict):
raise InvalidFieldError(
f'Invalid field definition for "{name}" on "{model_type}" (missing json_schema_extra dict)'
)
field_kind = field.json_schema_extra.get("field_kind", None)
# must have a field_kind
if field_kind is None or field_kind not in {"input", "output", "internal"}:
if not isinstance(field_kind, FieldKind):
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:
if field_kind is FieldKind.Input and (
name in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES or 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:
if field_kind is FieldKind.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
):
if (field_kind is FieldKind.Internal) and name not in RESERVED_INPUT_FIELD_NAMES:
raise InvalidFieldError(
f'Invalid field name "{name}" on "{model_type}" (internal field without reserved name)'
)
# node attribute fields *must* be in the reserved list
if (
field_kind is FieldKind.NodeAttribute
and name not in RESERVED_NODE_ATTRIBUTE_FIELD_NAMES
and name not in RESERVED_OUTPUT_FIELD_NAMES
):
raise InvalidFieldError(
f'Invalid field name "{name}" on "{model_type}" (node attribute field without reserved name)'
)
ui_type = field.json_schema_extra.get("ui_type", None)
if isinstance(ui_type, str) and ui_type.startswith("DEPRECATED_"):
logger.warn(f"\"UIType.{ui_type.split('_')[-1]}\" is deprecated, ignoring")
field.json_schema_extra.pop("ui_type")
return None
@ -720,21 +799,30 @@ def invocation(
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:
cls.UIConfig = type(uiconf_name, (UIConfigBase,), {})
if title is not None:
cls.UIConfig.title = title
if tags is not None:
cls.UIConfig.tags = tags
if category is not None:
cls.UIConfig.category = category
uiconfig_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconfig_name:
cls.UIConfig = type(uiconfig_name, (UIConfigBase,), {})
cls.UIConfig.title = title
cls.UIConfig.tags = tags
cls.UIConfig.category = category
# Grab the node pack's name from the module name, if it's a custom node
module_name = cls.__module__.split(".")[0]
if module_name.endswith(CUSTOM_NODE_PACK_SUFFIX):
cls.UIConfig.node_pack = module_name.split(CUSTOM_NODE_PACK_SUFFIX)[0]
else:
cls.UIConfig.node_pack = None
if version is not None:
try:
semver.Version.parse(version)
except ValueError as e:
raise InvalidVersionError(f'Invalid version string for node "{invocation_type}": "{version}"') from e
cls.UIConfig.version = version
else:
logger.warn(f'No version specified for node "{invocation_type}", using "1.0.0"')
cls.UIConfig.version = "1.0.0"
if use_cache is not None:
cls.model_fields["use_cache"].default = use_cache
@ -749,7 +837,7 @@ def invocation(
invocation_type_annotation = Literal[invocation_type] # type: ignore
invocation_type_field = Field(
title="type", default=invocation_type, json_schema_extra={"_field_kind": "internal"}
title="type", default=invocation_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
)
docstring = cls.__doc__
@ -795,7 +883,9 @@ def invocation_output(
# Add the output type to the model.
output_type_annotation = Literal[output_type] # type: ignore
output_type_field = Field(title="type", default=output_type, json_schema_extra={"_field_kind": "internal"})
output_type_field = Field(
title="type", default=output_type, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
)
docstring = cls.__doc__
cls = create_model(
@ -827,7 +917,7 @@ WorkflowFieldValidator = TypeAdapter(WorkflowField)
class WithWorkflow(BaseModel):
workflow: Optional[WorkflowField] = Field(
default=None, description=FieldDescriptions.workflow, json_schema_extra={"_field_kind": "internal"}
default=None, description=FieldDescriptions.workflow, json_schema_extra={"field_kind": FieldKind.NodeAttribute}
)
@ -845,5 +935,11 @@ MetadataFieldValidator = TypeAdapter(MetadataField)
class WithMetadata(BaseModel):
metadata: Optional[MetadataField] = Field(
default=None, description=FieldDescriptions.metadata, json_schema_extra={"_field_kind": "internal"}
default=None,
description=FieldDescriptions.metadata,
json_schema_extra=InputFieldJSONSchemaExtra(
field_kind=FieldKind.Internal,
input=Input.Connection,
orig_required=False,
).model_dump(exclude_none=True),
)

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

@ -5,7 +5,7 @@ import numpy as np
from pydantic import ValidationInfo, field_validator
from invokeai.app.invocations.primitives import IntegerCollectionOutput
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.app.util.misc import SEED_MAX
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@ -55,7 +55,7 @@ class RangeOfSizeInvocation(BaseInvocation):
title="Random Range",
tags=["range", "integer", "random", "collection"],
category="collections",
version="1.0.0",
version="1.0.1",
use_cache=False,
)
class RandomRangeInvocation(BaseInvocation):
@ -65,10 +65,10 @@ class RandomRangeInvocation(BaseInvocation):
high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
size: int = InputField(default=1, description="The number of values to generate")
seed: int = InputField(
default=0,
ge=0,
le=SEED_MAX,
description="The seed for the RNG (omit for random)",
default_factory=get_random_seed,
)
def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:

34
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -96,7 +96,7 @@ class ControlOutput(BaseInvocationOutput):
control: ControlField = OutputField(description=FieldDescriptions.control)
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.0.0")
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet", version="1.1.0")
class ControlNetInvocation(BaseInvocation):
"""Collects ControlNet info to pass to other nodes"""
@ -173,7 +173,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithWorkflow):
title="Canny Processor",
tags=["controlnet", "canny"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class CannyImageProcessorInvocation(ImageProcessorInvocation):
"""Canny edge detection for ControlNet"""
@ -196,7 +196,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
title="HED (softedge) Processor",
tags=["controlnet", "hed", "softedge"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class HedImageProcessorInvocation(ImageProcessorInvocation):
"""Applies HED edge detection to image"""
@ -225,7 +225,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
title="Lineart Processor",
tags=["controlnet", "lineart"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class LineartImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art processing to image"""
@ -247,7 +247,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
title="Lineart Anime Processor",
tags=["controlnet", "lineart", "anime"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art anime processing to image"""
@ -270,7 +270,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
title="Openpose Processor",
tags=["controlnet", "openpose", "pose"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Openpose processing to image"""
@ -295,7 +295,7 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
title="Midas Depth Processor",
tags=["controlnet", "midas"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Midas depth processing to image"""
@ -322,7 +322,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
title="Normal BAE Processor",
tags=["controlnet"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies NormalBae processing to image"""
@ -339,7 +339,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
@invocation(
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.0.0"
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.1.0"
)
class MlsdImageProcessorInvocation(ImageProcessorInvocation):
"""Applies MLSD processing to image"""
@ -362,7 +362,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
@invocation(
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.0.0"
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.1.0"
)
class PidiImageProcessorInvocation(ImageProcessorInvocation):
"""Applies PIDI processing to image"""
@ -389,7 +389,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
title="Content Shuffle Processor",
tags=["controlnet", "contentshuffle"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
"""Applies content shuffle processing to image"""
@ -419,7 +419,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
title="Zoe (Depth) Processor",
tags=["controlnet", "zoe", "depth"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Zoe depth processing to image"""
@ -435,7 +435,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
title="Mediapipe Face Processor",
tags=["controlnet", "mediapipe", "face"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
"""Applies mediapipe face processing to image"""
@ -458,7 +458,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
title="Leres (Depth) Processor",
tags=["controlnet", "leres", "depth"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class LeresImageProcessorInvocation(ImageProcessorInvocation):
"""Applies leres processing to image"""
@ -487,7 +487,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
title="Tile Resample Processor",
tags=["controlnet", "tile"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class TileResamplerProcessorInvocation(ImageProcessorInvocation):
"""Tile resampler processor"""
@ -527,7 +527,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
title="Segment Anything Processor",
tags=["controlnet", "segmentanything"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
"""Applies segment anything processing to image"""
@ -569,7 +569,7 @@ class SamDetectorReproducibleColors(SamDetector):
title="Color Map Processor",
tags=["controlnet"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
"""Generates a color map from the provided image"""

11
invokeai/app/invocations/custom_nodes/init.py
View File

@ -6,6 +6,7 @@ import sys
from importlib.util import module_from_spec, spec_from_file_location
from pathlib import Path
from invokeai.app.invocations.baseinvocation import CUSTOM_NODE_PACK_SUFFIX
from invokeai.backend.util.logging import InvokeAILogger
logger = InvokeAILogger.get_logger()
@ -32,13 +33,15 @@ for d in Path(__file__).parent.iterdir():
if module_name in globals():
continue
# we have a legit module to import
spec = spec_from_file_location(module_name, init.absolute())
# load the module, appending adding a suffix to identify it as a custom node pack
spec = spec_from_file_location(f"{module_name}{CUSTOM_NODE_PACK_SUFFIX}", init.absolute())
if spec is None or spec.loader is None:
logger.warn(f"Could not load {init}")
continue
logger.info(f"Loading node pack {module_name}")
module = module_from_spec(spec)
sys.modules[spec.name] = module
spec.loader.exec_module(module)
@ -47,5 +50,5 @@ for d in Path(__file__).parent.iterdir():
del init, module_name
logger.info(f"Loaded {loaded_count} modules from {Path(__file__).parent}")
if loaded_count > 0:
logger.info(f"Loaded {loaded_count} node packs from {Path(__file__).parent}")

2
invokeai/app/invocations/cv.py
View File

@ -11,7 +11,7 @@ from invokeai.app.services.image_records.image_records_common import ImageCatego
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.0.0")
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.1.0")
class CvInpaintInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Simple inpaint using opencv."""

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

@ -438,7 +438,7 @@ def get_faces_list(
return all_faces
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.0.2")
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.1.0")
class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Bound, extract, and mask a face from an image using MediaPipe detection"""
@ -532,7 +532,7 @@ class FaceOffInvocation(BaseInvocation, WithWorkflow, WithMetadata):
return output
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.0.2")
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.1.0")
class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Face mask creation using mediapipe face detection"""
@ -650,7 +650,7 @@ class FaceMaskInvocation(BaseInvocation, WithWorkflow, WithMetadata):
@invocation(
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.0.2"
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.1.0"
)
class FaceIdentifierInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""

76
invokeai/app/invocations/image.py
View File

@ -8,7 +8,7 @@ import numpy
from PIL import Image, ImageChops, ImageFilter, ImageOps
from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker
@ -36,7 +36,7 @@ class ShowImageInvocation(BaseInvocation):
)
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.0.0")
@invocation("blank_image", title="Blank Image", tags=["image"], category="image", version="1.1.0")
class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Creates a blank image and forwards it to the pipeline"""
@ -66,7 +66,7 @@ class BlankImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
)
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.0.0")
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image", version="1.1.0")
class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Crops an image to a specified box. The box can be outside of the image."""
@ -100,7 +100,7 @@ class ImageCropInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.0.1")
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image", version="1.1.0")
class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Pastes an image into another image."""
@ -154,7 +154,7 @@ class ImagePasteInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.0.0")
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image", version="1.1.0")
class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Extracts the alpha channel of an image as a mask."""
@ -186,7 +186,7 @@ class MaskFromAlphaInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.0.0")
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image", version="1.1.0")
class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
@ -220,7 +220,7 @@ class ImageMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.0.0")
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image", version="1.1.0")
class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Gets a channel from an image."""
@ -253,7 +253,7 @@ class ImageChannelInvocation(BaseInvocation, WithWorkflow, WithMetadata):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.0.0")
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image", version="1.1.0")
class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Converts an image to a different mode."""
@ -283,7 +283,7 @@ class ImageConvertInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.0.0")
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image", version="1.1.0")
class ImageBlurInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Blurs an image"""
@ -338,7 +338,7 @@ PIL_RESAMPLING_MAP = {
}
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.0.0")
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image", version="1.1.0")
class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Resizes an image to specific dimensions"""
@ -375,7 +375,7 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata, WithWorkflow):
)
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.0.0")
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image", version="1.1.0")
class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Scales an image by a factor"""
@ -417,7 +417,7 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata, WithWorkflow):
)
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.0.0")
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image", version="1.1.0")
class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Linear interpolation of all pixels of an image"""
@ -451,7 +451,7 @@ class ImageLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.0.0")
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image", version="1.1.0")
class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Inverse linear interpolation of all pixels of an image"""
@ -485,7 +485,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.0.0")
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image", version="1.1.0")
class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add blur to NSFW-flagged images"""
@ -532,7 +532,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithWorkflow):
title="Add Invisible Watermark",
tags=["image", "watermark"],
category="image",
version="1.0.0",
version="1.1.0",
)
class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Add an invisible watermark to an image"""
@ -561,7 +561,7 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithWorkflow):
)
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.0.0")
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image", version="1.1.0")
class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Applies an edge mask to an image"""
@ -612,7 +612,7 @@ class MaskEdgeInvocation(BaseInvocation, WithWorkflow, WithMetadata):
title="Combine Masks",
tags=["image", "mask", "multiply"],
category="image",
version="1.0.0",
version="1.1.0",
)
class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
@ -644,7 +644,7 @@ class MaskCombineInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.0.0")
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image", version="1.1.0")
class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""
Shifts the colors of a target image to match the reference image, optionally
@ -755,7 +755,7 @@ class ColorCorrectInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.0.0")
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image", version="1.1.0")
class ImageHueAdjustmentInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Adjusts the Hue of an image."""
@ -858,7 +858,7 @@ CHANNEL_FORMATS = {
"value",
],
category="image",
version="1.0.0",
version="1.1.0",
)
class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Add or subtract a value from a specific color channel of an image."""
@ -929,7 +929,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"value",
],
category="image",
version="1.0.0",
version="1.1.0",
)
class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Scale a specific color channel of an image."""
@ -988,7 +988,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithWorkflow, WithMetadata)
title="Save Image",
tags=["primitives", "image"],
category="primitives",
version="1.0.1",
version="1.1.0",
use_cache=False,
)
class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
@ -1017,3 +1017,35 @@ class SaveImageInvocation(BaseInvocation, WithWorkflow, WithMetadata):
width=image_dto.width,
height=image_dto.height,
)
@invocation(
"linear_ui_output",
title="Linear UI Image Output",
tags=["primitives", "image"],
category="primitives",
version="1.0.1",
use_cache=False,
)
class LinearUIOutputInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Handles Linear UI Image Outputting tasks."""
image: ImageField = InputField(description=FieldDescriptions.image)
board: Optional[BoardField] = InputField(default=None, description=FieldDescriptions.board, input=Input.Direct)
def invoke(self, context: InvocationContext) -> ImageOutput:
image_dto = context.services.images.get_dto(self.image.image_name)
if self.board:
context.services.board_images.add_image_to_board(self.board.board_id, self.image.image_name)
if image_dto.is_intermediate != self.is_intermediate:
context.services.images.update(
self.image.image_name, changes=ImageRecordChanges(is_intermediate=self.is_intermediate)
)
return ImageOutput(
image=ImageField(image_name=self.image.image_name),
width=image_dto.width,
height=image_dto.height,
)

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

@ -8,7 +8,7 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.app.util.misc import SEED_MAX
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
@ -118,7 +118,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
return si
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with a solid color"""
@ -154,17 +154,17 @@ class InfillColorInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.1")
class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image with tiles of the image"""
image: ImageField = InputField(description="The image to infill")
tile_size: int = InputField(default=32, ge=1, description="The tile size (px)")
seed: int = InputField(
default=0,
ge=0,
le=SEED_MAX,
description="The seed to use for tile generation (omit for random)",
default_factory=get_random_seed,
)
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -192,7 +192,7 @@ class InfillTileInvocation(BaseInvocation, WithWorkflow, WithMetadata):
@invocation(
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0"
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0"
)
class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
@ -245,7 +245,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.0.0")
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using the LaMa model"""
@ -274,7 +274,7 @@ class LaMaInfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.1.0")
class CV2InfillInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Infills transparent areas of an image using OpenCV Inpainting"""

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

@ -11,7 +11,6 @@ from invokeai.app.invocations.baseinvocation import (
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
@ -67,7 +66,7 @@ class IPAdapterInvocation(BaseInvocation):
# 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=-1, 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", title="Weight"
)
begin_step_percent: float = InputField(

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

@ -274,7 +274,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
ui_order=7,
)
latents: Optional[LatentsField] = InputField(
default=None, description=FieldDescriptions.latents, input=Input.Connection
default=None,
description=FieldDescriptions.latents,
input=Input.Connection,
ui_order=4,
)
denoise_mask: Optional[DenoiseMaskField] = InputField(
default=None,
@ -706,7 +709,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
)
with (
ExitStack() as exit_stack,
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),
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,
@ -790,7 +792,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
title="Latents to Image",
tags=["latents", "image", "vae", "l2i"],
category="latents",
version="1.0.0",
version="1.1.0",
)
class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""

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

@ -112,7 +112,7 @@ GENERATION_MODES = Literal[
]
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.0")
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.0.1")
class CoreMetadataInvocation(BaseInvocation):
"""Collects core generation metadata into a MetadataField"""
@ -160,7 +160,7 @@ class CoreMetadataInvocation(BaseInvocation):
)
# High resolution fix metadata.
hrf_enabled: Optional[float] = InputField(
hrf_enabled: Optional[bool] = InputField(
default=None,
description="Whether or not high resolution fix was enabled.",
)

2
invokeai/app/invocations/model.py
View File

@ -14,7 +14,6 @@ from .baseinvocation import (
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
@ -395,7 +394,6 @@ class VaeLoaderInvocation(BaseInvocation):
vae_model: VAEModelField = InputField(
description=FieldDescriptions.vae_model,
input=Input.Direct,
ui_type=UIType.VaeModel,
title="VAE",
)

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

@ -6,7 +6,7 @@ from pydantic import field_validator
from invokeai.app.invocations.latent import LatentsField
from invokeai.app.shared.fields import FieldDescriptions
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.app.util.misc import SEED_MAX
from ...backend.util.devices import choose_torch_device, torch_dtype
from .baseinvocation import (
@ -83,16 +83,16 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
title="Noise",
tags=["latents", "noise"],
category="latents",
version="1.0.0",
version="1.0.1",
)
class NoiseInvocation(BaseInvocation):
"""Generates latent noise."""
seed: int = InputField(
default=0,
ge=0,
le=SEED_MAX,
description=FieldDescriptions.seed,
default_factory=get_random_seed,
)
width: int = InputField(
default=512,

2
invokeai/app/invocations/onnx.py
View File

@ -326,7 +326,7 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
title="ONNX Latents to Image",
tags=["latents", "image", "vae", "onnx"],
category="image",
version="1.0.0",
version="1.1.0",
)
class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""

20
invokeai/app/invocations/primitives.py
View File

@ -62,12 +62,12 @@ class BooleanInvocation(BaseInvocation):
title="Boolean Collection Primitive",
tags=["primitives", "boolean", "collection"],
category="primitives",
version="1.0.0",
version="1.0.1",
)
class BooleanCollectionInvocation(BaseInvocation):
"""A collection of boolean primitive values"""
collection: list[bool] = InputField(default_factory=list, description="The collection of boolean values")
collection: list[bool] = InputField(default=[], description="The collection of boolean values")
def invoke(self, context: InvocationContext) -> BooleanCollectionOutput:
return BooleanCollectionOutput(collection=self.collection)
@ -111,12 +111,12 @@ class IntegerInvocation(BaseInvocation):
title="Integer Collection Primitive",
tags=["primitives", "integer", "collection"],
category="primitives",
version="1.0.0",
version="1.0.1",
)
class IntegerCollectionInvocation(BaseInvocation):
"""A collection of integer primitive values"""
collection: list[int] = InputField(default_factory=list, description="The collection of integer values")
collection: list[int] = InputField(default=[], description="The collection of integer values")
def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
return IntegerCollectionOutput(collection=self.collection)
@ -158,12 +158,12 @@ class FloatInvocation(BaseInvocation):
title="Float Collection Primitive",
tags=["primitives", "float", "collection"],
category="primitives",
version="1.0.0",
version="1.0.1",
)
class FloatCollectionInvocation(BaseInvocation):
"""A collection of float primitive values"""
collection: list[float] = InputField(default_factory=list, description="The collection of float values")
collection: list[float] = InputField(default=[], description="The collection of float values")
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
return FloatCollectionOutput(collection=self.collection)
@ -205,12 +205,12 @@ class StringInvocation(BaseInvocation):
title="String Collection Primitive",
tags=["primitives", "string", "collection"],
category="primitives",
version="1.0.0",
version="1.0.1",
)
class StringCollectionInvocation(BaseInvocation):
"""A collection of string primitive values"""
collection: list[str] = InputField(default_factory=list, description="The collection of string values")
collection: list[str] = InputField(default=[], description="The collection of string values")
def invoke(self, context: InvocationContext) -> StringCollectionOutput:
return StringCollectionOutput(collection=self.collection)
@ -467,13 +467,13 @@ class ConditioningInvocation(BaseInvocation):
title="Conditioning Collection Primitive",
tags=["primitives", "conditioning", "collection"],
category="primitives",
version="1.0.0",
version="1.0.1",
)
class ConditioningCollectionInvocation(BaseInvocation):
"""A collection of conditioning tensor primitive values"""
collection: list[ConditioningField] = InputField(
default_factory=list,
default=[],
description="The collection of conditioning tensors",
)

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

@ -44,7 +44,7 @@ class DynamicPromptInvocation(BaseInvocation):
title="Prompts from File",
tags=["prompt", "file"],
category="prompt",
version="1.0.0",
version="1.0.1",
)
class PromptsFromFileInvocation(BaseInvocation):
"""Loads prompts from a text file"""
@ -82,7 +82,7 @@ class PromptsFromFileInvocation(BaseInvocation):
end_line = start_line + max_prompts
if max_prompts <= 0:
end_line = np.iinfo(np.int32).max
with open(file_path) as f:
with open(file_path, encoding="utf-8") as f:
for i, line in enumerate(f):
if i >= start_line and i < end_line:
prompts.append((pre_prompt or "") + line.strip() + (post_prompt or ""))

3
invokeai/app/invocations/t2i_adapter.py
View File

@ -9,7 +9,6 @@ from invokeai.app.invocations.baseinvocation import (
InputField,
InvocationContext,
OutputField,
UIType,
invocation,
invocation_output,
)
@ -59,7 +58,7 @@ class T2IAdapterInvocation(BaseInvocation):
ui_order=-1,
)
weight: Union[float, list[float]] = InputField(
default=1, ge=0, description="The weight given to the T2I-Adapter", ui_type=UIType.Float, title="Weight"
default=1, ge=0, description="The weight given to the T2I-Adapter", title="Weight"
)
begin_step_percent: float = InputField(
default=0, ge=-1, le=2, description="When the T2I-Adapter is first applied (% of total steps)"

22
invokeai/app/invocations/upscale.py
View File

@ -2,16 +2,16 @@
from pathlib import Path
from typing import Literal
import cv2 as cv
import cv2
import numpy as np
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from PIL import Image
from pydantic import ConfigDict
from realesrgan import RealESRGANer
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
from invokeai.backend.util.devices import choose_torch_device
from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
@ -29,7 +29,7 @@ if choose_torch_device() == torch.device("mps"):
from torch import mps
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.1.0")
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.2.0")
class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
"""Upscales an image using RealESRGAN."""
@ -92,9 +92,9 @@ class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
upsampler = RealESRGANer(
upscaler = RealESRGAN(
scale=netscale,
model_path=str(models_path / esrgan_model_path),
model_path=models_path / esrgan_model_path,
model=rrdbnet_model,
half=False,
tile=self.tile_size,
@ -102,15 +102,9 @@ class ESRGANInvocation(BaseInvocation, WithWorkflow, WithMetadata):
# prepare image - Real-ESRGAN uses cv2 internally, and cv2 uses BGR vs RGB for PIL
# TODO: This strips the alpha... is that okay?
cv_image = cv.cvtColor(np.array(image.convert("RGB")), cv.COLOR_RGB2BGR)
# We can pass an `outscale` value here, but it just resizes the image by that factor after
# upscaling, so it's kinda pointless for our purposes. If you want something other than 4x
# upscaling, you'll need to add a resize node after this one.
upscaled_image, img_mode = upsampler.enhance(cv_image)
# back to PIL
pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
cv2_image = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
upscaled_image = upscaler.upscale(cv2_image)
pil_image = Image.fromarray(cv2.cvtColor(upscaled_image, cv2.COLOR_BGR2RGB)).convert("RGBA")
torch.cuda.empty_cache()
if choose_torch_device() == torch.device("mps"):

24
invokeai/app/services/config/config_base.py
View File

@ -15,7 +15,7 @@ import os
import sys
from argparse import ArgumentParser
from pathlib import Path
from typing import ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
from omegaconf import DictConfig, ListConfig, OmegaConf
from pydantic_settings import BaseSettings, SettingsConfigDict
@ -24,10 +24,7 @@ from invokeai.app.services.config.config_common import PagingArgumentParser, int
class InvokeAISettings(BaseSettings):
"""
Runtime configuration settings in which default values are
read from an omegaconf .yaml file.
"""
"""Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
initconf: ClassVar[Optional[DictConfig]] = None
argparse_groups: ClassVar[Dict] = {}
@ -35,6 +32,7 @@ class InvokeAISettings(BaseSettings):
model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
def parse_args(self, argv: Optional[list] = sys.argv[1:]):
"""Call to parse command-line arguments."""
parser = self.get_parser()
opt, unknown_opts = parser.parse_known_args(argv)
if len(unknown_opts) > 0:
@ -49,20 +47,19 @@ class InvokeAISettings(BaseSettings):
setattr(self, name, value)
def to_yaml(self) -> str:
"""
Return a YAML string representing our settings. This can be used
as the contents of `invokeai.yaml` to restore settings later.
"""
"""Return a YAML string representing our settings. This can be used as the contents of `invokeai.yaml` to restore settings later."""
cls = self.__class__
type = get_args(get_type_hints(cls)["type"])[0]
field_dict = {type: {}}
field_dict: Dict[str, Dict[str, Any]] = {type: {}}
for name, field in self.model_fields.items():
if name in cls._excluded_from_yaml():
continue
assert isinstance(field.json_schema_extra, dict)
category = (
field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
)
value = getattr(self, name)
assert isinstance(category, str)
if category not in field_dict[type]:
field_dict[type][category] = {}
# keep paths as strings to make it easier to read
@ -72,6 +69,7 @@ class InvokeAISettings(BaseSettings):
@classmethod
def add_parser_arguments(cls, parser):
"""Dynamically create arguments for a settings parser."""
if "type" in get_type_hints(cls):
settings_stanza = get_args(get_type_hints(cls)["type"])[0]
else:
@ -116,6 +114,7 @@ class InvokeAISettings(BaseSettings):
@classmethod
def cmd_name(cls, command_field: str = "type") -> str:
"""Return the category of a setting."""
hints = get_type_hints(cls)
if command_field in hints:
return get_args(hints[command_field])[0]
@ -124,6 +123,7 @@ class InvokeAISettings(BaseSettings):
@classmethod
def get_parser(cls) -> ArgumentParser:
"""Get the command-line parser for a setting."""
parser = PagingArgumentParser(
prog=cls.cmd_name(),
description=cls.__doc__,
@ -152,10 +152,14 @@ class InvokeAISettings(BaseSettings):
"free_gpu_mem",
"xformers_enabled",
"tiled_decode",
"lora_dir",
"embedding_dir",
"controlnet_dir",
]
@classmethod
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
"""Add the argparse arguments for a setting parser."""
field_type = get_type_hints(cls).get(name)
default = (
default_override

145
invokeai/app/services/config/config_default.py
View File

@ -177,6 +177,7 @@ from typing import ClassVar, Dict, List, Literal, Optional, Union, get_type_hint
from omegaconf import DictConfig, OmegaConf
from pydantic import Field, TypeAdapter
from pydantic.config import JsonDict
from pydantic_settings import SettingsConfigDict
from .config_base import InvokeAISettings
@ -188,28 +189,24 @@ DEFAULT_MAX_VRAM = 0.5
class Categories(object):
WebServer = {"category": "Web Server"}
Features = {"category": "Features"}
Paths = {"category": "Paths"}
Logging = {"category": "Logging"}
Development = {"category": "Development"}
Other = {"category": "Other"}
ModelCache = {"category": "Model Cache"}
Device = {"category": "Device"}
Generation = {"category": "Generation"}
Queue = {"category": "Queue"}
Nodes = {"category": "Nodes"}
MemoryPerformance = {"category": "Memory/Performance"}
"""Category headers for configuration variable groups."""
WebServer: JsonDict = {"category": "Web Server"}
Features: JsonDict = {"category": "Features"}
Paths: JsonDict = {"category": "Paths"}
Logging: JsonDict = {"category": "Logging"}
Development: JsonDict = {"category": "Development"}
Other: JsonDict = {"category": "Other"}
ModelCache: JsonDict = {"category": "Model Cache"}
Device: JsonDict = {"category": "Device"}
Generation: JsonDict = {"category": "Generation"}
Queue: JsonDict = {"category": "Queue"}
Nodes: JsonDict = {"category": "Nodes"}
MemoryPerformance: JsonDict = {"category": "Memory/Performance"}
class InvokeAIAppConfig(InvokeAISettings):
"""
Generate images using Stable Diffusion. Use "invokeai" to launch
the command-line client (recommended for experts only), or
"invokeai-web" to launch the web server. Global options
can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
setting environment variables INVOKEAI_<setting>.
"""
"""Configuration object for InvokeAI App."""
singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
singleton_init: ClassVar[Optional[Dict]] = None
@ -234,15 +231,12 @@ class InvokeAIAppConfig(InvokeAISettings):
# PATHS
root : Optional[Path] = Field(default=None, description='InvokeAI runtime root directory', json_schema_extra=Categories.Paths)
autoimport_dir : Optional[Path] = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
lora_dir : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
embedding_dir : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
controlnet_dir : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
conf_path : Optional[Path] = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
models_dir : Optional[Path] = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
legacy_conf_dir : Optional[Path] = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
db_dir : Optional[Path] = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
outdir : Optional[Path] = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
autoimport_dir : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
conf_path : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
models_dir : Path = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
legacy_conf_dir : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
db_dir : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
outdir : 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)
@ -285,11 +279,15 @@ class InvokeAIAppConfig(InvokeAISettings):
# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.MemoryPerformance)
max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.MemoryPerformance)
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.MemoryPerformance)
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.MemoryPerformance)
lora_dir : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Paths)
embedding_dir : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
controlnet_dir : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Paths)
# this is not referred to in the source code and can be removed entirely
#free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
# See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
# fmt: on
@ -303,8 +301,8 @@ class InvokeAIAppConfig(InvokeAISettings):
clobber=False,
):
"""
Update settings with contents of init file, environment, and
command-line settings.
Update settings with contents of init file, environment, and command-line settings.
:param conf: alternate Omegaconf dictionary object
:param argv: aternate sys.argv list
:param clobber: ovewrite any initialization parameters passed during initialization
@ -337,9 +335,7 @@ class InvokeAIAppConfig(InvokeAISettings):
@classmethod
def get_config(cls, **kwargs) -> InvokeAIAppConfig:
"""
This returns a singleton InvokeAIAppConfig configuration object.
"""
"""Return a singleton InvokeAIAppConfig configuration object."""
if (
cls.singleton_config is None
or type(cls.singleton_config) is not cls
@ -351,9 +347,7 @@ class InvokeAIAppConfig(InvokeAISettings):
@property
def root_path(self) -> Path:
"""
Path to the runtime root directory
"""
"""Path to the runtime root directory."""
if self.root:
root = Path(self.root).expanduser().absolute()
else:
@ -363,9 +357,7 @@ class InvokeAIAppConfig(InvokeAISettings):
@property
def root_dir(self) -> Path:
"""
Alias for above.
"""
"""Alias for above."""
return self.root_path
def _resolve(self, partial_path: Path) -> Path:
@ -373,108 +365,95 @@ class InvokeAIAppConfig(InvokeAISettings):
@property
def init_file_path(self) -> Path:
"""
Path to invokeai.yaml
"""
return self._resolve(INIT_FILE)
"""Path to invokeai.yaml."""
resolved_path = self._resolve(INIT_FILE)
assert resolved_path is not None
return resolved_path
@property
def output_path(self) -> Path:
"""
Path to defaults outputs directory.
"""
def output_path(self) -> Optional[Path]:
"""Path to defaults outputs directory."""
return self._resolve(self.outdir)
@property
def db_path(self) -> Path:
"""
Path to the invokeai.db file.
"""
return self._resolve(self.db_dir) / DB_FILE
"""Path to the invokeai.db file."""
db_dir = self._resolve(self.db_dir)
assert db_dir is not None
return db_dir / DB_FILE
@property
def model_conf_path(self) -> Path:
"""
Path to models configuration file.
"""
def model_conf_path(self) -> Optional[Path]:
"""Path to models configuration file."""
return self._resolve(self.conf_path)
@property
def legacy_conf_path(self) -> Path:
"""
Path to directory of legacy configuration files (e.g. v1-inference.yaml)
"""
def legacy_conf_path(self) -> Optional[Path]:
"""Path to directory of legacy configuration files (e.g. v1-inference.yaml)."""
return self._resolve(self.legacy_conf_dir)
@property
def models_path(self) -> Path:
"""
Path to the models directory
"""
def models_path(self) -> Optional[Path]:
"""Path to the models directory."""
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)
"""Path to the custom nodes directory."""
custom_nodes_path = self._resolve(self.custom_nodes_dir)
assert custom_nodes_path is not None
return custom_nodes_path
# the following methods support legacy calls leftover from the Globals era
@property
def full_precision(self) -> bool:
"""Return true if precision set to float32"""
"""Return true if precision set to float32."""
return self.precision == "float32"
@property
def try_patchmatch(self) -> bool:
"""Return true if patchmatch true"""
"""Return true if patchmatch true."""
return self.patchmatch
@property
def nsfw_checker(self) -> bool:
"""NSFW node is always active and disabled from Web UIe"""
"""Return value for NSFW checker. The NSFW node is always active and disabled from Web UI."""
return True
@property
def invisible_watermark(self) -> bool:
"""invisible watermark node is always active and disabled from Web UIe"""
"""Return value of invisible watermark. It is always active and disabled from Web UI."""
return True
@property
def ram_cache_size(self) -> Union[Literal["auto"], float]:
"""Return the ram cache size using the legacy or modern setting."""
return self.max_cache_size or self.ram
@property
def vram_cache_size(self) -> Union[Literal["auto"], float]:
"""Return the vram cache size using the legacy or modern setting."""
return self.max_vram_cache_size or self.vram
@property
def use_cpu(self) -> bool:
"""Return true if the device is set to CPU or the always_use_cpu flag is set."""
return self.always_use_cpu or self.device == "cpu"
@property
def disable_xformers(self) -> bool:
"""
Return true if enable_xformers is false (reversed logic)
and attention type is not set to xformers.
"""
"""Return true if enable_xformers is false (reversed logic) and attention type is not set to xformers."""
disabled_in_config = not self.xformers_enabled
return disabled_in_config and self.attention_type != "xformers"
@staticmethod
def find_root() -> Path:
"""
Choose the runtime root directory when not specified on command line or
init file.
"""
"""Choose the runtime root directory when not specified on command line or init file."""
return _find_root()
def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
"""
Legacy function which returns InvokeAIAppConfig.get_config()
"""
"""Legacy function which returns InvokeAIAppConfig.get_config()."""
return InvokeAIAppConfig.get_config(**kwargs)

9
invokeai/app/services/model_records/model_records_sql.py
View File

@ -48,7 +48,6 @@ from typing import List, Optional, Union
from invokeai.backend.model_manager.config import (
AnyModelConfig,
BaseModelType,
ModelConfigBase,
ModelConfigFactory,
ModelType,
)
@ -158,7 +157,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
("version", CONFIG_FILE_VERSION),
)
def add_model(self, key: str, config: Union[dict, ModelConfigBase]) -> AnyModelConfig:
def add_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
"""
Add a model to the database.
@ -255,7 +254,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
self._db.conn.rollback()
raise e
def update_model(self, key: str, config: ModelConfigBase) -> AnyModelConfig:
def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
"""
Update the model, returning the updated version.
@ -368,7 +367,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
return results
def search_by_path(self, path: Union[str, Path]) -> List[ModelConfigBase]:
def search_by_path(self, path: Union[str, Path]) -> List[AnyModelConfig]:
"""Return models with the indicated path."""
results = []
with self._db.lock:
@ -382,7 +381,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
return results
def search_by_hash(self, hash: str) -> List[ModelConfigBase]:
def search_by_hash(self, hash: str) -> List[AnyModelConfig]:
"""Return models with the indicated original_hash."""
results = []
with self._db.lock:

3
invokeai/app/services/session_processor/session_processor_default.py
View File

@ -1,7 +1,6 @@
import traceback
from threading import BoundedSemaphore
from threading import BoundedSemaphore, Thread
from threading import Event as ThreadEvent
from threading import Thread
from typing import Optional
from fastapi_events.handlers.local import local_handler

16
invokeai/app/services/shared/graph.py
View File

@ -49,7 +49,7 @@ class Edge(BaseModel):
def get_output_field(node: BaseInvocation, field: str) -> Any:
node_type = type(node)
node_outputs = get_type_hints(node_type.get_output_type())
node_outputs = get_type_hints(node_type.get_output_annotation())
node_output_field = node_outputs.get(field) or None
return node_output_field
@ -188,7 +188,7 @@ class GraphInvocationOutput(BaseInvocationOutput):
# TODO: Fill this out and move to invocations
@invocation("graph")
@invocation("graph", version="1.0.0")
class GraphInvocation(BaseInvocation):
"""Execute a graph"""
@ -205,7 +205,7 @@ class IterateInvocationOutput(BaseInvocationOutput):
"""Used to connect iteration outputs. Will be expanded to a specific output."""
item: Any = OutputField(
description="The item being iterated over", title="Collection Item", ui_type=UIType.CollectionItem
description="The item being iterated over", title="Collection Item", ui_type=UIType._CollectionItem
)
@ -215,7 +215,7 @@ class IterateInvocation(BaseInvocation):
"""Iterates over a list of items"""
collection: list[Any] = InputField(
description="The list of items to iterate over", default_factory=list, ui_type=UIType.Collection
description="The list of items to iterate over", default=[], ui_type=UIType._Collection
)
index: int = InputField(description="The index, will be provided on executed iterators", default=0, ui_hidden=True)
@ -227,7 +227,7 @@ class IterateInvocation(BaseInvocation):
@invocation_output("collect_output")
class CollectInvocationOutput(BaseInvocationOutput):
collection: list[Any] = OutputField(
description="The collection of input items", title="Collection", ui_type=UIType.Collection
description="The collection of input items", title="Collection", ui_type=UIType._Collection
)
@ -238,12 +238,12 @@ class CollectInvocation(BaseInvocation):
item: Optional[Any] = InputField(
default=None,
description="The item to collect (all inputs must be of the same type)",
ui_type=UIType.CollectionItem,
ui_type=UIType._CollectionItem,
title="Collection Item",
input=Input.Connection,
)
collection: list[Any] = InputField(
description="The collection, will be provided on execution", default_factory=list, ui_hidden=True
description="The collection, will be provided on execution", default=[], ui_hidden=True
)
def invoke(self, context: InvocationContext) -> CollectInvocationOutput:
@ -379,7 +379,7 @@ class Graph(BaseModel):
raise NodeNotFoundError(f"Edge destination node {edge.destination.node_id} does not exist in the graph")
# output fields are not on the node object directly, they are on the output type
if edge.source.field not in source_node.get_output_type().model_fields:
if edge.source.field not in source_node.get_output_annotation().model_fields:
raise NodeFieldNotFoundError(
f"Edge source field {edge.source.field} does not exist in node {edge.source.node_id}"
)

29
invokeai/backend/image_util/realesrgan/LICENSE Normal file
View File

@ -0,0 +1,29 @@
BSD 3-Clause License
Copyright (c) 2021, Xintao Wang
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

0
invokeai/backend/image_util/realesrgan/__init__.py Normal file
View File

274
invokeai/backend/image_util/realesrgan/realesrgan.py Normal file
View File

@ -0,0 +1,274 @@
import math
from enum import Enum
from pathlib import Path
from typing import Any, Optional
import cv2
import numpy as np
import numpy.typing as npt
import torch
from basicsr.archs.rrdbnet_arch import RRDBNet
from cv2.typing import MatLike
from tqdm import tqdm
from invokeai.backend.util.devices import choose_torch_device
"""
Adapted from https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
License is BSD3, copied to `LICENSE` in this directory.
The adaptation here has a few changes:
- Remove print statements, use `tqdm` to show progress
- Remove unused "outscale" logic, which simply scales the final image to a given factor
- Remove `dni_weight` logic, which was only used when multiple models were used
- Remove logic to fetch models from network
- Add types, rename a few things
"""
class ImageMode(str, Enum):
L = "L"
RGB = "RGB"
RGBA = "RGBA"
class RealESRGAN:
"""A helper class for upsampling images with RealESRGAN.
Args:
scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
model (nn.Module): The defined network. Default: None.
tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
input images into tiles, and then process each of them. Finally, they will be merged into one image.
0 denotes for do not use tile. Default: 0.
tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
half (float): Whether to use half precision during inference. Default: False.
"""
output: torch.Tensor
def __init__(
self,
scale: int,
model_path: Path,
model: RRDBNet,
tile: int = 0,
tile_pad: int = 10,
pre_pad: int = 10,
half: bool = False,
) -> None:
self.scale = scale
self.tile_size = tile
self.tile_pad = tile_pad
self.pre_pad = pre_pad
self.mod_scale: Optional[int] = None
self.half = half
self.device = choose_torch_device()
loadnet = torch.load(model_path, map_location=torch.device("cpu"))
# prefer to use params_ema
if "params_ema" in loadnet:
keyname = "params_ema"
else:
keyname = "params"
model.load_state_dict(loadnet[keyname], strict=True)
model.eval()
self.model = model.to(self.device)
if self.half:
self.model = self.model.half()
def pre_process(self, img: MatLike) -> None:
"""Pre-process, such as pre-pad and mod pad, so that the images can be divisible"""
img_tensor: torch.Tensor = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
self.img = img_tensor.unsqueeze(0).to(self.device)
if self.half:
self.img = self.img.half()
# pre_pad
if self.pre_pad != 0:
self.img = torch.nn.functional.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), "reflect")
# mod pad for divisible borders
if self.scale == 2:
self.mod_scale = 2
elif self.scale == 1:
self.mod_scale = 4
if self.mod_scale is not None:
self.mod_pad_h, self.mod_pad_w = 0, 0
_, _, h, w = self.img.size()
if h % self.mod_scale != 0:
self.mod_pad_h = self.mod_scale - h % self.mod_scale
if w % self.mod_scale != 0:
self.mod_pad_w = self.mod_scale - w % self.mod_scale
self.img = torch.nn.functional.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), "reflect")
def process(self) -> None:
# model inference
self.output = self.model(self.img)
def tile_process(self) -> None:
"""It will first crop input images to tiles, and then process each tile.
Finally, all the processed tiles are merged into one images.
Modified from: https://github.com/ata4/esrgan-launcher
"""
batch, channel, height, width = self.img.shape
output_height = height * self.scale
output_width = width * self.scale
output_shape = (batch, channel, output_height, output_width)
# start with black image
self.output = self.img.new_zeros(output_shape)
tiles_x = math.ceil(width / self.tile_size)
tiles_y = math.ceil(height / self.tile_size)
# loop over all tiles
total_steps = tiles_y * tiles_x
for i in tqdm(range(total_steps), desc="Upscaling"):
y = i // tiles_x
x = i % tiles_x
# extract tile from input image
ofs_x = x * self.tile_size
ofs_y = y * self.tile_size
# input tile area on total image
input_start_x = ofs_x
input_end_x = min(ofs_x + self.tile_size, width)
input_start_y = ofs_y
input_end_y = min(ofs_y + self.tile_size, height)
# input tile area on total image with padding
input_start_x_pad = max(input_start_x - self.tile_pad, 0)
input_end_x_pad = min(input_end_x + self.tile_pad, width)
input_start_y_pad = max(input_start_y - self.tile_pad, 0)
input_end_y_pad = min(input_end_y + self.tile_pad, height)
# input tile dimensions
input_tile_width = input_end_x - input_start_x
input_tile_height = input_end_y - input_start_y
input_tile = self.img[
:,
:,
input_start_y_pad:input_end_y_pad,
input_start_x_pad:input_end_x_pad,
]
# upscale tile
with torch.no_grad():
output_tile = self.model(input_tile)
# output tile area on total image
output_start_x = input_start_x * self.scale
output_end_x = input_end_x * self.scale
output_start_y = input_start_y * self.scale
output_end_y = input_end_y * self.scale
# output tile area without padding
output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
# put tile into output image
self.output[:, :, output_start_y:output_end_y, output_start_x:output_end_x] = output_tile[
:,
:,
output_start_y_tile:output_end_y_tile,
output_start_x_tile:output_end_x_tile,
]
def post_process(self) -> torch.Tensor:
# remove extra pad
if self.mod_scale is not None:
_, _, h, w = self.output.size()
self.output = self.output[
:,
:,
0 : h - self.mod_pad_h * self.scale,
0 : w - self.mod_pad_w * self.scale,
]
# remove prepad
if self.pre_pad != 0:
_, _, h, w = self.output.size()
self.output = self.output[
:,
:,
0 : h - self.pre_pad * self.scale,
0 : w - self.pre_pad * self.scale,
]
return self.output
@torch.no_grad()
def upscale(self, img: MatLike, esrgan_alpha_upscale: bool = True) -> npt.NDArray[Any]:
np_img = img.astype(np.float32)
alpha: Optional[np.ndarray] = None
if np.max(np_img) > 256:
# 16-bit image
max_range = 65535
else:
max_range = 255
np_img = np_img / max_range
if len(np_img.shape) == 2:
# grayscale image
img_mode = ImageMode.L
np_img = cv2.cvtColor(np_img, cv2.COLOR_GRAY2RGB)
elif np_img.shape[2] == 4:
# RGBA image with alpha channel
img_mode = ImageMode.RGBA
alpha = np_img[:, :, 3]
np_img = np_img[:, :, 0:3]
np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)
if esrgan_alpha_upscale:
alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
else:
img_mode = ImageMode.RGB
np_img = cv2.cvtColor(np_img, cv2.COLOR_BGR2RGB)
# ------------------- process image (without the alpha channel) ------------------- #
self.pre_process(np_img)
if self.tile_size > 0:
self.tile_process()
else:
self.process()
output_tensor = self.post_process()
output_img: npt.NDArray[Any] = output_tensor.data.squeeze().float().cpu().clamp_(0, 1).numpy()
output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
if img_mode is ImageMode.L:
output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
# ------------------- process the alpha channel if necessary ------------------- #
if img_mode is ImageMode.RGBA:
if esrgan_alpha_upscale:
assert alpha is not None
self.pre_process(alpha)
if self.tile_size > 0:
self.tile_process()
else:
self.process()
output_alpha_tensor = self.post_process()
output_alpha: npt.NDArray[Any] = output_alpha_tensor.data.squeeze().float().cpu().clamp_(0, 1).numpy()
output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
else: # use the cv2 resize for alpha channel
assert alpha is not None
h, w = alpha.shape[0:2]
output_alpha = cv2.resize(
alpha,
(w * self.scale, h * self.scale),
interpolation=cv2.INTER_LINEAR,
)
# merge the alpha channel
output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
output_img[:, :, 3] = output_alpha
# ------------------------------ return ------------------------------ #
if max_range == 65535: # 16-bit image
output = (output_img * 65535.0).round().astype(np.uint16)
else:
output = (output_img * 255.0).round().astype(np.uint8)
return output

11
invokeai/backend/model_management/__init__.py
View File

@ -1,12 +1,13 @@
# ruff: noqa: I001, F401
"""
Initialization file for invokeai.backend.model_management
"""
# This import must be first
from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType # noqa: F401 isort: split
from .model_manager import AddModelResult, ModelInfo, ModelManager, SchedulerPredictionType
from .lora import ModelPatcher, ONNXModelPatcher
from .model_cache import ModelCache
from .lora import ModelPatcher, ONNXModelPatcher # noqa: F401
from .model_cache import ModelCache # noqa: F401
from .models import ( # noqa: F401
from .models import (
BaseModelType,
DuplicateModelException,
ModelNotFoundException,
@ -16,4 +17,4 @@ from .models import ( # noqa: F401
)
# This import must be last
from .model_merge import ModelMerger, MergeInterpolationMethod # noqa: F401 isort: split
from .model_merge import MergeInterpolationMethod, ModelMerger

3
invokeai/backend/model_management/model_probe.py
View File

@ -53,6 +53,7 @@ class ModelProbe(object):
"StableDiffusionXLPipeline": ModelType.Main,
"StableDiffusionXLImg2ImgPipeline": ModelType.Main,
"StableDiffusionXLInpaintPipeline": ModelType.Main,
"LatentConsistencyModelPipeline": ModelType.Main,
"AutoencoderKL": ModelType.Vae,
"AutoencoderTiny": ModelType.Vae,
"ControlNetModel": ModelType.ControlNet,
@ -224,7 +225,7 @@ class ModelProbe(object):
with SilenceWarnings():
if model_path.suffix.endswith((".ckpt", ".pt", ".bin")):
cls._scan_model(model_path, model_path)
return torch.load(model_path)
return torch.load(model_path, map_location="cpu")
else:
return safetensors.torch.load_file(model_path)

101
invokeai/backend/util/logging.py
View File

@ -1,8 +1,7 @@
# Copyright (c) 2023 Lincoln D. Stein and The InvokeAI Development Team
"""invokeai.backend.util.logging
Logging class for InvokeAI that produces console messages
"""
Logging class for InvokeAI that produces console messages.
Usage:
@ -178,8 +177,8 @@ InvokeAI:
import logging.handlers
import socket
import urllib.parse
from abc import abstractmethod
from pathlib import Path
from typing import Any, Dict, Optional
from invokeai.app.services.config import InvokeAIAppConfig
@ -192,36 +191,36 @@ except ImportError:
# module level functions
def debug(msg, *args, **kwargs):
def debug(msg: str, *args: str, **kwargs: Any) -> None: # noqa D103
InvokeAILogger.get_logger().debug(msg, *args, **kwargs)
def info(msg, *args, **kwargs):
def info(msg: str, *args: str, **kwargs: Any) -> None: # noqa D103
InvokeAILogger.get_logger().info(msg, *args, **kwargs)
def warning(msg, *args, **kwargs):
def warning(msg: str, *args: str, **kwargs: Any) -> None: # noqa D103
InvokeAILogger.get_logger().warning(msg, *args, **kwargs)
def error(msg, *args, **kwargs):
def error(msg: str, *args: str, **kwargs: Any) -> None: # noqa D103
InvokeAILogger.get_logger().error(msg, *args, **kwargs)
def critical(msg, *args, **kwargs):
def critical(msg: str, *args: str, **kwargs: Any) -> None: # noqa D103
InvokeAILogger.get_logger().critical(msg, *args, **kwargs)
def log(level, msg, *args, **kwargs):
def log(level: int, msg: str, *args: str, **kwargs: Any) -> None: # noqa D103
InvokeAILogger.get_logger().log(level, msg, *args, **kwargs)
def disable(level=logging.CRITICAL):
InvokeAILogger.get_logger().disable(level)
def disable(level: int = logging.CRITICAL) -> None: # noqa D103
logging.disable(level)
def basicConfig(**kwargs):
InvokeAILogger.get_logger().basicConfig(**kwargs)
def basicConfig(**kwargs: Any) -> None: # noqa D103
logging.basicConfig(**kwargs)
_FACILITY_MAP = (
@ -256,33 +255,25 @@ _SOCK_MAP = {
class InvokeAIFormatter(logging.Formatter):
"""
Base class for logging formatter
"""Base class for logging formatter."""
"""
def format(self, record):
def format(self, record: logging.LogRecord) -> str: # noqa D102
formatter = logging.Formatter(self.log_fmt(record.levelno))
return formatter.format(record)
@abstractmethod
def log_fmt(self, levelno: int) -> str:
pass
def log_fmt(self, levelno: int) -> str: # noqa D102
return "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
class InvokeAISyslogFormatter(InvokeAIFormatter):
"""
Formatting for syslog
"""
"""Formatting for syslog."""
def log_fmt(self, levelno: int) -> str:
def log_fmt(self, levelno: int) -> str: # noqa D102
return "%(name)s [%(process)d] <%(levelname)s> %(message)s"
class InvokeAILegacyLogFormatter(InvokeAIFormatter):
"""
Formatting for the InvokeAI Logger (legacy version)
"""
class InvokeAILegacyLogFormatter(InvokeAIFormatter): # noqa D102
"""Formatting for the InvokeAI Logger (legacy version)."""
FORMATS = {
logging.DEBUG: " | %(message)s",
@ -292,23 +283,21 @@ class InvokeAILegacyLogFormatter(InvokeAIFormatter):
logging.CRITICAL: "### %(message)s",
}
def log_fmt(self, levelno: int) -> str:
return self.FORMATS.get(levelno)
def log_fmt(self, levelno: int) -> str: # noqa D102
format = self.FORMATS.get(levelno)
assert format is not None
return format
class InvokeAIPlainLogFormatter(InvokeAIFormatter):
"""
Custom Formatting for the InvokeAI Logger (plain version)
"""
"""Custom Formatting for the InvokeAI Logger (plain version)."""
def log_fmt(self, levelno: int) -> str:
def log_fmt(self, levelno: int) -> str: # noqa D102
return "[%(asctime)s]::[%(name)s]::%(levelname)s --> %(message)s"
class InvokeAIColorLogFormatter(InvokeAIFormatter):
"""
Custom Formatting for the InvokeAI Logger
"""
"""Custom Formatting for the InvokeAI Logger."""
# Color Codes
grey = "\x1b[38;20m"
@ -331,8 +320,10 @@ class InvokeAIColorLogFormatter(InvokeAIFormatter):
logging.CRITICAL: bold_red + log_format + reset,
}
def log_fmt(self, levelno: int) -> str:
return self.FORMATS.get(levelno)
def log_fmt(self, levelno: int) -> str: # noqa D102
format = self.FORMATS.get(levelno)
assert format is not None
return format
LOG_FORMATTERS = {
@ -343,13 +334,13 @@ LOG_FORMATTERS = {
}
class InvokeAILogger(object):
loggers = {}
class InvokeAILogger(object): # noqa D102
loggers: Dict[str, logging.Logger] = {}
@classmethod
def get_logger(
cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
) -> logging.Logger:
) -> logging.Logger: # noqa D102
if name in cls.loggers:
logger = cls.loggers[name]
logger.handlers.clear()
@ -362,7 +353,7 @@ class InvokeAILogger(object):
return cls.loggers[name]
@classmethod
def get_loggers(cls, config: InvokeAIAppConfig) -> list[logging.Handler]:
def get_loggers(cls, config: InvokeAIAppConfig) -> list[logging.Handler]: # noqa D102
handler_strs = config.log_handlers
handlers = []
for handler in handler_strs:
@ -374,7 +365,7 @@ class InvokeAILogger(object):
# http gets no custom formatter
formatter = LOG_FORMATTERS[config.log_format]
if handler_name == "console":
ch = logging.StreamHandler()
ch: logging.Handler = logging.StreamHandler()
ch.setFormatter(formatter())
handlers.append(ch)
@ -393,18 +384,18 @@ class InvokeAILogger(object):
return handlers
@staticmethod
def _parse_syslog_args(args: str = None) -> logging.Handler:
def _parse_syslog_args(args: Optional[str] = None) -> logging.Handler:
if not SYSLOG_AVAILABLE:
raise ValueError("syslog is not available on this system")
if not args:
args = "/dev/log" if Path("/dev/log").exists() else "address:localhost:514"
syslog_args = {}
syslog_args: Dict[str, Any] = {}
try:
for a in args.split(","):
arg_name, *arg_value = a.split(":", 2)
if arg_name == "address":
host, *port = arg_value
port = 514 if len(port) == 0 else int(port[0])
host, *port_list = arg_value
port = 514 if not port_list else int(port_list[0])
syslog_args["address"] = (host, port)
elif arg_name == "facility":
syslog_args["facility"] = _FACILITY_MAP[arg_value[0]]
@ -417,13 +408,13 @@ class InvokeAILogger(object):
return logging.handlers.SysLogHandler(**syslog_args)
@staticmethod
def _parse_file_args(args: str = None) -> logging.Handler:
def _parse_file_args(args: Optional[str] = None) -> logging.Handler: # noqa D102
if not args:
raise ValueError("please provide filename for file logging using format 'file=/path/to/logfile.txt'")
return logging.FileHandler(args)
@staticmethod
def _parse_http_args(args: str = None) -> logging.Handler:
def _parse_http_args(args: Optional[str] = None) -> logging.Handler: # noqa D102
if not args:
raise ValueError("please provide destination for http logging using format 'http=url'")
arg_list = args.split(",")
@ -434,12 +425,12 @@ class InvokeAILogger(object):
path = url.path
port = url.port or 80
syslog_args = {}
syslog_args: Dict[str, Any] = {}
for a in arg_list:
arg_name, *arg_value = a.split(":", 2)
if arg_name == "method":
arg_value = arg_value[0] if len(arg_value) > 0 else "GET"
syslog_args[arg_name] = arg_value
method = arg_value[0] if len(arg_value) > 0 else "GET"
syslog_args[arg_name] = method
else: # TODO: Provide support for SSL context and credentials
pass
return logging.handlers.HTTPHandler(f"{host}:{port}", path, **syslog_args)

10
invokeai/frontend/web/.eslintrc.js
View File

@ -20,10 +20,18 @@ module.exports = {
ecmaVersion: 2018,
sourceType: 'module',
},
plugins: ['react', '@typescript-eslint', 'eslint-plugin-react-hooks'],
plugins: [
'react',
'@typescript-eslint',
'eslint-plugin-react-hooks',
'i18next',
'path',
],
root: true,
rules: {
'path/no-relative-imports': ['error', { maxDepth: 0 }],
curly: 'error',
'i18next/no-literal-string': 2,
'react/jsx-no-bind': ['error', { allowBind: true }],
'react/jsx-curly-brace-presence': [
'error',

1
invokeai/frontend/web/.prettierignore
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@ -9,6 +9,5 @@ index.html
.yalc/
*.scss
src/services/api/schema.d.ts
docs/
static/
src/theme/css/overlayscrollbars.css

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280
invokeai/frontend/web/dist/assets/ThemeLocaleProvider-58d6b3b6.js vendored
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@ -1,280 +0,0 @@
import{w as s,ia as T,v as l,a2 as I,ib as R,ae as V,ic as z,id as j,ie as D,ig as F,ih as G,ii as W,ij as K,aG as H,ik as U,il as Y}from"./index-54a1ea80.js";import{M as Z}from"./MantineProvider-17a58e64.js";var P=String.raw,E=P`
:root,
:host {
--chakra-vh: 100vh;
}
@supports (height: -webkit-fill-available) {
:root,
:host {
--chakra-vh: -webkit-fill-available;
}
}
@supports (height: -moz-fill-available) {
:root,
:host {
--chakra-vh: -moz-fill-available;
}
}
@supports (height: 100dvh) {
:root,
:host {
--chakra-vh: 100dvh;
}
}
`,B=()=>s.jsx(T,{styles:E}),J=({scope:e=""})=>s.jsx(T,{styles:P`
html {
line-height: 1.5;
-webkit-text-size-adjust: 100%;
font-family: system-ui, sans-serif;
-webkit-font-smoothing: antialiased;
text-rendering: optimizeLegibility;
-moz-osx-font-smoothing: grayscale;
touch-action: manipulation;
}
body {
position: relative;
min-height: 100%;
margin: 0;
font-feature-settings: "kern";
}
${e} :where(*, *::before, *::after) {
border-width: 0;
border-style: solid;
box-sizing: border-box;
word-wrap: break-word;
}
main {
display: block;
}
${e} hr {
border-top-width: 1px;
box-sizing: content-box;
height: 0;
overflow: visible;
}
${e} :where(pre, code, kbd,samp) {
font-family: SFMono-Regular, Menlo, Monaco, Consolas, monospace;
font-size: 1em;
}
${e} a {
background-color: transparent;
color: inherit;
text-decoration: inherit;
}
${e} abbr[title] {
border-bottom: none;
text-decoration: underline;
-webkit-text-decoration: underline dotted;
text-decoration: underline dotted;
}
${e} :where(b, strong) {
font-weight: bold;
}
${e} small {
font-size: 80%;
}
${e} :where(sub,sup) {
font-size: 75%;
line-height: 0;
position: relative;
vertical-align: baseline;
}
${e} sub {
bottom: -0.25em;
}
${e} sup {
top: -0.5em;
}
${e} img {
border-style: none;
}
${e} :where(button, input, optgroup, select, textarea) {
font-family: inherit;
font-size: 100%;
line-height: 1.15;
margin: 0;
}
${e} :where(button, input) {
overflow: visible;
}
${e} :where(button, select) {
text-transform: none;
}
${e} :where(
button::-moz-focus-inner,
[type="button"]::-moz-focus-inner,
[type="reset"]::-moz-focus-inner,
[type="submit"]::-moz-focus-inner
) {
border-style: none;
padding: 0;
}
${e} fieldset {
padding: 0.35em 0.75em 0.625em;
}
${e} legend {
box-sizing: border-box;
color: inherit;
display: table;
max-width: 100%;
padding: 0;
white-space: normal;
}
${e} progress {
vertical-align: baseline;
}
${e} textarea {
overflow: auto;
}
${e} :where([type="checkbox"], [type="radio"]) {
box-sizing: border-box;
padding: 0;
}
${e} input[type="number"]::-webkit-inner-spin-button,
${e} input[type="number"]::-webkit-outer-spin-button {
-webkit-appearance: none !important;
}
${e} input[type="number"] {
-moz-appearance: textfield;
}
${e} input[type="search"] {
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@ -1,4 +1,4 @@
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@ -15,7 +15,7 @@
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@ -4,14 +4,14 @@
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"nodesDesc": "Ein knotenbasiertes System, für die Erzeugung von Bildern, ist derzeit in der Entwicklung. Bleiben Sie gespannt auf Updates zu dieser fantastischen Funktion.",
"postProcessing": "Nachbearbeitung",
"postProcessDesc1": "InvokeAI bietet eine breite Palette von Nachbearbeitungsfunktionen. Bildhochskalierung und Gesichtsrekonstruktion sind bereits in der WebUI verfügbar. Sie können sie über das Menü Erweiterte Optionen der Reiter Text in Bild und Bild in Bild aufrufen. Sie können Bilder auch direkt bearbeiten, indem Sie die Schaltflächen für Bildaktionen oberhalb der aktuellen Bildanzeige oder im Viewer verwenden.",
"postProcessDesc2": "Eine spezielle Benutzeroberfläche wird in Kürze veröffentlicht, um erweiterte Nachbearbeitungs-Workflows zu erleichtern.",
"postProcessDesc3": "Die InvokeAI Kommandozeilen-Schnittstelle bietet verschiedene andere Funktionen, darunter Embiggen.",
"training": "Training",
"training": "trainieren",
"trainingDesc1": "Ein spezieller Arbeitsablauf zum Trainieren Ihrer eigenen Embeddings und Checkpoints mit Textual Inversion und Dreambooth über die Weboberfläche.",
"trainingDesc2": "InvokeAI unterstützt bereits das Training von benutzerdefinierten Embeddings mit Textual Inversion unter Verwendung des Hauptskripts.",
"upload": "Hochladen",
@ -38,14 +38,14 @@
"statusUpscalingESRGAN": "Hochskalierung (ESRGAN)",
"statusLoadingModel": "Laden des Modells",
"statusModelChanged": "Modell Geändert",
"cancel": "Abbruch",
"cancel": "Abbrechen",
"accept": "Annehmen",
"back": "Zurück",
"langEnglish": "Englisch",
"langDutch": "Niederländisch",
"langFrench": "Französisch",
"langItalian": "Italienisch",
"langPortuguese": "Portogisisch",
"langPortuguese": "Portugiesisch",
"langRussian": "Russisch",
"langUkranian": "Ukrainisch",
"hotkeysLabel": "Tastenkombinationen",
@ -58,12 +58,44 @@
"langArabic": "Arabisch",
"langKorean": "Koreanisch",
"langHebrew": "Hebräisch",
"langSpanish": "Spanisch"
"langSpanish": "Spanisch",
"t2iAdapter": "T2I Adapter",
"communityLabel": "Gemeinschaft",
"dontAskMeAgain": "Frag mich nicht nochmal",
"loadingInvokeAI": "Lade Invoke AI",
"statusMergedModels": "Modelle zusammengeführt",
"areYouSure": "Bist du dir sicher?",
"statusConvertingModel": "Model konvertieren",
"on": "An",
"nodeEditor": "Knoten Editor",
"statusMergingModels": "Modelle zusammenführen",
"langSimplifiedChinese": "Vereinfachtes Chinesisch",
"ipAdapter": "IP Adapter",
"controlAdapter": "Control Adapter",
"auto": "Automatisch",
"controlNet": "ControlNet",
"imageFailedToLoad": "Kann Bild nicht laden",
"statusModelConverted": "Model konvertiert",
"modelManager": "Model Manager",
"lightMode": "Heller Modus",
"generate": "Erstellen",
"learnMore": "Mehr lernen",
"darkMode": "Dunkler Modus",
"loading": "Lade",
"random": "Zufall",
"batch": "Stapel-Manager",
"advanced": "Erweitert",
"langBrPortuguese": "Portugiesisch (Brasilien)",
"unifiedCanvas": "Einheitliche Leinwand",
"openInNewTab": "In einem neuem Tab öffnen",
"statusProcessing": "wird bearbeitet",
"linear": "Linear",
"imagePrompt": "Bild Prompt"
},
"gallery": {
"generations": "Erzeugungen",
"showGenerations": "Zeige Erzeugnisse",
"uploads": "Hochgelades",
"uploads": "Uploads",
"showUploads": "Zeige Uploads",
"galleryImageSize": "Bildgröße",
"galleryImageResetSize": "Größe zurücksetzen",
@ -73,7 +105,22 @@
"singleColumnLayout": "Einspaltiges Layout",
"allImagesLoaded": "Alle Bilder geladen",
"loadMore": "Mehr laden",
"noImagesInGallery": "Keine Bilder in der Galerie"
"noImagesInGallery": "Keine Bilder in der Galerie",
"loading": "Lade",
"preparingDownload": "bereite Download vor",
"preparingDownloadFailed": "Problem beim Download vorbereiten",
"deleteImage": "Lösche Bild",
"images": "Bilder",
"copy": "Kopieren",
"download": "Runterladen",
"setCurrentImage": "Setze aktuelle Bild",
"featuresWillReset": "Wenn Sie dieses Bild löschen, werden diese Funktionen sofort zurückgesetzt.",
"deleteImageBin": "Gelöschte Bilder werden an den Papierkorb Ihres Betriebssystems gesendet.",
"unableToLoad": "Galerie kann nicht geladen werden",
"downloadSelection": "Auswahl herunterladen",
"currentlyInUse": "Dieses Bild wird derzeit in den folgenden Funktionen verwendet:",
"deleteImagePermanent": "Gelöschte Bilder können nicht wiederhergestellt werden.",
"autoAssignBoardOnClick": "Board per Klick automatisch zuweisen"
},
"hotkeys": {
"keyboardShortcuts": "Tastenkürzel",
@ -82,7 +129,8 @@
"galleryHotkeys": "Galerie Tastenkürzel",
"unifiedCanvasHotkeys": "Unified Canvas Tastenkürzel",
"invoke": {
"desc": "Ein Bild erzeugen"
"desc": "Ein Bild erzeugen",
"title": "Invoke"
},
"cancel": {
"title": "Abbrechen",
@ -166,7 +214,7 @@
},
"toggleGalleryPin": {
"title": "Galerie anheften umschalten",
"desc": "Heftet die Galerie an die Benutzeroberfläche bzw. löst die sie."
"desc": "Heftet die Galerie an die Benutzeroberfläche bzw. löst die sie"
},
"increaseGalleryThumbSize": {
"title": "Größe der Galeriebilder erhöhen",
@ -279,6 +327,11 @@
"acceptStagingImage": {
"title": "Staging-Bild akzeptieren",
"desc": "Akzeptieren Sie das aktuelle Bild des Staging-Bereichs"
},
"nodesHotkeys": "Knoten Tastenkürzel",
"addNodes": {
"title": "Knotenpunkt hinzufügen",
"desc": "Öffnet das Menü zum Hinzufügen von Knoten"
}
},
"modelManager": {
@ -295,7 +348,7 @@
"config": "Konfiguration",
"configValidationMsg": "Pfad zur Konfigurationsdatei Ihres Models.",
"modelLocation": "Ort des Models",
"modelLocationValidationMsg": "Pfad zum Speicherort Ihres Models.",
"modelLocationValidationMsg": "Pfad zum Speicherort Ihres Models",
"vaeLocation": "VAE Ort",
"vaeLocationValidationMsg": "Pfad zum Speicherort Ihres VAE.",
"width": "Breite",
@ -328,11 +381,99 @@
"deleteModel": "Model löschen",
"deleteConfig": "Konfiguration löschen",
"deleteMsg1": "Möchten Sie diesen Model-Eintrag wirklich aus InvokeAI löschen?",
"deleteMsg2": "Dadurch wird die Modellprüfpunktdatei nicht von Ihrer Festplatte gelöscht. Sie können sie bei Bedarf erneut hinzufügen.",
"deleteMsg2": "Dadurch WIRD das Modell von der Festplatte gelöscht WENN es im InvokeAI Root Ordner liegt. Wenn es in einem anderem Ordner liegt wird das Modell NICHT von der Festplatte gelöscht.",
"customConfig": "Benutzerdefinierte Konfiguration",
"invokeRoot": "InvokeAI Ordner",
"formMessageDiffusersVAELocationDesc": "Falls nicht angegeben, sucht InvokeAI nach der VAE-Datei innerhalb des oben angegebenen Modell Speicherortes.",
"checkpointModels": "Kontrollpunkte"
"checkpointModels": "Kontrollpunkte",
"convert": "Umwandeln",
"addCheckpointModel": "Kontrollpunkt / SafeTensors Modell hinzufügen",
"allModels": "Alle Modelle",
"alpha": "Alpha",
"addDifference": "Unterschied hinzufügen",
"convertToDiffusersHelpText2": "Bei diesem Vorgang wird Ihr Eintrag im Modell-Manager durch die Diffusor-Version desselben Modells ersetzt.",
"convertToDiffusersHelpText5": "Bitte stellen Sie sicher, dass Sie über genügend Speicherplatz verfügen. Die Modelle sind in der Regel zwischen 2 GB und 7 GB groß.",
"convertToDiffusersHelpText3": "Ihre Kontrollpunktdatei auf der Festplatte wird NICHT gelöscht oder in irgendeiner Weise verändert. Sie können Ihren Kontrollpunkt dem Modell-Manager wieder hinzufügen, wenn Sie dies wünschen.",
"convertToDiffusersHelpText4": "Dies ist ein einmaliger Vorgang. Er kann je nach den Spezifikationen Ihres Computers etwa 30-60 Sekunden dauern.",
"convertToDiffusersHelpText6": "Möchten Sie dieses Modell konvertieren?",
"custom": "Benutzerdefiniert",
"modelConverted": "Modell umgewandelt",
"inverseSigmoid": "Inverses Sigmoid",
"invokeAIFolder": "Invoke AI Ordner",
"formMessageDiffusersModelLocationDesc": "Bitte geben Sie mindestens einen an.",
"customSaveLocation": "Benutzerdefinierter Speicherort",
"formMessageDiffusersVAELocation": "VAE Speicherort",
"mergedModelCustomSaveLocation": "Benutzerdefinierter Pfad",
"modelMergeHeaderHelp2": "Nur Diffusers sind für die Zusammenführung verfügbar. Wenn Sie ein Kontrollpunktmodell zusammenführen möchten, konvertieren Sie es bitte zuerst in Diffusers.",
"manual": "Manuell",
"modelManager": "Modell Manager",
"modelMergeAlphaHelp": "Alpha steuert die Überblendungsstärke für die Modelle. Niedrigere Alphawerte führen zu einem geringeren Einfluss des zweiten Modells.",
"modelMergeHeaderHelp1": "Sie können bis zu drei verschiedene Modelle miteinander kombinieren, um eine Mischung zu erstellen, die Ihren Bedürfnissen entspricht.",
"ignoreMismatch": "Unstimmigkeiten zwischen ausgewählten Modellen ignorieren",
"model": "Modell",
"convertToDiffusersSaveLocation": "Speicherort",
"pathToCustomConfig": "Pfad zur benutzerdefinierten Konfiguration",
"v1": "v1",
"modelMergeInterpAddDifferenceHelp": "In diesem Modus wird zunächst Modell 3 von Modell 2 subtrahiert. Die resultierende Version wird mit Modell 1 mit dem oben eingestellten Alphasatz gemischt.",
"modelTwo": "Modell 2",
"modelOne": "Modell 1",
"v2_base": "v2 (512px)",
"scanForModels": "Nach Modellen suchen",
"name": "Name",
"safetensorModels": "SafeTensors",
"pickModelType": "Modell Typ auswählen",
"sameFolder": "Gleicher Ordner",
"modelThree": "Modell 3",
"v2_768": "v2 (768px)",
"none": "Nix",
"repoIDValidationMsg": "Online Repo Ihres Modells",
"vaeRepoIDValidationMsg": "Online Repo Ihrer VAE",
"importModels": "Importiere Modelle",
"merge": "Zusammenführen",
"addDiffuserModel": "Diffusers hinzufügen",
"advanced": "Erweitert",
"closeAdvanced": "Schließe Erweitert",
"convertingModelBegin": "Konvertiere Modell. Bitte warten.",
"customConfigFileLocation": "Benutzerdefinierte Konfiguration Datei Speicherort",
"baseModel": "Basis Modell",
"convertToDiffusers": "Konvertiere zu Diffusers",
"diffusersModels": "Diffusers",
"noCustomLocationProvided": "Kein benutzerdefinierter Standort angegeben",
"onnxModels": "Onnx",
"vaeRepoID": "VAE-Repo-ID",
"weightedSum": "Gewichtete Summe",
"syncModelsDesc": "Wenn Ihre Modelle nicht mit dem Backend synchronisiert sind, können Sie sie mit dieser Option aktualisieren. Dies ist im Allgemeinen praktisch, wenn Sie Ihre models.yaml-Datei manuell aktualisieren oder Modelle zum InvokeAI-Stammordner hinzufügen, nachdem die Anwendung gestartet wurde.",
"vae": "VAE",
"noModels": "Keine Modelle gefunden",
"statusConverting": "Konvertieren",
"sigmoid": "Sigmoid",
"predictionType": "Vorhersagetyp (für Stable Diffusion 2.x-Modelle und gelegentliche Stable Diffusion 1.x-Modelle)",
"selectModel": "Wählen Sie Modell aus",
"repo_id": "Repo-ID",
"modelSyncFailed": "Modellsynchronisierung fehlgeschlagen",
"quickAdd": "Schnell hinzufügen",
"simpleModelDesc": "Geben Sie einen Pfad zu einem lokalen Diffusers-Modell, einem lokalen Checkpoint-/Safetensors-Modell, einer HuggingFace-Repo-ID oder einer Checkpoint-/Diffusers-Modell-URL an.",
"modelDeleted": "Modell gelöscht",
"inpainting": "v1 Ausmalen",
"modelUpdateFailed": "Modellaktualisierung fehlgeschlagen",
"useCustomConfig": "Benutzerdefinierte Konfiguration verwenden",
"settings": "Einstellungen",
"modelConversionFailed": "Modellkonvertierung fehlgeschlagen",
"syncModels": "Modelle synchronisieren",
"mergedModelSaveLocation": "Speicherort",
"modelType": "Modelltyp",
"modelsMerged": "Modelle zusammengeführt",
"modelsMergeFailed": "Modellzusammenführung fehlgeschlagen",
"convertToDiffusersHelpText1": "Dieses Modell wird in das 🧨 Diffusers-Format konvertiert.",
"modelsSynced": "Modelle synchronisiert",
"vaePrecision": "VAE-Präzision",
"mergeModels": "Modelle zusammenführen",
"interpolationType": "Interpolationstyp",
"oliveModels": "Olives",
"variant": "Variante",
"loraModels": "LoRAs",
"modelDeleteFailed": "Modell konnte nicht gelöscht werden",
"mergedModelName": "Zusammengeführter Modellname"
},
"parameters": {
"images": "Bilder",
@ -352,7 +493,7 @@
"type": "Art",
"strength": "Stärke",
"upscaling": "Hochskalierung",
"upscale": "Hochskalieren",
"upscale": "Hochskalieren (Shift + U)",
"upscaleImage": "Bild hochskalieren",
"scale": "Maßstab",
"otherOptions": "Andere Optionen",
@ -369,7 +510,7 @@
"seamCorrectionHeader": "Nahtkorrektur",
"infillScalingHeader": "Infill und Skalierung",
"img2imgStrength": "Bild-zu-Bild-Stärke",
"toggleLoopback": "Toggle Loopback",
"toggleLoopback": "Loopback umschalten",
"sendTo": "Senden an",
"sendToImg2Img": "Senden an Bild zu Bild",
"sendToUnifiedCanvas": "Senden an Unified Canvas",
@ -384,8 +525,20 @@
"initialImage": "Ursprüngliches Bild",
"showOptionsPanel": "Optionsleiste zeigen",
"cancel": {
"setType": "Abbruchart festlegen"
}
"setType": "Abbruchart festlegen",
"immediate": "Sofort abbrechen",
"schedule": "Abbrechen nach der aktuellen Iteration",
"isScheduled": "Abbrechen"
},
"copyImage": "Bild kopieren",
"denoisingStrength": "Stärke der Entrauschung",
"symmetry": "Symmetrie",
"imageToImage": "Bild zu Bild",
"info": "Information",
"general": "Allgemein",
"hiresStrength": "High Res Stärke",
"hidePreview": "Verstecke Vorschau",
"showPreview": "Zeige Vorschau"
},
"settings": {
"displayInProgress": "Bilder in Bearbeitung anzeigen",
@ -396,7 +549,9 @@
"resetWebUI": "Web-Oberfläche zurücksetzen",
"resetWebUIDesc1": "Das Zurücksetzen der Web-Oberfläche setzt nur den lokalen Cache des Browsers mit Ihren Bildern und gespeicherten Einstellungen zurück. Es werden keine Bilder von der Festplatte gelöscht.",
"resetWebUIDesc2": "Wenn die Bilder nicht in der Galerie angezeigt werden oder etwas anderes nicht funktioniert, versuchen Sie bitte, die Einstellungen zurückzusetzen, bevor Sie einen Fehler auf GitHub melden.",
"resetComplete": "Die Web-Oberfläche wurde zurückgesetzt. Aktualisieren Sie die Seite, um sie neu zu laden."
"resetComplete": "Die Web-Oberfläche wurde zurückgesetzt.",
"models": "Modelle",
"useSlidersForAll": "Schieberegler für alle Optionen verwenden"
},
"toast": {
"tempFoldersEmptied": "Temp-Ordner geleert",
@ -406,7 +561,7 @@
"imageCopied": "Bild kopiert",
"imageLinkCopied": "Bildlink kopiert",
"imageNotLoaded": "Kein Bild geladen",
"imageNotLoadedDesc": "Kein Bild gefunden, das an das Bild zu Bild-Modul gesendet werden kann",
"imageNotLoadedDesc": "Konnte kein Bild finden",
"imageSavedToGallery": "Bild in die Galerie gespeichert",
"canvasMerged": "Leinwand zusammengeführt",
"sentToImageToImage": "Gesendet an Bild zu Bild",
@ -476,7 +631,7 @@
"autoSaveToGallery": "Automatisch in Galerie speichern",
"saveBoxRegionOnly": "Nur Auswahlbox speichern",
"limitStrokesToBox": "Striche auf Box beschränken",
"showCanvasDebugInfo": "Leinwand-Debug-Infos anzeigen",
"showCanvasDebugInfo": "Zusätzliche Informationen zur Leinwand anzeigen",
"clearCanvasHistory": "Leinwand-Verlauf löschen",
"clearHistory": "Verlauf löschen",
"clearCanvasHistoryMessage": "Wenn Sie den Verlauf der Leinwand löschen, bleibt die aktuelle Leinwand intakt, aber der Verlauf der Rückgängig- und Wiederherstellung wird unwiderruflich gelöscht.",
@ -501,14 +656,17 @@
"betaClear": "Löschen",
"betaDarkenOutside": "Außen abdunkeln",
"betaLimitToBox": "Begrenzung auf das Feld",
"betaPreserveMasked": "Maskiertes bewahren"
"betaPreserveMasked": "Maskiertes bewahren",
"antialiasing": "Kantenglättung",
"showResultsOn": "Zeige Ergebnisse (An)",
"showResultsOff": "Zeige Ergebnisse (Aus)"
},
"accessibility": {
"modelSelect": "Model Auswahl",
"uploadImage": "Bild hochladen",
"previousImage": "Voriges Bild",
"useThisParameter": "Benutze diesen Parameter",
"copyMetadataJson": "Kopiere metadata JSON",
"copyMetadataJson": "Kopiere Metadaten JSON",
"zoomIn": "Vergrößern",
"rotateClockwise": "Im Uhrzeigersinn drehen",
"flipHorizontally": "Horizontal drehen",
@ -517,9 +675,299 @@
"toggleAutoscroll": "Auroscroll ein/ausschalten",
"toggleLogViewer": "Log Betrachter ein/ausschalten",
"showOptionsPanel": "Zeige Optionen",
"reset": "Zurücksetzen",
"reset": "Zurücksetzten",
"nextImage": "Nächstes Bild",
"zoomOut": "Verkleinern",
"rotateCounterClockwise": "Gegen den Uhrzeigersinn verdrehen"
"rotateCounterClockwise": "Gegen den Uhrzeigersinn verdrehen",
"showGalleryPanel": "Galeriefenster anzeigen",
"exitViewer": "Betrachten beenden",
"menu": "Menü",
"loadMore": "Mehr laden",
"invokeProgressBar": "Invoke Fortschrittsanzeige"
},
"boards": {
"autoAddBoard": "Automatisches Hinzufügen zum Ordner",
"topMessage": "Dieser Ordner enthält Bilder die in den folgenden Funktionen verwendet werden:",
"move": "Bewegen",
"menuItemAutoAdd": "Automatisches Hinzufügen zu diesem Ordner",
"myBoard": "Meine Ordner",
"searchBoard": "Ordner durchsuchen...",
"noMatching": "Keine passenden Ordner",
"selectBoard": "Ordner aussuchen",
"cancel": "Abbrechen",
"addBoard": "Ordner hinzufügen",
"uncategorized": "Nicht kategorisiert",
"downloadBoard": "Ordner runterladen",
"changeBoard": "Ordner wechseln",
"loading": "Laden...",
"clearSearch": "Suche leeren",
"bottomMessage": "Durch das Löschen dieses Ordners und seiner Bilder werden alle Funktionen zurückgesetzt, die sie derzeit verwenden."
},
"controlnet": {
"showAdvanced": "Zeige Erweitert",
"contentShuffleDescription": "Mischt den Inhalt von einem Bild",
"addT2IAdapter": "$t(common.t2iAdapter) hinzufügen",
"importImageFromCanvas": "Importieren Bild von Zeichenfläche",
"lineartDescription": "Konvertiere Bild zu Lineart",
"importMaskFromCanvas": "Importiere Maske von Zeichenfläche",
"hed": "HED",
"hideAdvanced": "Verstecke Erweitert",
"contentShuffle": "Inhalt mischen",
"controlNetEnabledT2IDisabled": "$t(common.controlNet) ist aktiv, $t(common.t2iAdapter) ist deaktiviert",
"ipAdapterModel": "Adapter Modell",
"beginEndStepPercent": "Start / Ende Step Prozent",
"duplicate": "Kopieren",
"f": "F",
"h": "H",
"depthMidasDescription": "Tiefenmap erstellen mit Midas",
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
"t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ist aktiv, $t(common.controlNet) ist deaktiviert",
"weight": "Breite",
"selectModel": "Wähle ein Modell",
"depthMidas": "Tiefe (Midas)",
"w": "W",
"addControlNet": "$t(common.controlNet) hinzufügen",
"none": "Kein",
"incompatibleBaseModel": "Inkompatibles Basismodell:",
"enableControlnet": "Aktiviere ControlNet",
"detectResolution": "Auflösung erkennen",
"controlNetT2IMutexDesc": "$t(common.controlNet) und $t(common.t2iAdapter) zur gleichen Zeit wird nicht unterstützt.",
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
"fill": "Füllen",
"addIPAdapter": "$t(common.ipAdapter) hinzufügen",
"colorMapDescription": "Erstelle eine Farbkarte von diesem Bild",
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
"imageResolution": "Bild Auflösung",
"depthZoe": "Tiefe (Zoe)",
"colorMap": "Farbe",
"lowThreshold": "Niedrige Schwelle",
"highThreshold": "Hohe Schwelle",
"toggleControlNet": "Schalten ControlNet um",
"delete": "Löschen",
"controlAdapter_one": "Control Adapter",
"controlAdapter_other": "Control Adapters",
"colorMapTileSize": "Tile Größe",
"depthZoeDescription": "Tiefenmap erstellen mit Zoe",
"setControlImageDimensions": "Setze Control Bild Auflösung auf Breite/Höhe",
"handAndFace": "Hand und Gesicht",
"enableIPAdapter": "Aktiviere IP Adapter",
"resize": "Größe ändern",
"resetControlImage": "Zurücksetzen vom Referenz Bild",
"balanced": "Ausgewogen",
"prompt": "Prompt",
"resizeMode": "Größenänderungsmodus",
"processor": "Prozessor",
"saveControlImage": "Speichere Referenz Bild",
"safe": "Speichern",
"ipAdapterImageFallback": "Kein IP Adapter Bild ausgewählt",
"resetIPAdapterImage": "Zurücksetzen vom IP Adapter Bild",
"pidi": "PIDI",
"normalBae": "Normales BAE",
"mlsdDescription": "Minimalistischer Liniensegmentdetektor",
"openPoseDescription": "Schätzung der menschlichen Pose mit Openpose",
"control": "Kontrolle",
"coarse": "Coarse",
"crop": "Zuschneiden",
"pidiDescription": "PIDI-Bildverarbeitung",
"mediapipeFace": "Mediapipe Gesichter",
"mlsd": "M-LSD",
"controlMode": "Steuermodus",
"cannyDescription": "Canny Ecken Erkennung",
"lineart": "Lineart",
"lineartAnimeDescription": "Lineart-Verarbeitung im Anime-Stil",
"minConfidence": "Minimales Vertrauen",
"megaControl": "Mega-Kontrolle",
"autoConfigure": "Prozessor automatisch konfigurieren",
"normalBaeDescription": "Normale BAE-Verarbeitung",
"noneDescription": "Es wurde keine Verarbeitung angewendet",
"openPose": "Openpose",
"lineartAnime": "Lineart Anime",
"mediapipeFaceDescription": "Gesichtserkennung mit Mediapipe",
"canny": "Canny",
"hedDescription": "Ganzheitlich verschachtelte Kantenerkennung",
"scribble": "Scribble",
"maxFaces": "Maximal Anzahl Gesichter"
},
"queue": {
"status": "Status",
"cancelTooltip": "Aktuellen Aufgabe abbrechen",
"queueEmpty": "Warteschlange leer",
"in_progress": "In Arbeit",
"queueFront": "An den Anfang der Warteschlange tun",
"completed": "Fertig",
"queueBack": "In die Warteschlange",
"clearFailed": "Probleme beim leeren der Warteschlange",
"clearSucceeded": "Warteschlange geleert",
"pause": "Pause",
"cancelSucceeded": "Auftrag abgebrochen",
"queue": "Warteschlange",
"batch": "Stapel",
"pending": "Ausstehend",
"clear": "Leeren",
"prune": "Leeren",
"total": "Gesamt",
"canceled": "Abgebrochen",
"clearTooltip": "Abbrechen und alle Aufträge leeren",
"current": "Aktuell",
"failed": "Fehler",
"cancelItem": "Abbruch Auftrag",
"next": "Nächste",
"cancel": "Abbruch",
"session": "Sitzung",
"queueTotal": "{{total}} Gesamt",
"resume": "Wieder aufnehmen",
"item": "Auftrag",
"notReady": "Warteschlange noch nicht bereit",
"batchValues": "Stapel Werte",
"queueCountPrediction": "{{predicted}} zur Warteschlange hinzufügen",
"queuedCount": "{{pending}} wartenden Elemente",
"clearQueueAlertDialog": "Die Warteschlange leeren, stoppt den aktuellen Prozess und leert die Warteschlange komplett.",
"completedIn": "Fertig in",
"cancelBatchSucceeded": "Stapel abgebrochen",
"cancelBatch": "Stapel stoppen",
"enqueueing": "Stapel in der Warteschlange",
"queueMaxExceeded": "Maximum von {{max_queue_size}} Elementen erreicht, würde {{skip}} Elemente überspringen",
"cancelBatchFailed": "Problem beim Abbruch vom Stapel",
"clearQueueAlertDialog2": "bist du sicher die Warteschlange zu leeren?",
"pruneSucceeded": "{{item_count}} abgeschlossene Elemente aus der Warteschlange entfernt",
"pauseSucceeded": "Prozessor angehalten",
"cancelFailed": "Problem beim Stornieren des Auftrags",
"pauseFailed": "Problem beim Anhalten des Prozessors",
"front": "Vorne",
"pruneTooltip": "Bereinigen Sie {{item_count}} abgeschlossene Aufträge",
"resumeFailed": "Problem beim wieder aufnehmen von Prozessor",
"pruneFailed": "Problem beim leeren der Warteschlange",
"pauseTooltip": "Pause von Prozessor",
"back": "Hinten",
"resumeSucceeded": "Prozessor wieder aufgenommen",
"resumeTooltip": "Prozessor wieder aufnehmen"
},
"metadata": {
"negativePrompt": "Negativ Beschreibung",
"metadata": "Meta-Data",
"strength": "Bild zu Bild stärke",
"imageDetails": "Bild Details",
"model": "Modell",
"noImageDetails": "Keine Bild Details gefunden",
"cfgScale": "CFG-Skala",
"fit": "Bild zu Bild passen",
"height": "Höhe",
"noMetaData": "Keine Meta-Data gefunden",
"width": "Breite",
"createdBy": "Erstellt von",
"steps": "Schritte",
"seamless": "Nahtlos",
"positivePrompt": "Positiver Prompt",
"generationMode": "Generierungsmodus",
"Threshold": "Noise Schwelle",
"seed": "Samen",
"perlin": "Perlin Noise",
"hiresFix": "Optimierung für hohe Auflösungen",
"initImage": "Erstes Bild",
"variations": "Samengewichtspaare",
"vae": "VAE",
"workflow": "Arbeitsablauf",
"scheduler": "Scheduler",
"noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden"
},
"popovers": {
"noiseUseCPU": {
"heading": "Nutze Prozessor rauschen"
},
"paramModel": {
"heading": "Modell"
},
"paramIterations": {
"heading": "Iterationen"
},
"paramCFGScale": {
"heading": "CFG-Skala"
},
"paramSteps": {
"heading": "Schritte"
},
"lora": {
"heading": "LoRA Gewichte"
},
"infillMethod": {
"heading": "Füllmethode"
},
"paramVAE": {
"heading": "VAE"
}
},
"ui": {
"lockRatio": "Verhältnis sperren",
"hideProgressImages": "Verstecke Prozess Bild",
"showProgressImages": "Zeige Prozess Bild"
},
"invocationCache": {
"disable": "Deaktivieren",
"misses": "Cache Nötig",
"hits": "Cache Treffer",
"enable": "Aktivieren",
"clear": "Leeren",
"maxCacheSize": "Maximale Cache Größe",
"cacheSize": "Cache Größe"
},
"embedding": {
"noMatchingEmbedding": "Keine passenden Embeddings",
"addEmbedding": "Embedding hinzufügen",
"incompatibleModel": "Inkompatibles Basismodell:"
},
"nodes": {
"booleanPolymorphicDescription": "Eine Sammlung boolescher Werte.",
"colorFieldDescription": "Eine RGBA-Farbe.",
"conditioningCollection": "Konditionierungssammlung",
"addNode": "Knoten hinzufügen",
"conditioningCollectionDescription": "Konditionierung kann zwischen Knoten weitergegeben werden.",
"colorPolymorphic": "Farbpolymorph",
"colorCodeEdgesHelp": "Farbkodieren Sie Kanten entsprechend ihren verbundenen Feldern",
"animatedEdges": "Animierte Kanten",
"booleanCollectionDescription": "Eine Sammlung boolescher Werte.",
"colorField": "Farbe",
"collectionItem": "Objekt in Sammlung",
"animatedEdgesHelp": "Animieren Sie ausgewählte Kanten und Kanten, die mit ausgewählten Knoten verbunden sind",
"cannotDuplicateConnection": "Es können keine doppelten Verbindungen erstellt werden",
"booleanPolymorphic": "Boolesche Polymorphie",
"colorPolymorphicDescription": "Eine Sammlung von Farben.",
"clipFieldDescription": "Tokenizer- und text_encoder-Untermodelle.",
"clipField": "Clip",
"colorCollection": "Eine Sammlung von Farben.",
"boolean": "Boolesche Werte",
"currentImage": "Aktuelles Bild",
"booleanDescription": "Boolesche Werte sind wahr oder falsch.",
"collection": "Sammlung",
"cannotConnectInputToInput": "Eingang kann nicht mit Eingang verbunden werden",
"conditioningField": "Konditionierung",
"cannotConnectOutputToOutput": "Ausgang kann nicht mit Ausgang verbunden werden",
"booleanCollection": "Boolesche Werte Sammlung",
"cannotConnectToSelf": "Es kann keine Verbindung zu sich selbst hergestellt werden",
"colorCodeEdges": "Farbkodierte Kanten",
"addNodeToolTip": "Knoten hinzufügen (Umschalt+A, Leertaste)"
},
"hrf": {
"enableHrf": "Aktivieren Sie die Korrektur für hohe Auflösungen",
"upscaleMethod": "Vergrößerungsmethoden",
"enableHrfTooltip": "Generieren Sie mit einer niedrigeren Anfangsauflösung, skalieren Sie auf die Basisauflösung hoch und führen Sie dann Image-to-Image aus.",
"metadata": {
"strength": "Hochauflösender Fix Stärke",
"enabled": "Hochauflösender Fix aktiviert",
"method": "Hochauflösender Fix Methode"
},
"hrf": "Hochauflösender Fix",
"hrfStrength": "Hochauflösende Fix Stärke",
"strengthTooltip": "Niedrigere Werte führen zu weniger Details, wodurch potenzielle Artefakte reduziert werden können."
},
"models": {
"noMatchingModels": "Keine passenden Modelle",
"loading": "lade",
"noMatchingLoRAs": "Keine passenden LoRAs",
"noLoRAsAvailable": "Keine LoRAs verfügbar",
"noModelsAvailable": "Keine Modelle verfügbar",
"selectModel": "Wählen ein Modell aus",
"noRefinerModelsInstalled": "Keine SDXL Refiner-Modelle installiert",
"noLoRAsInstalled": "Keine LoRAs installiert",
"selectLoRA": "Wählen ein LoRA aus"
}
}

86
invokeai/frontend/web/dist/locales/en.json vendored
View File

@ -6,6 +6,7 @@
"flipVertically": "Flip Vertically",
"invokeProgressBar": "Invoke progress bar",
"menu": "Menu",
"mode": "Mode",
"modelSelect": "Model Select",
"modifyConfig": "Modify Config",
"nextImage": "Next Image",
@ -30,6 +31,10 @@
"cancel": "Cancel",
"changeBoard": "Change Board",
"clearSearch": "Clear Search",
"deleteBoard": "Delete Board",
"deleteBoardAndImages": "Delete Board and Images",
"deleteBoardOnly": "Delete Board Only",
"deletedBoardsCannotbeRestored": "Deleted boards cannot be restored",
"loading": "Loading...",
"menuItemAutoAdd": "Auto-add to this Board",
"move": "Move",
@ -51,9 +56,12 @@
"cancel": "Cancel",
"close": "Close",
"on": "On",
"checkpoint": "Checkpoint",
"communityLabel": "Community",
"controlNet": "ControlNet",
"controlAdapter": "Control Adapter",
"data": "Data",
"details": "Details",
"ipAdapter": "IP Adapter",
"t2iAdapter": "T2I Adapter",
"darkMode": "Dark Mode",
@ -65,13 +73,14 @@
"imagePrompt": "Image Prompt",
"imageFailedToLoad": "Unable to Load Image",
"img2img": "Image To Image",
"inpaint": "inpaint",
"langArabic": "العربية",
"langBrPortuguese": "Português do Brasil",
"langDutch": "Nederlands",
"langEnglish": "English",
"langFrench": "Français",
"langGerman": "Deutsch",
"langHebrew": "עברית",
"langGerman": "German",
"langHebrew": "Hebrew",
"langItalian": "Italiano",
"langJapanese": "日本語",
"langKorean": "한국어",
@ -93,6 +102,8 @@
"nodes": "Workflow Editor",
"nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
"openInNewTab": "Open in New Tab",
"outpaint": "outpaint",
"outputs": "Outputs",
"postProcessDesc1": "Invoke AI offers a wide variety of post processing features. Image Upscaling and Face Restoration are already available in the WebUI. You can access them from the Advanced Options menu of the Text To Image and Image To Image tabs. You can also process images directly, using the image action buttons above the current image display or in the viewer.",
"postProcessDesc2": "A dedicated UI will be released soon to facilitate more advanced post processing workflows.",
"postProcessDesc3": "The Invoke AI Command Line Interface offers various other features including Embiggen.",
@ -100,7 +111,9 @@
"postProcessing": "Post Processing",
"random": "Random",
"reportBugLabel": "Report Bug",
"safetensors": "Safetensors",
"settingsLabel": "Settings",
"simple": "Simple",
"statusConnected": "Connected",
"statusConvertingModel": "Converting Model",
"statusDisconnected": "Disconnected",
@ -127,6 +140,7 @@
"statusSavingImage": "Saving Image",
"statusUpscaling": "Upscaling",
"statusUpscalingESRGAN": "Upscaling (ESRGAN)",
"template": "Template",
"training": "Training",
"trainingDesc1": "A dedicated workflow for training your own embeddings and checkpoints using Textual Inversion and Dreambooth from the web interface.",
"trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.",
@ -137,9 +151,9 @@
"controlnet": {
"controlAdapter_one": "Control Adapter",
"controlAdapter_other": "Control Adapters",
"controlnet": "$t(controlnet.controlAdapter) #{{number}} ($t(common.controlNet))",
"ip_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.ipAdapter))",
"t2i_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.t2iAdapter))",
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
"addControlNet": "Add $t(common.controlNet)",
"addIPAdapter": "Add $t(common.ipAdapter)",
"addT2IAdapter": "Add $t(common.t2iAdapter)",
@ -214,6 +228,7 @@
"setControlImageDimensions": "Set Control Image Dimensions To W/H",
"showAdvanced": "Show Advanced",
"toggleControlNet": "Toggle this ControlNet",
"unstarImage": "Unstar Image",
"w": "W",
"weight": "Weight",
"enableIPAdapter": "Enable IP Adapter",
@ -221,6 +236,19 @@
"resetIPAdapterImage": "Reset IP Adapter Image",
"ipAdapterImageFallback": "No IP Adapter Image Selected"
},
"hrf": {
"hrf": "High Resolution Fix",
"enableHrf": "Enable High Resolution Fix",
"enableHrfTooltip": "Generate with a lower initial resolution, upscale to the base resolution, then run Image-to-Image.",
"upscaleMethod": "Upscale Method",
"hrfStrength": "High Resolution Fix Strength",
"strengthTooltip": "Lower values result in fewer details, which may reduce potential artifacts.",
"metadata": {
"enabled": "High Resolution Fix Enabled",
"strength": "High Resolution Fix Strength",
"method": "High Resolution Fix Method"
}
},
"embedding": {
"addEmbedding": "Add Embedding",
"incompatibleModel": "Incompatible base model:",
@ -266,6 +294,7 @@
"next": "Next",
"status": "Status",
"total": "Total",
"time": "Time",
"pending": "Pending",
"in_progress": "In Progress",
"completed": "Completed",
@ -273,6 +302,7 @@
"canceled": "Canceled",
"completedIn": "Completed in",
"batch": "Batch",
"batchFieldValues": "Batch Field Values",
"item": "Item",
"session": "Session",
"batchValues": "Batch Values",
@ -322,6 +352,7 @@
"loading": "Loading",
"loadMore": "Load More",
"maintainAspectRatio": "Maintain Aspect Ratio",
"noImageSelected": "No Image Selected",
"noImagesInGallery": "No Images to Display",
"setCurrentImage": "Set as Current Image",
"showGenerations": "Show Generations",
@ -559,8 +590,10 @@
"negativePrompt": "Negative Prompt",
"noImageDetails": "No image details found",
"noMetaData": "No metadata found",
"noRecallParameters": "No parameters to recall found",
"perlin": "Perlin Noise",
"positivePrompt": "Positive Prompt",
"recallParameters": "Recall Parameters",
"scheduler": "Scheduler",
"seamless": "Seamless",
"seed": "Seed",
@ -568,6 +601,7 @@
"strength": "Image to image strength",
"Threshold": "Noise Threshold",
"variations": "Seed-weight pairs",
"vae": "VAE",
"width": "Width",
"workflow": "Workflow"
},
@ -590,6 +624,7 @@
"cannotUseSpaces": "Cannot Use Spaces",
"checkpointFolder": "Checkpoint Folder",
"checkpointModels": "Checkpoints",
"checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
"clearCheckpointFolder": "Clear Checkpoint Folder",
"closeAdvanced": "Close Advanced",
"config": "Config",
@ -669,6 +704,7 @@
"nameValidationMsg": "Enter a name for your model",
"noCustomLocationProvided": "No Custom Location Provided",
"noModels": "No Models Found",
"noModelSelected": "No Model Selected",
"noModelsFound": "No Models Found",
"none": "none",
"notLoaded": "not loaded",
@ -714,13 +750,17 @@
"widthValidationMsg": "Default width of your model."
},
"models": {
"addLora": "Add LoRA",
"esrganModel": "ESRGAN Model",
"loading": "loading",
"noLoRAsAvailable": "No LoRAs available",
"noMatchingLoRAs": "No matching LoRAs",
"noMatchingModels": "No matching Models",
"noModelsAvailable": "No models available",
"selectLoRA": "Select a LoRA",
"selectModel": "Select a Model"
"selectModel": "Select a Model",
"noLoRAsInstalled": "No LoRAs installed",
"noRefinerModelsInstalled": "No SDXL Refiner models installed"
},
"nodes": {
"addNode": "Add Node",
@ -902,7 +942,10 @@
"unknownTemplate": "Unknown Template",
"unkownInvocation": "Unknown Invocation type",
"updateNode": "Update Node",
"updateAllNodes": "Update All Nodes",
"updateApp": "Update App",
"unableToUpdateNodes_one": "Unable to update {{count}} node",
"unableToUpdateNodes_other": "Unable to update {{count}} nodes",
"vaeField": "Vae",
"vaeFieldDescription": "Vae submodel.",
"vaeModelField": "VAE",
@ -989,6 +1032,7 @@
"maskAdjustmentsHeader": "Mask Adjustments",
"maskBlur": "Blur",
"maskBlurMethod": "Blur Method",
"maskEdge": "Mask Edge",
"negativePromptPlaceholder": "Negative Prompt",
"noiseSettings": "Noise",
"noiseThreshold": "Noise Threshold",
@ -1036,6 +1080,7 @@
"upscale": "Upscale (Shift + U)",
"upscaleImage": "Upscale Image",
"upscaling": "Upscaling",
"unmasked": "Unmasked",
"useAll": "Use All",
"useCpuNoise": "Use CPU Noise",
"cpuNoise": "CPU Noise",
@ -1057,6 +1102,7 @@
"dynamicPrompts": "Dynamic Prompts",
"enableDynamicPrompts": "Enable Dynamic Prompts",
"maxPrompts": "Max Prompts",
"promptsPreview": "Prompts Preview",
"promptsWithCount_one": "{{count}} Prompt",
"promptsWithCount_other": "{{count}} Prompts",
"seedBehaviour": {
@ -1096,7 +1142,10 @@
"displayHelpIcons": "Display Help Icons",
"displayInProgress": "Display Progress Images",
"enableImageDebugging": "Enable Image Debugging",
"enableInformationalPopovers": "Enable Informational Popovers",
"enableInvisibleWatermark": "Enable Invisible Watermark",
"enableNodesEditor": "Enable Nodes Editor",
"enableNSFWChecker": "Enable NSFW Checker",
"experimental": "Experimental",
"favoriteSchedulers": "Favorite Schedulers",
"favoriteSchedulersPlaceholder": "No schedulers favorited",
@ -1113,13 +1162,13 @@
"showProgressInViewer": "Show Progress Images in Viewer",
"ui": "User Interface",
"useSlidersForAll": "Use Sliders For All Options",
"clearIntermediatesDisabled": "Queue must be empty to clear intermediates",
"clearIntermediatesDesc1": "Clearing intermediates will reset your Canvas and ControlNet state.",
"clearIntermediatesDesc2": "Intermediate images are byproducts of generation, different from the result images in the gallery. Clearing intermediates will free disk space.",
"clearIntermediatesDesc3": "Your gallery images will not be deleted.",
"clearIntermediates": "Clear Intermediates",
"clearIntermediatesWithCount_one": "Clear {{count}} Intermediate",
"clearIntermediatesWithCount_other": "Clear {{count}} Intermediates",
"clearIntermediatesWithCount_zero": "No Intermediates to Clear",
"intermediatesCleared_one": "Cleared {{count}} Intermediate",
"intermediatesCleared_other": "Cleared {{count}} Intermediates",
"intermediatesClearedFailed": "Problem Clearing Intermediates"
@ -1196,7 +1245,8 @@
"sentToImageToImage": "Sent To Image To Image",
"sentToUnifiedCanvas": "Sent to Unified Canvas",
"serverError": "Server Error",
"setCanvasInitialImage": "Set as canvas initial image",
"setAsCanvasInitialImage": "Set as canvas initial image",
"setCanvasInitialImage": "Set canvas initial image",
"setControlImage": "Set as control image",
"setIPAdapterImage": "Set as IP Adapter Image",
"setInitialImage": "Set as initial image",
@ -1254,11 +1304,15 @@
},
"compositingBlur": {
"heading": "Blur",
"paragraphs": ["The blur radius of the mask."]
"paragraphs": [
"The blur radius of the mask."
]
},
"compositingBlurMethod": {
"heading": "Blur Method",
"paragraphs": ["The method of blur applied to the masked area."]
"paragraphs": [
"The method of blur applied to the masked area."
]
},
"compositingCoherencePass": {
"heading": "Coherence Pass",
@ -1268,7 +1322,9 @@
},
"compositingCoherenceMode": {
"heading": "Mode",
"paragraphs": ["The mode of the Coherence Pass."]
"paragraphs": [
"The mode of the Coherence Pass."
]
},
"compositingCoherenceSteps": {
"heading": "Steps",
@ -1286,7 +1342,9 @@
},
"compositingMaskAdjustments": {
"heading": "Mask Adjustments",
"paragraphs": ["Adjust the mask."]
"paragraphs": [
"Adjust the mask."
]
},
"controlNetBeginEnd": {
"heading": "Begin / End Step Percentage",
@ -1344,7 +1402,9 @@
},
"infillMethod": {
"heading": "Infill Method",
"paragraphs": ["Method to infill the selected area."]
"paragraphs": [
"Method to infill the selected area."
]
},
"lora": {
"heading": "LoRA Weight",

228
invokeai/frontend/web/dist/locales/it.json vendored
View File

@ -87,7 +87,9 @@
"learnMore": "Per saperne di più",
"ipAdapter": "Adattatore IP",
"t2iAdapter": "Adattatore T2I",
"controlAdapter": "Adattatore di Controllo"
"controlAdapter": "Adattatore di Controllo",
"controlNet": "ControlNet",
"auto": "Automatico"
},
"gallery": {
"generations": "Generazioni",
@ -115,7 +117,10 @@
"currentlyInUse": "Questa immagine è attualmente utilizzata nelle seguenti funzionalità:",
"copy": "Copia",
"download": "Scarica",
"setCurrentImage": "Imposta come immagine corrente"
"setCurrentImage": "Imposta come immagine corrente",
"preparingDownload": "Preparazione del download",
"preparingDownloadFailed": "Problema durante la preparazione del download",
"downloadSelection": "Scarica gli elementi selezionati"
},
"hotkeys": {
"keyboardShortcuts": "Tasti rapidi",
@ -468,7 +473,8 @@
"useCustomConfig": "Utilizza configurazione personalizzata",
"closeAdvanced": "Chiudi Avanzate",
"modelType": "Tipo di modello",
"customConfigFileLocation": "Posizione del file di configurazione personalizzato"
"customConfigFileLocation": "Posizione del file di configurazione personalizzato",
"vaePrecision": "Precisione VAE"
},
"parameters": {
"images": "Immagini",
@ -570,9 +576,12 @@
"systemBusy": "Sistema occupato",
"unableToInvoke": "Impossibile invocare",
"systemDisconnected": "Sistema disconnesso",
"noControlImageForControlAdapter": "L'adattatore di controllo {{number}} non ha un'immagine di controllo",
"noModelForControlAdapter": "Nessun modello selezionato per l'adattatore di controllo {{number}}.",
"incompatibleBaseModelForControlAdapter": "Il modello dell'adattatore di controllo {{number}} non è compatibile con il modello principale."
"noControlImageForControlAdapter": "L'adattatore di controllo #{{number}} non ha un'immagine di controllo",
"noModelForControlAdapter": "Nessun modello selezionato per l'adattatore di controllo #{{number}}.",
"incompatibleBaseModelForControlAdapter": "Il modello dell'adattatore di controllo #{{number}} non è compatibile con il modello principale.",
"missingNodeTemplate": "Modello di nodo mancante",
"missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} ingresso mancante",
"missingFieldTemplate": "Modello di campo mancante"
},
"enableNoiseSettings": "Abilita le impostazioni del rumore",
"cpuNoise": "Rumore CPU",
@ -583,7 +592,7 @@
"iterations": "Iterazioni",
"iterationsWithCount_one": "{{count}} Iterazione",
"iterationsWithCount_many": "{{count}} Iterazioni",
"iterationsWithCount_other": "",
"iterationsWithCount_other": "{{count}} Iterazioni",
"seamlessX&Y": "Senza cuciture X & Y",
"isAllowedToUpscale": {
"useX2Model": "L'immagine è troppo grande per l'ampliamento con il modello x4, utilizza il modello x2",
@ -591,7 +600,8 @@
},
"seamlessX": "Senza cuciture X",
"seamlessY": "Senza cuciture Y",
"imageActions": "Azioni Immagine"
"imageActions": "Azioni Immagine",
"aspectRatioFree": "Libere"
},
"settings": {
"models": "Modelli",
@ -620,7 +630,19 @@
"beta": "Beta",
"enableNodesEditor": "Abilita l'editor dei nodi",
"experimental": "Sperimentale",
"autoChangeDimensions": "Aggiorna L/A alle impostazioni predefinite del modello in caso di modifica"
"autoChangeDimensions": "Aggiorna L/A alle impostazioni predefinite del modello in caso di modifica",
"clearIntermediates": "Cancella le immagini intermedie",
"clearIntermediatesDesc3": "Le immagini della galleria non verranno eliminate.",
"clearIntermediatesDesc2": "Le immagini intermedie sono sottoprodotti della generazione, diversi dalle immagini risultanti nella galleria. La cancellazione degli intermedi libererà spazio su disco.",
"intermediatesCleared_one": "Cancellata {{count}} immagine intermedia",
"intermediatesCleared_many": "Cancellate {{count}} immagini intermedie",
"intermediatesCleared_other": "Cancellate {{count}} immagini intermedie",
"clearIntermediatesDesc1": "La cancellazione delle immagini intermedie ripristinerà lo stato di Tela Unificata e ControlNet.",
"intermediatesClearedFailed": "Problema con la cancellazione delle immagini intermedie",
"clearIntermediatesWithCount_one": "Cancella {{count}} immagine intermedia",
"clearIntermediatesWithCount_many": "Cancella {{count}} immagini intermedie",
"clearIntermediatesWithCount_other": "Cancella {{count}} immagini intermedie",
"clearIntermediatesDisabled": "La coda deve essere vuota per cancellare le immagini intermedie"
},
"toast": {
"tempFoldersEmptied": "Cartella temporanea svuotata",
@ -670,9 +692,9 @@
"nodesUnrecognizedTypes": "Impossibile caricare. Il grafico ha tipi di dati non riconosciuti",
"nodesNotValidJSON": "JSON non valido",
"nodesBrokenConnections": "Impossibile caricare. Alcune connessioni sono interrotte.",
"baseModelChangedCleared_one": "Il modello base è stato modificato, cancellato o disabilitato {{number}} sotto-modello incompatibile",
"baseModelChangedCleared_many": "",
"baseModelChangedCleared_other": "",
"baseModelChangedCleared_one": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modello incompatibile",
"baseModelChangedCleared_many": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modelli incompatibili",
"baseModelChangedCleared_other": "Il modello base è stato modificato, cancellato o disabilitato {{count}} sotto-modelli incompatibili",
"imageSavingFailed": "Salvataggio dell'immagine non riuscito",
"canvasSentControlnetAssets": "Tela inviata a ControlNet & Risorse",
"problemCopyingCanvasDesc": "Impossibile copiare la tela",
@ -866,7 +888,145 @@
"workflowValidation": "Errore di convalida del flusso di lavoro",
"workflowAuthor": "Autore",
"workflowName": "Nome",
"workflowNotes": "Note"
"workflowNotes": "Note",
"unhandledInputProperty": "Proprietà di input non gestita",
"versionUnknown": " Versione sconosciuta",
"unableToValidateWorkflow": "Impossibile convalidare il flusso di lavoro",
"updateApp": "Aggiorna App",
"problemReadingWorkflow": "Problema durante la lettura del flusso di lavoro dall'immagine",
"unableToLoadWorkflow": "Impossibile caricare il flusso di lavoro",
"updateNode": "Aggiorna nodo",
"version": "Versione",
"notes": "Note",
"problemSettingTitle": "Problema nell'impostazione del titolo",
"unkownInvocation": "Tipo di invocazione sconosciuta",
"unknownTemplate": "Modello sconosciuto",
"nodeType": "Tipo di nodo",
"vaeField": "VAE",
"unhandledOutputProperty": "Proprietà di output non gestita",
"notesDescription": "Aggiunge note sul tuo flusso di lavoro",
"unknownField": "Campo sconosciuto",
"unknownNode": "Nodo sconosciuto",
"vaeFieldDescription": "Sotto modello VAE.",
"booleanPolymorphicDescription": "Una raccolta di booleani.",
"missingTemplate": "Modello mancante",
"outputSchemaNotFound": "Schema di output non trovato",
"colorFieldDescription": "Un colore RGBA.",
"maybeIncompatible": "Potrebbe essere incompatibile con quello installato",
"noNodeSelected": "Nessun nodo selezionato",
"colorPolymorphic": "Colore polimorfico",
"booleanCollectionDescription": "Una raccolta di booleani.",
"colorField": "Colore",
"nodeTemplate": "Modello di nodo",
"nodeOpacity": "Opacità del nodo",
"pickOne": "Sceglierne uno",
"outputField": "Campo di output",
"nodeSearch": "Cerca nodi",
"nodeOutputs": "Uscite del nodo",
"collectionItem": "Oggetto della raccolta",
"noConnectionInProgress": "Nessuna connessione in corso",
"noConnectionData": "Nessun dato di connessione",
"outputFields": "Campi di output",
"cannotDuplicateConnection": "Impossibile creare connessioni duplicate",
"booleanPolymorphic": "Polimorfico booleano",
"colorPolymorphicDescription": "Una collezione di colori polimorfici.",
"missingCanvaInitImage": "Immagine iniziale della tela mancante",
"clipFieldDescription": "Sottomodelli di tokenizzatore e codificatore di testo.",
"noImageFoundState": "Nessuna immagine iniziale trovata nello stato",
"clipField": "CLIP",
"noMatchingNodes": "Nessun nodo corrispondente",
"noFieldType": "Nessun tipo di campo",
"colorCollection": "Una collezione di colori.",
"noOutputSchemaName": "Nessun nome dello schema di output trovato nell'oggetto di riferimento",
"boolean": "Booleani",
"missingCanvaInitMaskImages": "Immagini di inizializzazione e maschera della tela mancanti",
"oNNXModelField": "Modello ONNX",
"node": "Nodo",
"booleanDescription": "I booleani sono veri o falsi.",
"collection": "Raccolta",
"cannotConnectInputToInput": "Impossibile collegare Input a Input",
"cannotConnectOutputToOutput": "Impossibile collegare Output ad Output",
"booleanCollection": "Raccolta booleana",
"cannotConnectToSelf": "Impossibile connettersi a se stesso",
"mismatchedVersion": "Ha una versione non corrispondente",
"outputNode": "Nodo di Output",
"loadingNodes": "Caricamento nodi...",
"oNNXModelFieldDescription": "Campo del modello ONNX.",
"denoiseMaskFieldDescription": "La maschera di riduzione del rumore può essere passata tra i nodi",
"floatCollectionDescription": "Una raccolta di numeri virgola mobile.",
"enum": "Enumeratore",
"float": "In virgola mobile",
"doesNotExist": "non esiste",
"currentImageDescription": "Visualizza l'immagine corrente nell'editor dei nodi",
"fieldTypesMustMatch": "I tipi di campo devono corrispondere",
"edge": "Bordo",
"enumDescription": "Gli enumeratori sono valori che possono essere una delle diverse opzioni.",
"denoiseMaskField": "Maschera riduzione rumore",
"currentImage": "Immagine corrente",
"floatCollection": "Raccolta in virgola mobile",
"inputField": "Campo di Input",
"controlFieldDescription": "Informazioni di controllo passate tra i nodi.",
"skippingUnknownOutputType": "Tipo di campo di output sconosciuto saltato",
"latentsFieldDescription": "Le immagini latenti possono essere passate tra i nodi.",
"ipAdapterPolymorphicDescription": "Una raccolta di adattatori IP.",
"latentsPolymorphicDescription": "Le immagini latenti possono essere passate tra i nodi.",
"ipAdapterCollection": "Raccolta Adattatori IP",
"conditioningCollection": "Raccolta condizionamenti",
"ipAdapterPolymorphic": "Adattatore IP Polimorfico",
"integerPolymorphicDescription": "Una raccolta di numeri interi.",
"conditioningCollectionDescription": "Il condizionamento può essere passato tra i nodi.",
"skippingReservedFieldType": "Tipo di campo riservato saltato",
"conditioningPolymorphic": "Condizionamento Polimorfico",
"integer": "Numero Intero",
"latentsCollection": "Raccolta Latenti",
"sourceNode": "Nodo di origine",
"integerDescription": "Gli interi sono numeri senza punto decimale.",
"stringPolymorphic": "Stringa polimorfica",
"conditioningPolymorphicDescription": "Il condizionamento può essere passato tra i nodi.",
"skipped": "Saltato",
"imagePolymorphic": "Immagine Polimorfica",
"imagePolymorphicDescription": "Una raccolta di immagini.",
"floatPolymorphic": "Numeri in virgola mobile Polimorfici",
"ipAdapterCollectionDescription": "Una raccolta di adattatori IP.",
"stringCollectionDescription": "Una raccolta di stringhe.",
"unableToParseNode": "Impossibile analizzare il nodo",
"controlCollection": "Raccolta di Controllo",
"stringCollection": "Raccolta di stringhe",
"inputMayOnlyHaveOneConnection": "L'ingresso può avere solo una connessione",
"ipAdapter": "Adattatore IP",
"integerCollection": "Raccolta di numeri interi",
"controlCollectionDescription": "Informazioni di controllo passate tra i nodi.",
"skippedReservedInput": "Campo di input riservato saltato",
"inputNode": "Nodo di Input",
"imageField": "Immagine",
"skippedReservedOutput": "Campo di output riservato saltato",
"integerCollectionDescription": "Una raccolta di numeri interi.",
"conditioningFieldDescription": "Il condizionamento può essere passato tra i nodi.",
"stringDescription": "Le stringhe sono testo.",
"integerPolymorphic": "Numero intero Polimorfico",
"ipAdapterModel": "Modello Adattatore IP",
"latentsPolymorphic": "Latenti polimorfici",
"skippingInputNoTemplate": "Campo di input senza modello saltato",
"ipAdapterDescription": "Un adattatore di prompt di immagini (Adattatore IP).",
"stringPolymorphicDescription": "Una raccolta di stringhe.",
"skippingUnknownInputType": "Tipo di campo di input sconosciuto saltato",
"controlField": "Controllo",
"ipAdapterModelDescription": "Campo Modello adattatore IP",
"invalidOutputSchema": "Schema di output non valido",
"floatDescription": "I numeri in virgola mobile sono numeri con un punto decimale.",
"floatPolymorphicDescription": "Una raccolta di numeri in virgola mobile.",
"conditioningField": "Condizionamento",
"string": "Stringa",
"latentsField": "Latenti",
"connectionWouldCreateCycle": "La connessione creerebbe un ciclo",
"inputFields": "Campi di Input",
"uNetFieldDescription": "Sub-modello UNet.",
"imageCollectionDescription": "Una raccolta di immagini.",
"imageFieldDescription": "Le immagini possono essere passate tra i nodi.",
"unableToParseEdge": "Impossibile analizzare il bordo",
"latentsCollectionDescription": "Le immagini latenti possono essere passate tra i nodi.",
"imageCollection": "Raccolta Immagini",
"loRAModelField": "LoRA"
},
"boards": {
"autoAddBoard": "Aggiungi automaticamente bacheca",
@ -883,7 +1043,8 @@
"searchBoard": "Cerca bacheche ...",
"noMatching": "Nessuna bacheca corrispondente",
"selectBoard": "Seleziona una Bacheca",
"uncategorized": "Non categorizzato"
"uncategorized": "Non categorizzato",
"downloadBoard": "Scarica la bacheca"
},
"controlnet": {
"contentShuffleDescription": "Rimescola il contenuto di un'immagine",
@ -951,8 +1112,13 @@
"addControlNet": "Aggiungi $t(common.controlNet)",
"controlNetT2IMutexDesc": "$t(common.controlNet) e $t(common.t2iAdapter) contemporaneamente non sono attualmente supportati.",
"addIPAdapter": "Aggiungi $t(common.ipAdapter)",
"controlAdapter": "Adattatore di Controllo",
"megaControl": "Mega ControlNet"
"controlAdapter_one": "Adattatore di Controllo",
"controlAdapter_many": "Adattatori di Controllo",
"controlAdapter_other": "Adattatori di Controllo",
"megaControl": "Mega ControlNet",
"minConfidence": "Confidenza minima",
"scribble": "Scribble",
"amult": "Angolo di illuminazione"
},
"queue": {
"queueFront": "Aggiungi all'inizio della coda",
@ -979,7 +1145,9 @@
"pause": "Sospendi",
"pruneTooltip": "Rimuovi {{item_count}} elementi completati",
"cancelSucceeded": "Elemento annullato",
"batchQueuedDesc": "Aggiunte {{item_count}} sessioni a {{direction}} della coda",
"batchQueuedDesc_one": "Aggiunta {{count}} sessione a {{direction}} della coda",
"batchQueuedDesc_many": "Aggiunte {{count}} sessioni a {{direction}} della coda",
"batchQueuedDesc_other": "Aggiunte {{count}} sessioni a {{direction}} della coda",
"graphQueued": "Grafico in coda",
"batch": "Lotto",
"clearQueueAlertDialog": "Lo svuotamento della coda annulla immediatamente tutti gli elementi in elaborazione e cancella completamente la coda.",
@ -1025,7 +1193,9 @@
"noLoRAsAvailable": "Nessun LoRA disponibile",
"noModelsAvailable": "Nessun modello disponibile",
"selectModel": "Seleziona un modello",
"selectLoRA": "Seleziona un LoRA"
"selectLoRA": "Seleziona un LoRA",
"noRefinerModelsInstalled": "Nessun modello SDXL Refiner installato",
"noLoRAsInstalled": "Nessun LoRA installato"
},
"invocationCache": {
"disable": "Disabilita",
@ -1056,7 +1226,7 @@
"maxPrompts": "Numero massimo di prompt",
"promptsWithCount_one": "{{count}} Prompt",
"promptsWithCount_many": "{{count}} Prompt",
"promptsWithCount_other": "",
"promptsWithCount_other": "{{count}} Prompt",
"dynamicPrompts": "Prompt dinamici"
},
"popovers": {
@ -1268,7 +1438,8 @@
"controlNet": {
"paragraphs": [
"ControlNet fornisce una guida al processo di generazione, aiutando a creare immagini con composizione, struttura o stile controllati, a seconda del modello selezionato."
]
],
"heading": "ControlNet"
}
},
"sdxl": {
@ -1313,6 +1484,21 @@
"createdBy": "Creato da",
"workflow": "Flusso di lavoro",
"steps": "Passi",
"scheduler": "Campionatore"
"scheduler": "Campionatore",
"recallParameters": "Richiama i parametri",
"noRecallParameters": "Nessun parametro da richiamare trovato"
},
"hrf": {
"enableHrf": "Abilita Correzione Alta Risoluzione",
"upscaleMethod": "Metodo di ampliamento",
"enableHrfTooltip": "Genera con una risoluzione iniziale inferiore, esegue l'ampliamento alla risoluzione di base, quindi esegue Immagine a Immagine.",
"metadata": {
"strength": "Forza della Correzione Alta Risoluzione",
"enabled": "Correzione Alta Risoluzione Abilitata",
"method": "Metodo della Correzione Alta Risoluzione"
},
"hrf": "Correzione Alta Risoluzione",
"hrfStrength": "Forza della Correzione Alta Risoluzione",
"strengthTooltip": "Valori più bassi comportano meno dettagli, il che può ridurre potenziali artefatti."
}
}

370
invokeai/frontend/web/dist/locales/ja.json vendored
View File

@ -1,6 +1,6 @@
{
"common": {
"languagePickerLabel": "言語選択",
"languagePickerLabel": "言語",
"reportBugLabel": "バグ報告",
"settingsLabel": "設定",
"langJapanese": "日本語",
@ -63,11 +63,34 @@
"langFrench": "Français",
"langGerman": "Deutsch",
"langPortuguese": "Português",
"nodes": "ノード",
"nodes": "ワークフローエディター",
"langKorean": "한국어",
"langPolish": "Polski",
"txt2img": "txt2img",
"postprocessing": "Post Processing"
"postprocessing": "Post Processing",
"t2iAdapter": "T2I アダプター",
"communityLabel": "コミュニティ",
"dontAskMeAgain": "次回から確認しない",
"areYouSure": "本当によろしいですか？",
"on": "オン",
"nodeEditor": "ノードエディター",
"ipAdapter": "IPアダプター",
"controlAdapter": "コントロールアダプター",
"auto": "自動",
"openInNewTab": "新しいタブで開く",
"controlNet": "コントロールネット",
"statusProcessing": "処理中",
"linear": "リニア",
"imageFailedToLoad": "画像が読み込めません",
"imagePrompt": "画像プロンプト",
"modelManager": "モデルマネージャー",
"lightMode": "ライトモード",
"generate": "生成",
"learnMore": "もっと学ぶ",
"darkMode": "ダークモード",
"random": "ランダム",
"batch": "バッチマネージャー",
"advanced": "高度な設定"
},
"gallery": {
"uploads": "アップロード",
@ -274,7 +297,7 @@
"config": "Config",
"configValidationMsg": "モデルの設定ファイルへのパス",
"modelLocation": "モデルの場所",
"modelLocationValidationMsg": "モデルが配置されている場所へのパス。",
"modelLocationValidationMsg": "ディフューザーモデルのあるローカルフォルダーのパスを入力してください",
"repo_id": "Repo ID",
"repoIDValidationMsg": "モデルのリモートリポジトリ",
"vaeLocation": "VAEの場所",
@ -309,12 +332,79 @@
"delete": "削除",
"deleteModel": "モデルを削除",
"deleteConfig": "設定を削除",
"deleteMsg1": "InvokeAIからこのモデルエントリーを削除してよろしいですか?",
"deleteMsg2": "これは、ドライブからモデルのCheckpointファイルを削除するものではありません。必要であればそれらを読み込むことができます。",
"deleteMsg1": "InvokeAIからこのモデルを削除してよろしいですか?",
"deleteMsg2": "これは、モデルがInvokeAIルートフォルダ内にある場合、ディスクからモデルを削除します。カスタム保存場所を使用している場合、モデルはディスクから削除されません。",
"formMessageDiffusersModelLocation": "Diffusersモデルの場所",
"formMessageDiffusersModelLocationDesc": "最低でも1つは入力してください。",
"formMessageDiffusersVAELocation": "VAEの場所s",
"formMessageDiffusersVAELocationDesc": "指定しない場合、InvokeAIは上記のモデルの場所にあるVAEファイルを探します。"
"formMessageDiffusersVAELocationDesc": "指定しない場合、InvokeAIは上記のモデルの場所にあるVAEファイルを探します。",
"importModels": "モデルをインポート",
"custom": "カスタム",
"none": "なし",
"convert": "変換",
"statusConverting": "変換中",
"cannotUseSpaces": "スペースは使えません",
"convertToDiffusersHelpText6": "このモデルを変換しますか？",
"checkpointModels": "チェックポイント",
"settings": "設定",
"convertingModelBegin": "モデルを変換しています...",
"baseModel": "ベースモデル",
"modelDeleteFailed": "モデルの削除ができませんでした",
"convertToDiffusers": "ディフューザーに変換",
"alpha": "アルファ",
"diffusersModels": "ディフューザー",
"pathToCustomConfig": "カスタム設定のパス",
"noCustomLocationProvided": "カスタムロケーションが指定されていません",
"modelConverted": "モデル変換が完了しました",
"weightedSum": "重み付け総和",
"inverseSigmoid": "逆シグモイド",
"invokeAIFolder": "Invoke AI フォルダ",
"syncModelsDesc": "モデルがバックエンドと同期していない場合、このオプションを使用してモデルを更新できます。通常、モデル.yamlファイルを手動で更新したり、アプリケーションの起動後にモデルをInvokeAIルートフォルダに追加した場合に便利です。",
"noModels": "モデルが見つかりません",
"sigmoid": "シグモイド",
"merge": "マージ",
"modelMergeInterpAddDifferenceHelp": "このモードでは、モデル3がまずモデル2から減算されます。その結果得られたバージョンが、上記で設定されたアルファ率でモデル1とブレンドされます。",
"customConfig": "カスタム設定",
"predictionType": "予測タイプ（安定したディフュージョン 2.x モデルおよび一部の安定したディフュージョン 1.x モデル用）",
"selectModel": "モデルを選択",
"modelSyncFailed": "モデルの同期に失敗しました",
"quickAdd": "クイック追加",
"simpleModelDesc": "ローカルのDiffusersモデル、ローカルのチェックポイント/safetensorsモデル、HuggingFaceリポジトリのID、またはチェックポイント/ DiffusersモデルのURLへのパスを指定してください。",
"customSaveLocation": "カスタム保存場所",
"advanced": "高度な設定",
"modelDeleted": "モデルが削除されました",
"convertToDiffusersHelpText2": "このプロセスでは、モデルマネージャーのエントリーを同じモデルのディフューザーバージョンに置き換えます。",
"modelUpdateFailed": "モデル更新が失敗しました",
"useCustomConfig": "カスタム設定を使用する",
"convertToDiffusersHelpText5": "十分なディスク空き容量があることを確認してください。モデルは一般的に2GBから7GBのサイズがあります。",
"modelConversionFailed": "モデル変換が失敗しました",
"modelEntryDeleted": "モデルエントリーが削除されました",
"syncModels": "モデルを同期",
"mergedModelSaveLocation": "保存場所",
"closeAdvanced": "高度な設定を閉じる",
"modelType": "モデルタイプ",
"modelsMerged": "モデルマージ完了",
"modelsMergeFailed": "モデルマージ失敗",
"scanForModels": "モデルをスキャン",
"customConfigFileLocation": "カスタム設定ファイルの場所",
"convertToDiffusersHelpText1": "このモデルは 🧨 Diffusers フォーマットに変換されます。",
"modelsSynced": "モデルが同期されました",
"invokeRoot": "InvokeAIフォルダ",
"mergedModelCustomSaveLocation": "カスタムパス",
"mergeModels": "マージモデル",
"interpolationType": "補間タイプ",
"modelMergeHeaderHelp2": "マージできるのはDiffusersのみです。チェックポイントモデルをマージしたい場合は、まずDiffusersに変換してください。",
"convertToDiffusersSaveLocation": "保存場所",
"pickModelType": "モデルタイプを選択",
"sameFolder": "同じフォルダ",
"convertToDiffusersHelpText3": "チェックポイントファイルは、InvokeAIルートフォルダ内にある場合、ディスクから削除されます。カスタムロケーションにある場合は、削除されません。",
"loraModels": "LoRA",
"modelMergeAlphaHelp": "アルファはモデルのブレンド強度を制御します。アルファ値が低いと、2番目のモデルの影響が低くなります。",
"addDifference": "差分を追加",
"modelMergeHeaderHelp1": "あなたのニーズに適したブレンドを作成するために、異なるモデルを最大3つまでマージすることができます。",
"ignoreMismatch": "選択されたモデル間の不一致を無視する",
"convertToDiffusersHelpText4": "これは一回限りのプロセスです。コンピュータの仕様によっては、約30秒から60秒かかる可能性があります。",
"mergedModelName": "マージされたモデル名"
},
"parameters": {
"images": "画像",
@ -440,7 +530,8 @@
"next": "次",
"accept": "同意",
"showHide": "表示/非表示",
"discardAll": "すべて破棄"
"discardAll": "すべて破棄",
"snapToGrid": "グリッドにスナップ"
},
"accessibility": {
"modelSelect": "モデルを選択",
@ -452,7 +543,7 @@
"useThisParameter": "このパラメータを使用する",
"copyMetadataJson": "メタデータをコピー(JSON)",
"zoomIn": "ズームイン",
"exitViewer": "ExitViewer",
"exitViewer": "ビューアーを終了",
"zoomOut": "ズームアウト",
"rotateCounterClockwise": "反時計回りに回転",
"rotateClockwise": "時計回りに回転",
@ -461,6 +552,265 @@
"toggleAutoscroll": "自動スクロールの切替",
"modifyConfig": "Modify Config",
"toggleLogViewer": "Log Viewerの切替",
"showOptionsPanel": "オプションパネルを表示"
"showOptionsPanel": "サイドパネルを表示",
"showGalleryPanel": "ギャラリーパネルを表示",
"menu": "メニュー",
"loadMore": "さらに読み込む"
},
"controlnet": {
"resize": "リサイズ",
"showAdvanced": "高度な設定を表示",
"addT2IAdapter": "$t(common.t2iAdapter)を追加",
"importImageFromCanvas": "キャンバスから画像をインポート",
"lineartDescription": "画像を線画に変換",
"importMaskFromCanvas": "キャンバスからマスクをインポート",
"hideAdvanced": "高度な設定を非表示",
"ipAdapterModel": "アダプターモデル",
"resetControlImage": "コントロール画像をリセット",
"beginEndStepPercent": "開始 / 終了ステップパーセンテージ",
"duplicate": "複製",
"balanced": "バランス",
"prompt": "プロンプト",
"depthMidasDescription": "Midasを使用して深度マップを生成",
"openPoseDescription": "Openposeを使用してポーズを推定",
"control": "コントロール",
"resizeMode": "リサイズモード",
"weight": "重み",
"selectModel": "モデルを選択",
"crop": "切り抜き",
"w": "幅",
"processor": "プロセッサー",
"addControlNet": "$t(common.controlNet)を追加",
"none": "なし",
"incompatibleBaseModel": "互換性のないベースモデル:",
"enableControlnet": "コントロールネットを有効化",
"detectResolution": "検出解像度",
"controlNetT2IMutexDesc": "$t(common.controlNet)と$t(common.t2iAdapter)の同時使用は現在サポートされていません。",
"pidiDescription": "PIDI画像処理",
"controlMode": "コントロールモード",
"fill": "塗りつぶし",
"cannyDescription": "Canny 境界検出",
"addIPAdapter": "$t(common.ipAdapter)を追加",
"colorMapDescription": "画像からカラーマップを生成",
"lineartAnimeDescription": "アニメスタイルの線画処理",
"imageResolution": "画像解像度",
"megaControl": "メガコントロール",
"lowThreshold": "最低閾値",
"autoConfigure": "プロセッサーを自動設定",
"highThreshold": "最大閾値",
"saveControlImage": "コントロール画像を保存",
"toggleControlNet": "このコントロールネットを切り替え",
"delete": "削除",
"controlAdapter_other": "コントロールアダプター",
"colorMapTileSize": "タイルサイズ",
"ipAdapterImageFallback": "IP Adapterの画像が選択されていません",
"mediapipeFaceDescription": "Mediapipeを使用して顔を検出",
"depthZoeDescription": "Zoeを使用して深度マップを生成",
"setControlImageDimensions": "コントロール画像のサイズを幅と高さにセット",
"resetIPAdapterImage": "IP Adapterの画像をリセット",
"handAndFace": "手と顔",
"enableIPAdapter": "IP Adapterを有効化",
"amult": "a_mult",
"contentShuffleDescription": "画像の内容をシャッフルします",
"bgth": "bg_th",
"controlNetEnabledT2IDisabled": "$t(common.controlNet) が有効化され、$t(common.t2iAdapter)s が無効化されました",
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
"t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) が有効化され、$t(common.controlNet)s が無効化されました",
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
"minConfidence": "最小確信度",
"colorMap": "Color",
"noneDescription": "処理は行われていません",
"canny": "Canny",
"hedDescription": "階層的エッジ検出",
"maxFaces": "顔の最大数"
},
"metadata": {
"seamless": "シームレス",
"Threshold": "ノイズ閾値",
"seed": "シード",
"width": "幅",
"workflow": "ワークフロー",
"steps": "ステップ",
"scheduler": "スケジューラー",
"positivePrompt": "ポジティブプロンプト",
"strength": "Image to Image 強度",
"perlin": "パーリンノイズ",
"recallParameters": "パラメータを呼び出す"
},
"queue": {
"queueEmpty": "キューが空です",
"pauseSucceeded": "処理が一時停止されました",
"queueFront": "キューの先頭へ追加",
"queueBack": "キューに追加",
"queueCountPrediction": "{{predicted}}をキューに追加",
"queuedCount": "保留中 {{pending}}",
"pause": "一時停止",
"queue": "キュー",
"pauseTooltip": "処理を一時停止",
"cancel": "キャンセル",
"queueTotal": "合計 {{total}}",
"resumeSucceeded": "処理が再開されました",
"resumeTooltip": "処理を再開",
"resume": "再会",
"status": "ステータス",
"pruneSucceeded": "キューから完了アイテム{{item_count}}件を削除しました",
"cancelTooltip": "現在のアイテムをキャンセル",
"in_progress": "進行中",
"notReady": "キューに追加できません",
"batchFailedToQueue": "バッチをキューに追加できませんでした",
"completed": "完了",
"batchValues": "バッチの値",
"cancelFailed": "アイテムのキャンセルに問題があります",
"batchQueued": "バッチをキューに追加しました",
"pauseFailed": "処理の一時停止に問題があります",
"clearFailed": "キューのクリアに問題があります",
"front": "先頭",
"clearSucceeded": "キューがクリアされました",
"pruneTooltip": "{{item_count}} の完了アイテムを削除",
"cancelSucceeded": "アイテムがキャンセルされました",
"batchQueuedDesc_other": "{{count}} セッションをキューの{{direction}}に追加しました",
"graphQueued": "グラフをキューに追加しました",
"batch": "バッチ",
"clearQueueAlertDialog": "キューをクリアすると、処理中のアイテムは直ちにキャンセルされ、キューは完全にクリアされます。",
"pending": "保留中",
"resumeFailed": "処理の再開に問題があります",
"clear": "クリア",
"total": "合計",
"canceled": "キャンセル",
"pruneFailed": "キューの削除に問題があります",
"cancelBatchSucceeded": "バッチがキャンセルされました",
"clearTooltip": "全てのアイテムをキャンセルしてクリア",
"current": "現在",
"failed": "失敗",
"cancelItem": "項目をキャンセル",
"next": "次",
"cancelBatch": "バッチをキャンセル",
"session": "セッション",
"enqueueing": "バッチをキューに追加",
"queueMaxExceeded": "{{max_queue_size}} の最大値を超えたため、{{skip}} をスキップします",
"cancelBatchFailed": "バッチのキャンセルに問題があります",
"clearQueueAlertDialog2": "キューをクリアしてもよろしいですか？",
"item": "アイテム",
"graphFailedToQueue": "グラフをキューに追加できませんでした"
},
"models": {
"noMatchingModels": "一致するモデルがありません",
"loading": "読み込み中",
"noMatchingLoRAs": "一致するLoRAがありません",
"noLoRAsAvailable": "使用可能なLoRAがありません",
"noModelsAvailable": "使用可能なモデルがありません",
"selectModel": "モデルを選択してください",
"selectLoRA": "LoRAを選択してください"
},
"nodes": {
"addNode": "ノードを追加",
"boardField": "ボード",
"boolean": "ブーリアン",
"boardFieldDescription": "ギャラリーボード",
"addNodeToolTip": "ノードを追加 (Shift+A, Space)",
"booleanPolymorphicDescription": "ブーリアンのコレクション。",
"inputField": "入力フィールド",
"latentsFieldDescription": "潜在空間はノード間で伝達できます。",
"floatCollectionDescription": "浮動小数点のコレクション。",
"missingTemplate": "テンプレートが見つかりません",
"ipAdapterPolymorphicDescription": "IP-Adaptersのコレクション。",
"latentsPolymorphicDescription": "潜在空間はノード間で伝達できます。",
"colorFieldDescription": "RGBAカラー。",
"ipAdapterCollection": "IP-Adapterコレクション",
"conditioningCollection": "条件付きコレクション",
"hideGraphNodes": "グラフオーバーレイを非表示",
"loadWorkflow": "ワークフローを読み込み",
"integerPolymorphicDescription": "整数のコレクション。",
"hideLegendNodes": "フィールドタイプの凡例を非表示",
"float": "浮動小数点",
"booleanCollectionDescription": "ブーリアンのコレクション。",
"integer": "整数",
"colorField": "カラー",
"nodeTemplate": "ノードテンプレート",
"integerDescription": "整数は小数点を持たない数値です。",
"imagePolymorphicDescription": "画像のコレクション。",
"doesNotExist": "存在しません",
"ipAdapterCollectionDescription": "IP-Adaptersのコレクション。",
"inputMayOnlyHaveOneConnection": "入力は1つの接続しか持つことができません",
"nodeOutputs": "ノード出力",
"currentImageDescription": "ノードエディタ内の現在の画像を表示",
"downloadWorkflow": "ワークフローのJSONをダウンロード",
"integerCollection": "整数コレクション",
"collectionItem": "コレクションアイテム",
"fieldTypesMustMatch": "フィールドタイプが一致している必要があります",
"edge": "輪郭",
"inputNode": "入力ノード",
"imageField": "画像",
"animatedEdgesHelp": "選択したエッジおよび選択したノードに接続されたエッジをアニメーション化します",
"cannotDuplicateConnection": "重複した接続は作れません",
"noWorkflow": "ワークフローがありません",
"integerCollectionDescription": "整数のコレクション。",
"colorPolymorphicDescription": "カラーのコレクション。",
"missingCanvaInitImage": "キャンバスの初期画像が見つかりません",
"clipFieldDescription": "トークナイザーとテキストエンコーダーサブモデル。",
"fullyContainNodesHelp": "ノードは選択ボックス内に完全に存在する必要があります",
"clipField": "クリップ",
"nodeType": "ノードタイプ",
"executionStateInProgress": "処理中",
"executionStateError": "エラー",
"ipAdapterModel": "IP-Adapterモデル",
"ipAdapterDescription": "イメージプロンプトアダプター(IP-Adapter)。",
"missingCanvaInitMaskImages": "キャンバスの初期画像およびマスクが見つかりません",
"hideMinimapnodes": "ミニマップを非表示",
"fitViewportNodes": "全体を表示",
"executionStateCompleted": "完了",
"node": "ノード",
"currentImage": "現在の画像",
"controlField": "コントロール",
"booleanDescription": "ブーリアンはtrueかfalseです。",
"collection": "コレクション",
"ipAdapterModelDescription": "IP-Adapterモデルフィールド",
"cannotConnectInputToInput": "入力から入力には接続できません",
"invalidOutputSchema": "無効な出力スキーマ",
"floatDescription": "浮動小数点は、小数点を持つ数値です。",
"floatPolymorphicDescription": "浮動小数点のコレクション。",
"floatCollection": "浮動小数点コレクション",
"latentsField": "潜在空間",
"cannotConnectOutputToOutput": "出力から出力には接続できません",
"booleanCollection": "ブーリアンコレクション",
"cannotConnectToSelf": "自身のノードには接続できません",
"inputFields": "入力フィールド(複数)",
"colorCodeEdges": "カラー-Code Edges",
"imageCollectionDescription": "画像のコレクション。",
"loadingNodes": "ノードを読み込み中...",
"imageCollection": "画像コレクション"
},
"boards": {
"autoAddBoard": "自動追加するボード",
"move": "移動",
"menuItemAutoAdd": "このボードに自動追加",
"myBoard": "マイボード",
"searchBoard": "ボードを検索...",
"noMatching": "一致するボードがありません",
"selectBoard": "ボードを選択",
"cancel": "キャンセル",
"addBoard": "ボードを追加",
"uncategorized": "未分類",
"downloadBoard": "ボードをダウンロード",
"changeBoard": "ボードを変更",
"loading": "ロード中...",
"topMessage": "このボードには、以下の機能で使用されている画像が含まれています：",
"bottomMessage": "このボードおよび画像を削除すると、現在これらを利用している機能はリセットされます。",
"clearSearch": "検索をクリア"
},
"embedding": {
"noMatchingEmbedding": "一致する埋め込みがありません",
"addEmbedding": "埋め込みを追加",
"incompatibleModel": "互換性のないベースモデル:"
},
"invocationCache": {
"invocationCache": "呼び出しキャッシュ",
"clearSucceeded": "呼び出しキャッシュをクリアしました",
"clearFailed": "呼び出しキャッシュのクリアに問題があります",
"enable": "有効",
"clear": "クリア",
"maxCacheSize": "最大キャッシュサイズ",
"cacheSize": "キャッシュサイズ"
}
}

824
invokeai/frontend/web/dist/locales/nl.json vendored
View File

@ -79,7 +79,18 @@
"modelManager": "Modelbeheer",
"darkMode": "Donkere modus",
"lightMode": "Lichte modus",
"communityLabel": "Gemeenschap"
"communityLabel": "Gemeenschap",
"t2iAdapter": "T2I-adapter",
"on": "Aan",
"nodeEditor": "Knooppunteditor",
"ipAdapter": "IP-adapter",
"controlAdapter": "Control-adapter",
"auto": "Autom.",
"controlNet": "ControlNet",
"statusProcessing": "Bezig met verwerken",
"imageFailedToLoad": "Kan afbeelding niet laden",
"learnMore": "Meer informatie",
"advanced": "Uitgebreid"
},
"gallery": {
"generations": "Gegenereerde afbeeldingen",
@ -95,12 +106,22 @@
"allImagesLoaded": "Alle afbeeldingen geladen",
"loadMore": "Laad meer",
"noImagesInGallery": "Geen afbeeldingen om te tonen",
"deleteImage": "Wis afbeelding",
"deleteImageBin": "Gewiste afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
"deleteImagePermanent": "Gewiste afbeeldingen kunnen niet worden hersteld.",
"deleteImage": "Verwijder afbeelding",
"deleteImageBin": "Verwijderde afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
"deleteImagePermanent": "Verwijderde afbeeldingen kunnen niet worden hersteld.",
"assets": "Eigen onderdelen",
"images": "Afbeeldingen",
"autoAssignBoardOnClick": "Ken automatisch bord toe bij klikken"
"autoAssignBoardOnClick": "Ken automatisch bord toe bij klikken",
"featuresWillReset": "Als je deze afbeelding verwijdert, dan worden deze functies onmiddellijk teruggezet.",
"loading": "Bezig met laden",
"unableToLoad": "Kan galerij niet laden",
"preparingDownload": "Bezig met voorbereiden van download",
"preparingDownloadFailed": "Fout bij voorbereiden van download",
"downloadSelection": "Download selectie",
"currentlyInUse": "Deze afbeelding is momenteel in gebruik door de volgende functies:",
"copy": "Kopieer",
"download": "Download",
"setCurrentImage": "Stel in als huidige afbeelding"
},
"hotkeys": {
"keyboardShortcuts": "Sneltoetsen",
@ -332,7 +353,7 @@
"config": "Configuratie",
"configValidationMsg": "Pad naar het configuratiebestand van je model.",
"modelLocation": "Locatie model",
"modelLocationValidationMsg": "Pad naar waar je model zich bevindt.",
"modelLocationValidationMsg": "Geef het pad naar een lokale map waar je Diffusers-model wordt bewaard",
"vaeLocation": "Locatie VAE",
"vaeLocationValidationMsg": "Pad naar waar je VAE zich bevindt.",
"width": "Breedte",
@ -365,11 +386,11 @@
"deleteModel": "Verwijder model",
"deleteConfig": "Verwijder configuratie",
"deleteMsg1": "Weet je zeker dat je dit model wilt verwijderen uit InvokeAI?",
"deleteMsg2": "Hiermee ZAL het model van schijf worden verwijderd als het zich bevindt in de InvokeAI-beginmap. Als je het model vanaf een eigen locatie gebruikt, dan ZAL het model NIET van schijf worden verwijderd.",
"deleteMsg2": "Hiermee ZAL het model van schijf worden verwijderd als het zich bevindt in de beginmap van InvokeAI. Als je het model vanaf een eigen locatie gebruikt, dan ZAL het model NIET van schijf worden verwijderd.",
"formMessageDiffusersVAELocationDesc": "Indien niet opgegeven, dan zal InvokeAI kijken naar het VAE-bestand in de hierboven gegeven modellocatie.",
"repoIDValidationMsg": "Online repository van je model",
"formMessageDiffusersModelLocation": "Locatie Diffusers-model",
"convertToDiffusersHelpText3": "Je checkpoint-bestand op schijf ZAL worden verwijderd als het zich in de InvokeAI root map bevindt. Het zal NIET worden verwijderd als het zich in een andere locatie bevindt.",
"convertToDiffusersHelpText3": "Je checkpoint-bestand op de schijf ZAL worden verwijderd als het zich in de beginmap van InvokeAI bevindt. Het ZAL NIET worden verwijderd als het zich in een andere locatie bevindt.",
"convertToDiffusersHelpText6": "Wil je dit model omzetten?",
"allModels": "Alle modellen",
"checkpointModels": "Checkpoints",
@ -437,14 +458,24 @@
"noCustomLocationProvided": "Geen Aangepaste Locatie Opgegeven",
"syncModels": "Synchroniseer Modellen",
"modelsSynced": "Modellen Gesynchroniseerd",
"modelSyncFailed": "Synchronisatie Modellen Gefaald",
"modelSyncFailed": "Synchronisatie modellen mislukt",
"modelDeleteFailed": "Model kon niet verwijderd worden",
"convertingModelBegin": "Model aan het converteren. Even geduld.",
"importModels": "Importeer Modellen",
"syncModelsDesc": "Als je modellen niet meer synchroon zijn met de backend, kan je ze met deze optie verversen. Dit wordt typisch gebruikt in het geval je het models.yaml bestand met de hand bewerkt of als je modellen aan de InvokeAI root map toevoegt nadat de applicatie gestart werd.",
"syncModelsDesc": "Als je modellen niet meer synchroon zijn met de backend, kan je ze met deze optie vernieuwen. Dit wordt meestal gebruikt in het geval je het bestand models.yaml met de hand bewerkt of als je modellen aan de beginmap van InvokeAI toevoegt nadat de applicatie gestart is.",
"loraModels": "LoRA's",
"onnxModels": "Onnx",
"oliveModels": "Olives"
"oliveModels": "Olives",
"noModels": "Geen modellen gevonden",
"predictionType": "Soort voorspelling (voor Stable Diffusion 2.x-modellen en incidentele Stable Diffusion 1.x-modellen)",
"quickAdd": "Voeg snel toe",
"simpleModelDesc": "Geef een pad naar een lokaal Diffusers-model, lokale-checkpoint- / safetensors-model, een HuggingFace-repo-ID of een url naar een checkpoint- / Diffusers-model.",
"advanced": "Uitgebreid",
"useCustomConfig": "Gebruik eigen configuratie",
"closeAdvanced": "Sluit uitgebreid",
"modelType": "Soort model",
"customConfigFileLocation": "Locatie eigen configuratiebestand",
"vaePrecision": "Nauwkeurigheid VAE"
},
"parameters": {
"images": "Afbeeldingen",
@ -465,7 +496,7 @@
"type": "Soort",
"strength": "Sterkte",
"upscaling": "Opschalen",
"upscale": "Schaal op",
"upscale": "Vergroot (Shift + U)",
"upscaleImage": "Schaal afbeelding op",
"scale": "Schaal",
"otherOptions": "Andere opties",
@ -496,7 +527,7 @@
"useInitImg": "Gebruik initiële afbeelding",
"info": "Info",
"initialImage": "Initiële afbeelding",
"showOptionsPanel": "Toon deelscherm Opties",
"showOptionsPanel": "Toon deelscherm Opties (O of T)",
"symmetry": "Symmetrie",
"hSymmetryStep": "Stap horiz. symmetrie",
"vSymmetryStep": "Stap vert. symmetrie",
@ -504,7 +535,8 @@
"immediate": "Annuleer direct",
"isScheduled": "Annuleren",
"setType": "Stel annuleervorm in",
"schedule": "Annuleer na huidige iteratie"
"schedule": "Annuleer na huidige iteratie",
"cancel": "Annuleer"
},
"general": "Algemeen",
"copyImage": "Kopieer afbeelding",
@ -520,7 +552,7 @@
"boundingBoxWidth": "Tekenvak breedte",
"boundingBoxHeight": "Tekenvak hoogte",
"clipSkip": "Overslaan CLIP",
"aspectRatio": "Verhouding",
"aspectRatio": "Beeldverhouding",
"negativePromptPlaceholder": "Negatieve prompt",
"controlNetControlMode": "Aansturingsmodus",
"positivePromptPlaceholder": "Positieve prompt",
@ -532,7 +564,46 @@
"coherenceSteps": "Stappen",
"coherenceStrength": "Sterkte",
"seamHighThreshold": "Hoog",
"seamLowThreshold": "Laag"
"seamLowThreshold": "Laag",
"invoke": {
"noNodesInGraph": "Geen knooppunten in graaf",
"noModelSelected": "Geen model ingesteld",
"invoke": "Start",
"noPrompts": "Geen prompts gegenereerd",
"systemBusy": "Systeem is bezig",
"noInitialImageSelected": "Geen initiële afbeelding gekozen",
"missingInputForField": "{{nodeLabel}} -> {{fieldLabel}} invoer ontbreekt",
"noControlImageForControlAdapter": "Controle-adapter #{{number}} heeft geen controle-afbeelding",
"noModelForControlAdapter": "Control-adapter #{{number}} heeft geen model ingesteld staan.",
"unableToInvoke": "Kan niet starten",
"incompatibleBaseModelForControlAdapter": "Model van controle-adapter #{{number}} is ongeldig in combinatie met het hoofdmodel.",
"systemDisconnected": "Systeem is niet verbonden",
"missingNodeTemplate": "Knooppuntsjabloon ontbreekt",
"readyToInvoke": "Klaar om te starten",
"missingFieldTemplate": "Veldsjabloon ontbreekt",
"addingImagesTo": "Bezig met toevoegen van afbeeldingen aan"
},
"seamlessX&Y": "Naadloos X en Y",
"isAllowedToUpscale": {
"useX2Model": "Afbeelding is te groot om te vergroten met het x4-model. Gebruik hiervoor het x2-model",
"tooLarge": "Afbeelding is te groot om te vergoten. Kies een kleinere afbeelding"
},
"aspectRatioFree": "Vrij",
"cpuNoise": "CPU-ruis",
"patchmatchDownScaleSize": "Verklein",
"gpuNoise": "GPU-ruis",
"seamlessX": "Naadloos X",
"useCpuNoise": "Gebruik CPU-ruis",
"clipSkipWithLayerCount": "Overslaan CLIP {{layerCount}}",
"seamlessY": "Naadloos Y",
"manualSeed": "Handmatige seedwaarde",
"imageActions": "Afbeeldingshandeling",
"randomSeed": "Willekeurige seedwaarde",
"iterations": "Iteraties",
"iterationsWithCount_one": "{{count}} iteratie",
"iterationsWithCount_other": "{{count}} iteraties",
"enableNoiseSettings": "Schakel ruisinstellingen in",
"coherenceMode": "Modus"
},
"settings": {
"models": "Modellen",
@ -544,14 +615,14 @@
"resetWebUI": "Herstel web-UI",
"resetWebUIDesc1": "Herstel web-UI herstelt alleen de lokale afbeeldingscache en de onthouden instellingen van je browser. Het verwijdert geen afbeeldingen van schijf.",
"resetWebUIDesc2": "Als afbeeldingen niet getoond worden in de galerij of iets anders werkt niet, probeer dan eerst deze herstelfunctie voordat je een fout aanmeldt op GitHub.",
"resetComplete": "Webgebruikersinterface is hersteld.",
"resetComplete": "Webinterface is hersteld.",
"useSlidersForAll": "Gebruik schuifbalken voor alle opties",
"consoleLogLevel": "Logboekniveau",
"consoleLogLevel": "Niveau logboek",
"shouldLogToConsole": "Schrijf logboek naar console",
"developer": "Ontwikkelaar",
"general": "Algemeen",
"showProgressInViewer": "Toon voortgangsafbeeldingen in viewer",
"generation": "Generatie",
"generation": "Genereren",
"ui": "Gebruikersinterface",
"antialiasProgressImages": "Voer anti-aliasing uit op voortgangsafbeeldingen",
"showAdvancedOptions": "Toon uitgebreide opties",
@ -560,8 +631,17 @@
"beta": "Bèta",
"experimental": "Experimenteel",
"alternateCanvasLayout": "Omwisselen Canvas Layout",
"enableNodesEditor": "Knopen Editor Inschakelen",
"autoChangeDimensions": "Werk bij wijziging afmetingen bij naar modelstandaard"
"enableNodesEditor": "Schakel Knooppunteditor in",
"autoChangeDimensions": "Werk B/H bij naar modelstandaard bij wijziging",
"clearIntermediates": "Wis tussentijdse afbeeldingen",
"clearIntermediatesDesc3": "Je galerijafbeeldingen zullen niet worden verwijderd.",
"clearIntermediatesWithCount_one": "Wis {{count}} tussentijdse afbeelding",
"clearIntermediatesWithCount_other": "Wis {{count}} tussentijdse afbeeldingen",
"clearIntermediatesDesc2": "Tussentijdse afbeeldingen zijn nevenproducten bij het genereren. Deze wijken af van de uitvoerafbeeldingen in de galerij. Als je tussentijdse afbeeldingen wist, wordt schijfruimte vrijgemaakt.",
"intermediatesCleared_one": "{{count}} tussentijdse afbeelding gewist",
"intermediatesCleared_other": "{{count}} tussentijdse afbeeldingen gewist",
"clearIntermediatesDesc1": "Als je tussentijdse afbeeldingen wist, dan wordt de staat hersteld van je canvas en van ControlNet.",
"intermediatesClearedFailed": "Fout bij wissen van tussentijdse afbeeldingen"
},
"toast": {
"tempFoldersEmptied": "Tijdelijke map geleegd",
@ -610,7 +690,42 @@
"nodesCorruptedGraph": "Kan niet laden. Graph lijkt corrupt.",
"nodesUnrecognizedTypes": "Laden mislukt. Graph heeft onherkenbare types",
"nodesBrokenConnections": "Laden mislukt. Sommige verbindingen zijn verbroken.",
"nodesNotValidGraph": "Geen geldige knooppunten graph"
"nodesNotValidGraph": "Geen geldige knooppunten graph",
"baseModelChangedCleared_one": "Basismodel is gewijzigd: {{count}} niet-compatibel submodel weggehaald of uitgeschakeld",
"baseModelChangedCleared_other": "Basismodel is gewijzigd: {{count}} niet-compatibele submodellen weggehaald of uitgeschakeld",
"imageSavingFailed": "Fout bij bewaren afbeelding",
"canvasSentControlnetAssets": "Canvas gestuurd naar ControlNet en Assets",
"problemCopyingCanvasDesc": "Kan basislaag niet exporteren",
"loadedWithWarnings": "Werkstroom geladen met waarschuwingen",
"setInitialImage": "Ingesteld als initiële afbeelding",
"canvasCopiedClipboard": "Canvas gekopieerd naar klembord",
"setControlImage": "Ingesteld als controle-afbeelding",
"setNodeField": "Ingesteld als knooppuntveld",
"problemSavingMask": "Fout bij bewaren masker",
"problemSavingCanvasDesc": "Kan basislaag niet exporteren",
"maskSavedAssets": "Masker bewaard in Assets",
"modelAddFailed": "Fout bij toevoegen model",
"problemDownloadingCanvas": "Fout bij downloaden van canvas",
"problemMergingCanvas": "Fout bij samenvoegen canvas",
"setCanvasInitialImage": "Ingesteld als initiële canvasafbeelding",
"imageUploaded": "Afbeelding geüpload",
"addedToBoard": "Toegevoegd aan bord",
"workflowLoaded": "Werkstroom geladen",
"modelAddedSimple": "Model toegevoegd",
"problemImportingMaskDesc": "Kan masker niet exporteren",
"problemCopyingCanvas": "Fout bij kopiëren canvas",
"problemSavingCanvas": "Fout bij bewaren canvas",
"canvasDownloaded": "Canvas gedownload",
"setIPAdapterImage": "Ingesteld als IP-adapterafbeelding",
"problemMergingCanvasDesc": "Kan basislaag niet exporteren",
"problemDownloadingCanvasDesc": "Kan basislaag niet exporteren",
"problemSavingMaskDesc": "Kan masker niet exporteren",
"imageSaved": "Afbeelding bewaard",
"maskSentControlnetAssets": "Masker gestuurd naar ControlNet en Assets",
"canvasSavedGallery": "Canvas bewaard in galerij",
"imageUploadFailed": "Fout bij uploaden afbeelding",
"modelAdded": "Model toegevoegd: {{modelName}}",
"problemImportingMask": "Fout bij importeren masker"
},
"tooltip": {
"feature": {
@ -685,7 +800,9 @@
"betaDarkenOutside": "Verduister buiten tekenvak",
"betaLimitToBox": "Beperk tot tekenvak",
"betaPreserveMasked": "Behoud masker",
"antialiasing": "Anti-aliasing"
"antialiasing": "Anti-aliasing",
"showResultsOn": "Toon resultaten (aan)",
"showResultsOff": "Toon resultaten (uit)"
},
"accessibility": {
"exitViewer": "Stop viewer",
@ -707,7 +824,9 @@
"toggleAutoscroll": "Autom. scrollen aan/uit",
"toggleLogViewer": "Logboekviewer aan/uit",
"showOptionsPanel": "Toon zijscherm",
"menu": "Menu"
"menu": "Menu",
"showGalleryPanel": "Toon deelscherm Galerij",
"loadMore": "Laad meer"
},
"ui": {
"showProgressImages": "Toon voortgangsafbeeldingen",
@ -730,6 +849,661 @@
"resetWorkflow": "Herstel werkstroom",
"resetWorkflowDesc": "Weet je zeker dat je deze werkstroom wilt herstellen?",
"resetWorkflowDesc2": "Herstel van een werkstroom haalt alle knooppunten, randen en werkstroomdetails weg.",
"downloadWorkflow": "Download JSON van werkstroom"
"downloadWorkflow": "Download JSON van werkstroom",
"booleanPolymorphicDescription": "Een verzameling Booleanse waarden.",
"scheduler": "Planner",
"inputField": "Invoerveld",
"controlFieldDescription": "Controlegegevens doorgegeven tussen knooppunten.",
"skippingUnknownOutputType": "Overslaan van onbekend soort uitvoerveld",
"latentsFieldDescription": "Latents kunnen worden doorgegeven tussen knooppunten.",
"denoiseMaskFieldDescription": "Ontruisingsmasker kan worden doorgegeven tussen knooppunten",
"floatCollectionDescription": "Een verzameling zwevende-kommagetallen.",
"missingTemplate": "Ontbrekende sjabloon",
"outputSchemaNotFound": "Uitvoerschema niet gevonden",
"ipAdapterPolymorphicDescription": "Een verzameling IP-adapters.",
"workflowDescription": "Korte beschrijving",
"latentsPolymorphicDescription": "Latents kunnen worden doorgegeven tussen knooppunten.",
"colorFieldDescription": "Een RGBA-kleur.",
"mainModelField": "Model",
"unhandledInputProperty": "Onverwerkt invoerkenmerk",
"versionUnknown": " Versie onbekend",
"ipAdapterCollection": "Verzameling IP-adapters",
"conditioningCollection": "Verzameling conditionering",
"maybeIncompatible": "Is mogelijk niet compatibel met geïnstalleerde knooppunten",
"ipAdapterPolymorphic": "Polymorfisme IP-adapter",
"noNodeSelected": "Geen knooppunt gekozen",
"addNode": "Voeg knooppunt toe",
"unableToValidateWorkflow": "Kan werkstroom niet valideren",
"enum": "Enumeratie",
"integerPolymorphicDescription": "Een verzameling gehele getallen.",
"noOutputRecorded": "Geen uitvoer opgenomen",
"updateApp": "Werk app bij",
"conditioningCollectionDescription": "Conditionering kan worden doorgegeven tussen knooppunten.",
"colorPolymorphic": "Polymorfisme kleur",
"colorCodeEdgesHelp": "Kleurgecodeerde randen op basis van hun verbonden velden",
"collectionDescription": "TODO",
"float": "Zwevende-kommagetal",
"workflowContact": "Contactpersoon",
"skippingReservedFieldType": "Overslaan van gereserveerd veldsoort",
"animatedEdges": "Geanimeerde randen",
"booleanCollectionDescription": "Een verzameling van Booleanse waarden.",
"sDXLMainModelFieldDescription": "SDXL-modelveld.",
"conditioningPolymorphic": "Polymorfisme conditionering",
"integer": "Geheel getal",
"colorField": "Kleur",
"boardField": "Bord",
"nodeTemplate": "Sjabloon knooppunt",
"latentsCollection": "Verzameling latents",
"problemReadingWorkflow": "Fout bij lezen van werkstroom uit afbeelding",
"sourceNode": "Bronknooppunt",
"nodeOpacity": "Dekking knooppunt",
"pickOne": "Kies er een",
"collectionItemDescription": "TODO",
"integerDescription": "Gehele getallen zijn getallen zonder een decimaalteken.",
"outputField": "Uitvoerveld",
"unableToLoadWorkflow": "Kan werkstroom niet valideren",
"snapToGrid": "Lijn uit op raster",
"stringPolymorphic": "Polymorfisme tekenreeks",
"conditioningPolymorphicDescription": "Conditionering kan worden doorgegeven tussen knooppunten.",
"noFieldsLinearview": "Geen velden toegevoegd aan lineaire weergave",
"skipped": "Overgeslagen",
"imagePolymorphic": "Polymorfisme afbeelding",
"nodeSearch": "Zoek naar knooppunten",
"updateNode": "Werk knooppunt bij",
"sDXLRefinerModelFieldDescription": "Beschrijving",
"imagePolymorphicDescription": "Een verzameling afbeeldingen.",
"floatPolymorphic": "Polymorfisme zwevende-kommagetal",
"version": "Versie",
"doesNotExist": "bestaat niet",
"ipAdapterCollectionDescription": "Een verzameling van IP-adapters.",
"stringCollectionDescription": "Een verzameling tekenreeksen.",
"unableToParseNode": "Kan knooppunt niet inlezen",
"controlCollection": "Controle-verzameling",
"validateConnections": "Valideer verbindingen en graaf",
"stringCollection": "Verzameling tekenreeksen",
"inputMayOnlyHaveOneConnection": "Invoer mag slechts een enkele verbinding hebben",
"notes": "Opmerkingen",
"uNetField": "UNet",
"nodeOutputs": "Uitvoer knooppunt",
"currentImageDescription": "Toont de huidige afbeelding in de knooppunteditor",
"validateConnectionsHelp": "Voorkom dat er ongeldige verbindingen worden gelegd en dat er ongeldige grafen worden aangeroepen",
"problemSettingTitle": "Fout bij instellen titel",
"ipAdapter": "IP-adapter",
"integerCollection": "Verzameling gehele getallen",
"collectionItem": "Verzamelingsonderdeel",
"noConnectionInProgress": "Geen verbinding bezig te maken",
"vaeModelField": "VAE",
"controlCollectionDescription": "Controlegegevens doorgegeven tussen knooppunten.",
"skippedReservedInput": "Overgeslagen gereserveerd invoerveld",
"workflowVersion": "Versie",
"noConnectionData": "Geen verbindingsgegevens",
"outputFields": "Uitvoervelden",
"fieldTypesMustMatch": "Veldsoorten moeten overeenkomen",
"workflow": "Werkstroom",
"edge": "Rand",
"inputNode": "Invoerknooppunt",
"enumDescription": "Enumeraties zijn waarden die uit een aantal opties moeten worden gekozen.",
"unkownInvocation": "Onbekende aanroepsoort",
"loRAModelFieldDescription": "TODO",
"imageField": "Afbeelding",
"skippedReservedOutput": "Overgeslagen gereserveerd uitvoerveld",
"animatedEdgesHelp": "Animeer gekozen randen en randen verbonden met de gekozen knooppunten",
"cannotDuplicateConnection": "Kan geen dubbele verbindingen maken",
"booleanPolymorphic": "Polymorfisme Booleaanse waarden",
"unknownTemplate": "Onbekend sjabloon",
"noWorkflow": "Geen werkstroom",
"removeLinearView": "Verwijder uit lineaire weergave",
"colorCollectionDescription": "TODO",
"integerCollectionDescription": "Een verzameling gehele getallen.",
"colorPolymorphicDescription": "Een verzameling kleuren.",
"sDXLMainModelField": "SDXL-model",
"workflowTags": "Labels",
"denoiseMaskField": "Ontruisingsmasker",
"schedulerDescription": "Beschrijving",
"missingCanvaInitImage": "Ontbrekende initialisatie-afbeelding voor canvas",
"conditioningFieldDescription": "Conditionering kan worden doorgegeven tussen knooppunten.",
"clipFieldDescription": "Submodellen voor tokenizer en text_encoder.",
"fullyContainNodesHelp": "Knooppunten moeten zich volledig binnen het keuzevak bevinden om te worden gekozen",
"noImageFoundState": "Geen initiële afbeelding gevonden in de staat",
"workflowValidation": "Validatiefout werkstroom",
"clipField": "Clip",
"stringDescription": "Tekenreeksen zijn tekst.",
"nodeType": "Soort knooppunt",
"noMatchingNodes": "Geen overeenkomende knooppunten",
"fullyContainNodes": "Omvat knooppunten volledig om ze te kiezen",
"integerPolymorphic": "Polymorfisme geheel getal",
"executionStateInProgress": "Bezig",
"noFieldType": "Geen soort veld",
"colorCollection": "Een verzameling kleuren.",
"executionStateError": "Fout",
"noOutputSchemaName": "Geen naam voor uitvoerschema gevonden in referentieobject",
"ipAdapterModel": "Model IP-adapter",
"latentsPolymorphic": "Polymorfisme latents",
"vaeModelFieldDescription": "Beschrijving",
"skippingInputNoTemplate": "Overslaan van invoerveld zonder sjabloon",
"ipAdapterDescription": "Een Afbeeldingsprompt-adapter (IP-adapter).",
"boolean": "Booleaanse waarden",
"missingCanvaInitMaskImages": "Ontbrekende initialisatie- en maskerafbeeldingen voor canvas",
"problemReadingMetadata": "Fout bij lezen van metagegevens uit afbeelding",
"stringPolymorphicDescription": "Een verzameling tekenreeksen.",
"oNNXModelField": "ONNX-model",
"executionStateCompleted": "Voltooid",
"node": "Knooppunt",
"skippingUnknownInputType": "Overslaan van onbekend soort invoerveld",
"workflowAuthor": "Auteur",
"currentImage": "Huidige afbeelding",
"controlField": "Controle",
"workflowName": "Naam",
"booleanDescription": "Booleanse waarden zijn waar en onwaar.",
"collection": "Verzameling",
"ipAdapterModelDescription": "Modelveld IP-adapter",
"cannotConnectInputToInput": "Kan invoer niet aan invoer verbinden",
"invalidOutputSchema": "Ongeldig uitvoerschema",
"boardFieldDescription": "Een galerijbord",
"floatDescription": "Zwevende-kommagetallen zijn getallen met een decimaalteken.",
"floatPolymorphicDescription": "Een verzameling zwevende-kommagetallen.",
"vaeField": "Vae",
"conditioningField": "Conditionering",
"unhandledOutputProperty": "Onverwerkt uitvoerkenmerk",
"workflowNotes": "Opmerkingen",
"string": "Tekenreeks",
"floatCollection": "Verzameling zwevende-kommagetallen",
"latentsField": "Latents",
"cannotConnectOutputToOutput": "Kan uitvoer niet aan uitvoer verbinden",
"booleanCollection": "Verzameling Booleaanse waarden",
"connectionWouldCreateCycle": "Verbinding zou cyclisch worden",
"cannotConnectToSelf": "Kan niet aan zichzelf verbinden",
"notesDescription": "Voeg opmerkingen toe aan je werkstroom",
"unknownField": "Onbekend veld",
"inputFields": "Invoervelden",
"colorCodeEdges": "Kleurgecodeerde randen",
"uNetFieldDescription": "UNet-submodel.",
"unknownNode": "Onbekend knooppunt",
"imageCollectionDescription": "Een verzameling afbeeldingen.",
"mismatchedVersion": "Heeft niet-overeenkomende versie",
"vaeFieldDescription": "Vae-submodel.",
"imageFieldDescription": "Afbeeldingen kunnen worden doorgegeven tussen knooppunten.",
"outputNode": "Uitvoerknooppunt",
"addNodeToolTip": "Voeg knooppunt toe (Shift+A, spatie)",
"loadingNodes": "Bezig met laden van knooppunten...",
"snapToGridHelp": "Lijn knooppunten uit op raster bij verplaatsing",
"workflowSettings": "Instellingen werkstroomeditor",
"mainModelFieldDescription": "TODO",
"sDXLRefinerModelField": "Verfijningsmodel",
"loRAModelField": "LoRA",
"unableToParseEdge": "Kan rand niet inlezen",
"latentsCollectionDescription": "Latents kunnen worden doorgegeven tussen knooppunten.",
"oNNXModelFieldDescription": "ONNX-modelveld.",
"imageCollection": "Afbeeldingsverzameling"
},
"controlnet": {
"amult": "a_mult",
"resize": "Schaal",
"showAdvanced": "Toon uitgebreide opties",
"contentShuffleDescription": "Verschuift het materiaal in de afbeelding",
"bgth": "bg_th",
"addT2IAdapter": "Voeg $t(common.t2iAdapter) toe",
"pidi": "PIDI",
"importImageFromCanvas": "Importeer afbeelding uit canvas",
"lineartDescription": "Zet afbeelding om naar line-art",
"normalBae": "Normale BAE",
"importMaskFromCanvas": "Importeer masker uit canvas",
"hed": "HED",
"hideAdvanced": "Verberg uitgebreid",
"contentShuffle": "Verschuif materiaal",
"controlNetEnabledT2IDisabled": "$t(common.controlNet) ingeschakeld, $t(common.t2iAdapter)s uitgeschakeld",
"ipAdapterModel": "Adaptermodel",
"resetControlImage": "Herstel controle-afbeelding",
"beginEndStepPercent": "Percentage begin-/eindstap",
"mlsdDescription": "Minimalistische herkenning lijnsegmenten",
"duplicate": "Maak kopie",
"balanced": "Gebalanceerd",
"f": "F",
"h": "H",
"prompt": "Prompt",
"depthMidasDescription": "Genereer diepteblad via Midas",
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
"openPoseDescription": "Menselijke pose-benadering via Openpose",
"control": "Controle",
"resizeMode": "Modus schaling",
"t2iEnabledControlNetDisabled": "$t(common.t2iAdapter) ingeschakeld, $t(common.controlNet)s uitgeschakeld",
"coarse": "Grof",
"weight": "Gewicht",
"selectModel": "Kies een model",
"crop": "Snij bij",
"depthMidas": "Diepte (Midas)",
"w": "B",
"processor": "Verwerker",
"addControlNet": "Voeg $t(common.controlNet) toe",
"none": "Geen",
"incompatibleBaseModel": "Niet-compatibel basismodel:",
"enableControlnet": "Schakel ControlNet in",
"detectResolution": "Herken resolutie",
"controlNetT2IMutexDesc": "Gelijktijdig gebruik van $t(common.controlNet) en $t(common.t2iAdapter) wordt op dit moment niet ondersteund.",
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
"pidiDescription": "PIDI-afbeeldingsverwerking",
"mediapipeFace": "Mediapipe - Gezicht",
"mlsd": "M-LSD",
"controlMode": "Controlemodus",
"fill": "Vul",
"cannyDescription": "Herkenning Canny-rand",
"addIPAdapter": "Voeg $t(common.ipAdapter) toe",
"lineart": "Line-art",
"colorMapDescription": "Genereert een kleurenblad van de afbeelding",
"lineartAnimeDescription": "Lineartverwerking in anime-stijl",
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
"minConfidence": "Min. vertrouwensniveau",
"imageResolution": "Resolutie afbeelding",
"megaControl": "Zeer veel controle",
"depthZoe": "Diepte (Zoe)",
"colorMap": "Kleur",
"lowThreshold": "Lage drempelwaarde",
"autoConfigure": "Configureer verwerker automatisch",
"highThreshold": "Hoge drempelwaarde",
"normalBaeDescription": "Normale BAE-verwerking",
"noneDescription": "Geen verwerking toegepast",
"saveControlImage": "Bewaar controle-afbeelding",
"openPose": "Openpose",
"toggleControlNet": "Zet deze ControlNet aan/uit",
"delete": "Verwijder",
"controlAdapter_one": "Control-adapter",
"controlAdapter_other": "Control-adapters",
"safe": "Veilig",
"colorMapTileSize": "Grootte tegel",
"lineartAnime": "Line-art voor anime",
"ipAdapterImageFallback": "Geen IP-adapterafbeelding gekozen",
"mediapipeFaceDescription": "Gezichtsherkenning met Mediapipe",
"canny": "Canny",
"depthZoeDescription": "Genereer diepteblad via Zoe",
"hedDescription": "Herkenning van holistisch-geneste randen",
"setControlImageDimensions": "Stel afmetingen controle-afbeelding in op B/H",
"scribble": "Krabbel",
"resetIPAdapterImage": "Herstel IP-adapterafbeelding",
"handAndFace": "Hand en gezicht",
"enableIPAdapter": "Schakel IP-adapter in",
"maxFaces": "Max. gezichten"
},
"dynamicPrompts": {
"seedBehaviour": {
"perPromptDesc": "Gebruik een verschillende seedwaarde per afbeelding",
"perIterationLabel": "Seedwaarde per iteratie",
"perIterationDesc": "Gebruik een verschillende seedwaarde per iteratie",
"perPromptLabel": "Seedwaarde per afbeelding",
"label": "Gedrag seedwaarde"
},
"enableDynamicPrompts": "Schakel dynamische prompts in",
"combinatorial": "Combinatorisch genereren",
"maxPrompts": "Max. prompts",
"promptsWithCount_one": "{{count}} prompt",
"promptsWithCount_other": "{{count}} prompts",
"dynamicPrompts": "Dynamische prompts"
},
"popovers": {
"noiseUseCPU": {
"paragraphs": [
"Bepaalt of ruis wordt gegenereerd op de CPU of de GPU.",
"Met CPU-ruis ingeschakeld zal een bepaalde seedwaarde dezelfde afbeelding opleveren op welke machine dan ook.",
"Er is geen prestatieverschil bij het inschakelen van CPU-ruis."
],
"heading": "Gebruik CPU-ruis"
},
"paramScheduler": {
"paragraphs": [
"De planner bepaalt hoe ruis per iteratie wordt toegevoegd aan een afbeelding of hoe een monster wordt bijgewerkt op basis van de uitvoer van een model."
],
"heading": "Planner"
},
"scaleBeforeProcessing": {
"paragraphs": [
"Schaalt het gekozen gebied naar de grootte die het meest geschikt is voor het model, vooraf aan het proces van het afbeeldingen genereren."
],
"heading": "Schaal vooraf aan verwerking"
},
"compositingMaskAdjustments": {
"heading": "Aanpassingen masker",
"paragraphs": [
"Pas het masker aan."
]
},
"paramRatio": {
"heading": "Beeldverhouding",
"paragraphs": [
"De beeldverhouding van de afmetingen van de afbeelding die wordt gegenereerd.",
"Een afbeeldingsgrootte (in aantal pixels) equivalent aan 512x512 wordt aanbevolen voor SD1.5-modellen. Een grootte-equivalent van 1024x1024 wordt aanbevolen voor SDXL-modellen."
]
},
"compositingCoherenceSteps": {
"heading": "Stappen",
"paragraphs": [
"Het aantal te gebruiken ontruisingsstappen in de coherentiefase.",
"Gelijk aan de hoofdparameter Stappen."
]
},
"dynamicPrompts": {
"paragraphs": [
"Dynamische prompts vormt een enkele prompt om in vele.",
"De basissyntax is \"a {red|green|blue} ball\". Dit zal de volgende drie prompts geven: \"a red ball\", \"a green ball\" en \"a blue ball\".",
"Gebruik de syntax zo vaak als je wilt in een enkele prompt, maar zorg ervoor dat het aantal gegenereerde prompts in lijn ligt met de instelling Max. prompts."
],
"heading": "Dynamische prompts"
},
"paramVAE": {
"paragraphs": [
"Het model gebruikt voor het vertalen van AI-uitvoer naar de uiteindelijke afbeelding."
],
"heading": "VAE"
},
"compositingBlur": {
"heading": "Vervaging",
"paragraphs": [
"De vervagingsstraal van het masker."
]
},
"paramIterations": {
"paragraphs": [
"Het aantal te genereren afbeeldingen.",
"Als dynamische prompts is ingeschakeld, dan zal elke prompt dit aantal keer gegenereerd worden."
],
"heading": "Iteraties"
},
"paramVAEPrecision": {
"heading": "Nauwkeurigheid VAE",
"paragraphs": [
"De nauwkeurigheid gebruikt tijdens de VAE-codering en -decodering. FP16/halve nauwkeurig is efficiënter, ten koste van kleine afbeeldingsvariaties."
]
},
"compositingCoherenceMode": {
"heading": "Modus",
"paragraphs": [
"De modus van de coherentiefase."
]
},
"paramSeed": {
"paragraphs": [
"Bepaalt de startruis die gebruikt wordt bij het genereren.",
"Schakel \"Willekeurige seedwaarde\" uit om identieke resultaten te krijgen met dezelfde genereer-instellingen."
],
"heading": "Seedwaarde"
},
"controlNetResizeMode": {
"heading": "Schaalmodus",
"paragraphs": [
"Hoe de ControlNet-afbeelding zal worden geschaald aan de uitvoergrootte van de afbeelding."
]
},
"controlNetBeginEnd": {
"paragraphs": [
"Op welke stappen van het ontruisingsproces ControlNet worden toegepast.",
"ControlNets die worden toegepast aan het begin begeleiden het compositieproces. ControlNets die worden toegepast aan het eind zorgen voor details."
],
"heading": "Percentage begin- / eindstap"
},
"dynamicPromptsSeedBehaviour": {
"paragraphs": [
"Bepaalt hoe de seedwaarde wordt gebruikt bij het genereren van prompts.",
"Per iteratie zal een unieke seedwaarde worden gebruikt voor elke iteratie. Gebruik dit om de promptvariaties binnen een enkele seedwaarde te verkennen.",
"Bijvoorbeeld: als je vijf prompts heb, dan zal voor elke afbeelding dezelfde seedwaarde gebruikt worden.",
"De optie Per afbeelding zal een unieke seedwaarde voor elke afbeelding gebruiken. Dit biedt meer variatie."
],
"heading": "Gedrag seedwaarde"
},
"clipSkip": {
"paragraphs": [
"Kies hoeveel CLIP-modellagen je wilt overslaan.",
"Bepaalde modellen werken beter met bepaalde Overslaan CLIP-instellingen.",
"Een hogere waarde geeft meestal een minder gedetailleerde afbeelding."
],
"heading": "Overslaan CLIP"
},
"paramModel": {
"heading": "Model",
"paragraphs": [
"Model gebruikt voor de ontruisingsstappen.",
"Verschillende modellen zijn meestal getraind om zich te specialiseren in het maken van bepaalde esthetische resultaten en materiaal."
]
},
"compositingCoherencePass": {
"heading": "Coherentiefase",
"paragraphs": [
"Een tweede ronde ontruising helpt bij het samenstellen van de erin- of eruitgetekende afbeelding."
]
},
"paramDenoisingStrength": {
"paragraphs": [
"Hoeveel ruis wordt toegevoegd aan de invoerafbeelding.",
"0 levert een identieke afbeelding op, waarbij 1 een volledig nieuwe afbeelding oplevert."
],
"heading": "Ontruisingssterkte"
},
"compositingStrength": {
"heading": "Sterkte",
"paragraphs": [
"Ontruisingssterkte voor de coherentiefase.",
"Gelijk aan de parameter Ontruisingssterkte Afbeelding naar afbeelding."
]
},
"paramNegativeConditioning": {
"paragraphs": [
"Het genereerproces voorkomt de gegeven begrippen in de negatieve prompt. Gebruik dit om bepaalde zaken of voorwerpen uit te sluiten van de uitvoerafbeelding.",
"Ondersteunt Compel-syntax en -embeddingen."
],
"heading": "Negatieve prompt"
},
"compositingBlurMethod": {
"heading": "Vervagingsmethode",
"paragraphs": [
"De methode van de vervaging die wordt toegepast op het gemaskeerd gebied."
]
},
"dynamicPromptsMaxPrompts": {
"heading": "Max. prompts",
"paragraphs": [
"Beperkt het aantal prompts die kunnen worden gegenereerd door dynamische prompts."
]
},
"infillMethod": {
"paragraphs": [
"Methode om een gekozen gebied in te vullen."
],
"heading": "Invulmethode"
},
"controlNetWeight": {
"heading": "Gewicht",
"paragraphs": [
"Hoe sterk ControlNet effect heeft op de gegeneerde afbeelding."
]
},
"controlNet": {
"heading": "ControlNet",
"paragraphs": [
"ControlNets begeleidt het genereerproces, waarbij geholpen wordt bij het maken van afbeeldingen met aangestuurde compositie, structuur of stijl, afhankelijk van het gekozen model."
]
},
"paramCFGScale": {
"heading": "CFG-schaal",
"paragraphs": [
"Bepaalt hoeveel je prompt invloed heeft op het genereerproces."
]
},
"controlNetControlMode": {
"paragraphs": [
"Geeft meer gewicht aan ofwel de prompt danwel ControlNet."
],
"heading": "Controlemodus"
},
"paramSteps": {
"heading": "Stappen",
"paragraphs": [
"Het aantal uit te voeren stappen tijdens elke generatie.",
"Een hoger aantal stappen geven meestal betere afbeeldingen, ten koste van een hogere benodigde tijd om te genereren."
]
},
"paramPositiveConditioning": {
"heading": "Positieve prompt",
"paragraphs": [
"Begeleidt het generartieproces. Gebruik een woord of frase naar keuze.",
"Syntaxes en embeddings voor Compel en dynamische prompts."
]
},
"lora": {
"heading": "Gewicht LoRA",
"paragraphs": [
"Een hogere LoRA-gewicht zal leiden tot een groter effect op de uiteindelijke afbeelding."
]
}
},
"metadata": {
"seamless": "Naadloos",
"positivePrompt": "Positieve prompt",
"negativePrompt": "Negatieve prompt",
"generationMode": "Genereermodus",
"Threshold": "Drempelwaarde ruis",
"metadata": "Metagegevens",
"strength": "Sterkte Afbeelding naar afbeelding",
"seed": "Seedwaarde",
"imageDetails": "Afbeeldingsdetails",
"perlin": "Perlin-ruis",
"model": "Model",
"noImageDetails": "Geen afbeeldingsdetails gevonden",
"hiresFix": "Optimalisatie voor hoge resolutie",
"cfgScale": "CFG-schaal",
"fit": "Schaal aanpassen in Afbeelding naar afbeelding",
"initImage": "Initiële afbeelding",
"recallParameters": "Opnieuw aan te roepen parameters",
"height": "Hoogte",
"variations": "Paren seedwaarde-gewicht",
"noMetaData": "Geen metagegevens gevonden",
"width": "Breedte",
"createdBy": "Gemaakt door",
"workflow": "Werkstroom",
"steps": "Stappen",
"scheduler": "Planner",
"noRecallParameters": "Geen opnieuw uit te voeren parameters gevonden"
},
"queue": {
"status": "Status",
"pruneSucceeded": "{{item_count}} voltooide onderdelen uit wachtrij opgeruimd",
"cancelTooltip": "Annuleer huidig onderdeel",
"queueEmpty": "Wachtrij leeg",
"pauseSucceeded": "Verwerker onderbroken",
"in_progress": "Bezig",
"queueFront": "Voeg vooraan toe in wachtrij",
"notReady": "Fout bij plaatsen in wachtrij",
"batchFailedToQueue": "Fout bij reeks in wachtrij plaatsen",
"completed": "Voltooid",
"queueBack": "Voeg toe aan wachtrij",
"batchValues": "Reekswaarden",
"cancelFailed": "Fout bij annuleren onderdeel",
"queueCountPrediction": "Voeg {{predicted}} toe aan wachtrij",
"batchQueued": "Reeks in wachtrij geplaatst",
"pauseFailed": "Fout bij onderbreken verwerker",
"clearFailed": "Fout bij wissen van wachtrij",
"queuedCount": "{{pending}} wachtend",
"front": "begin",
"clearSucceeded": "Wachtrij gewist",
"pause": "Onderbreek",
"pruneTooltip": "Ruim {{item_count}} voltooide onderdelen op",
"cancelSucceeded": "Onderdeel geannuleerd",
"batchQueuedDesc_one": "Voeg {{count}} sessie toe aan het {{direction}} van de wachtrij",
"batchQueuedDesc_other": "Voeg {{count}} sessies toe aan het {{direction}} van de wachtrij",
"graphQueued": "Graaf in wachtrij geplaatst",
"queue": "Wachtrij",
"batch": "Reeks",
"clearQueueAlertDialog": "Als je de wachtrij onmiddellijk wist, dan worden alle onderdelen die bezig zijn geannuleerd en wordt de wachtrij volledig gewist.",
"pending": "Wachtend",
"completedIn": "Voltooid na",
"resumeFailed": "Fout bij hervatten verwerker",
"clear": "Wis",
"prune": "Ruim op",
"total": "Totaal",
"canceled": "Geannuleerd",
"pruneFailed": "Fout bij opruimen van wachtrij",
"cancelBatchSucceeded": "Reeks geannuleerd",
"clearTooltip": "Annuleer en wis alle onderdelen",
"current": "Huidig",
"pauseTooltip": "Onderbreek verwerker",
"failed": "Mislukt",
"cancelItem": "Annuleer onderdeel",
"next": "Volgende",
"cancelBatch": "Annuleer reeks",
"back": "eind",
"cancel": "Annuleer",
"session": "Sessie",
"queueTotal": "Totaal {{total}}",
"resumeSucceeded": "Verwerker hervat",
"enqueueing": "Bezig met toevoegen van reeks aan wachtrij",
"resumeTooltip": "Hervat verwerker",
"queueMaxExceeded": "Max. aantal van {{max_queue_size}} overschreden, {{skip}} worden overgeslagen",
"resume": "Hervat",
"cancelBatchFailed": "Fout bij annuleren van reeks",
"clearQueueAlertDialog2": "Weet je zeker dat je de wachtrij wilt wissen?",
"item": "Onderdeel",
"graphFailedToQueue": "Fout bij toevoegen graaf aan wachtrij"
},
"sdxl": {
"refinerStart": "Startwaarde verfijning",
"selectAModel": "Kies een model",
"scheduler": "Planner",
"cfgScale": "CFG-schaal",
"negStylePrompt": "Negatieve-stijlprompt",
"noModelsAvailable": "Geen modellen beschikbaar",
"refiner": "Verfijning",
"negAestheticScore": "Negatieve esthetische score",
"useRefiner": "Gebruik verfijning",
"denoisingStrength": "Sterkte ontruising",
"refinermodel": "Verfijningsmodel",
"posAestheticScore": "Positieve esthetische score",
"concatPromptStyle": "Plak prompt- en stijltekst aan elkaar",
"loading": "Bezig met laden...",
"steps": "Stappen",
"posStylePrompt": "Positieve-stijlprompt"
},
"models": {
"noMatchingModels": "Geen overeenkomend modellen",
"loading": "bezig met laden",
"noMatchingLoRAs": "Geen overeenkomende LoRA's",
"noLoRAsAvailable": "Geen LoRA's beschikbaar",
"noModelsAvailable": "Geen modellen beschikbaar",
"selectModel": "Kies een model",
"selectLoRA": "Kies een LoRA"
},
"boards": {
"autoAddBoard": "Voeg automatisch bord toe",
"topMessage": "Dit bord bevat afbeeldingen die in gebruik zijn door de volgende functies:",
"move": "Verplaats",
"menuItemAutoAdd": "Voeg dit automatisch toe aan bord",
"myBoard": "Mijn bord",
"searchBoard": "Zoek borden...",
"noMatching": "Geen overeenkomende borden",
"selectBoard": "Kies een bord",
"cancel": "Annuleer",
"addBoard": "Voeg bord toe",
"bottomMessage": "Als je dit bord en alle afbeeldingen erop verwijdert, dan worden alle functies teruggezet die ervan gebruik maken.",
"uncategorized": "Zonder categorie",
"downloadBoard": "Download bord",
"changeBoard": "Wijzig bord",
"loading": "Bezig met laden...",
"clearSearch": "Maak zoekopdracht leeg"
},
"invocationCache": {
"disable": "Schakel uit",
"misses": "Mislukt cacheverzoek",
"enableFailed": "Fout bij inschakelen aanroepcache",
"invocationCache": "Aanroepcache",
"clearSucceeded": "Aanroepcache gewist",
"enableSucceeded": "Aanroepcache ingeschakeld",
"clearFailed": "Fout bij wissen aanroepcache",
"hits": "Gelukt cacheverzoek",
"disableSucceeded": "Aanroepcache uitgeschakeld",
"disableFailed": "Fout bij uitschakelen aanroepcache",
"enable": "Schakel in",
"clear": "Wis",
"maxCacheSize": "Max. grootte cache",
"cacheSize": "Grootte cache"
},
"embedding": {
"noMatchingEmbedding": "Geen overeenkomende embeddings",
"addEmbedding": "Voeg embedding toe",
"incompatibleModel": "Niet-compatibel basismodel:"
}
}

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@ -88,7 +88,9 @@
"t2iAdapter": "T2I Adapter",
"ipAdapter": "IP Adapter",
"controlAdapter": "Control Adapter",
"controlNet": "ControlNet"
"controlNet": "ControlNet",
"on": "开",
"auto": "自动"
},
"gallery": {
"generations": "生成的图像",
@ -472,7 +474,8 @@
"vae": "VAE",
"oliveModels": "Olive",
"loraModels": "LoRA",
"alpha": "Alpha"
"alpha": "Alpha",
"vaePrecision": "VAE 精度"
},
"parameters": {
"images": "图像",
@ -595,7 +598,11 @@
"useX2Model": "图像太大，无法使用 x4 模型，使用 x2 模型作为替代",
"tooLarge": "图像太大无法进行放大，请选择更小的图像"
},
"iterationsWithCount_other": "{{count}} 次迭代生成"
"iterationsWithCount_other": "{{count}} 次迭代生成",
"seamlessX&Y": "无缝 X & Y",
"aspectRatioFree": "自由",
"seamlessX": "无缝 X",
"seamlessY": "无缝 Y"
},
"settings": {
"models": "模型",
@ -628,10 +635,11 @@
"clearIntermediates": "清除中间产物",
"clearIntermediatesDesc3": "您图库中的图像不会被删除。",
"clearIntermediatesDesc2": "中间产物图像是生成过程中产生的副产品，与图库中的结果图像不同。清除中间产物可释放磁盘空间。",
"intermediatesCleared_other": "已清除 {{number}} 个中间产物",
"intermediatesCleared_other": "已清除 {{count}} 个中间产物",
"clearIntermediatesDesc1": "清除中间产物会重置您的画布和 ControlNet 状态。",
"intermediatesClearedFailed": "清除中间产物时出现问题",
"noIntermediates": "没有可清除的中间产物"
"clearIntermediatesWithCount_other": "清除 {{count}} 个中间产物",
"clearIntermediatesDisabled": "队列为空才能清理中间产物"
},
"toast": {
"tempFoldersEmptied": "临时文件夹已清空",
@ -714,7 +722,7 @@
"canvasSavedGallery": "画布已保存到图库",
"imageUploadFailed": "图像上传失败",
"problemImportingMask": "导入遮罩时出现问题",
"baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{number}} 个不兼容的子模型"
"baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{count}} 个不兼容的子模型"
},
"unifiedCanvas": {
"layer": "图层",
@ -858,7 +866,7 @@
"version": "版本",
"validateConnections": "验证连接和节点图",
"inputMayOnlyHaveOneConnection": "输入仅能有一个连接",
"notes": "节点",
"notes": "注释",
"nodeOutputs": "节点输出",
"currentImageDescription": "在节点编辑器中显示当前图像",
"validateConnectionsHelp": "防止建立无效连接和调用无效节点图",
@ -884,11 +892,11 @@
"currentImage": "当前图像",
"workflowName": "名称",
"cannotConnectInputToInput": "无法将输入连接到输入",
"workflowNotes": "节点",
"workflowNotes": "注释",
"cannotConnectOutputToOutput": "无法将输出连接到输出",
"connectionWouldCreateCycle": "连接将创建一个循环",
"cannotConnectToSelf": "无法连接自己",
"notesDescription": "添加有关您的工作流的节点",
"notesDescription": "添加有关您的工作流的注释",
"unknownField": "未知",
"colorCodeEdges": "边缘颜色编码",
"unknownNode": "未知节点",
@ -1003,7 +1011,27 @@
"booleanCollection": "布尔值合集",
"imageCollectionDescription": "一个图像合集。",
"loRAModelField": "LoRA",
"imageCollection": "图像合集"
"imageCollection": "图像合集",
"ipAdapterPolymorphicDescription": "一个 IP-Adapters Collection 合集。",
"ipAdapterCollection": "IP-Adapters 合集",
"conditioningCollection": "条件合集",
"ipAdapterPolymorphic": "IP-Adapters 多态",
"conditioningCollectionDescription": "条件可以在节点间传递。",
"colorPolymorphic": "颜色多态",
"conditioningPolymorphic": "条件多态",
"latentsCollection": "Latents 合集",
"stringPolymorphic": "字符多态",
"conditioningPolymorphicDescription": "条件可以在节点间传递。",
"imagePolymorphic": "图像多态",
"floatPolymorphic": "浮点多态",
"ipAdapterCollectionDescription": "一个 IP-Adapters Collection 合集。",
"ipAdapter": "IP-Adapter",
"booleanPolymorphic": "布尔多态",
"conditioningFieldDescription": "条件可以在节点间传递。",
"integerPolymorphic": "整数多态",
"latentsPolymorphic": "Latents 多态",
"conditioningField": "条件",
"latentsField": "Latents"
},
"controlnet": {
"resize": "直接缩放",
@ -1073,21 +1101,21 @@
"contentShuffle": "Content Shuffle",
"f": "F",
"h": "H",
"controlnet": "$t(controlnet.controlAdapter) #{{number}} ($t(common.controlNet))",
"controlnet": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.controlNet))",
"control": "Control (普通控制)",
"coarse": "Coarse",
"depthMidas": "Depth (Midas)",
"w": "W",
"ip_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.ipAdapter))",
"ip_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.ipAdapter))",
"mediapipeFace": "Mediapipe Face",
"mlsd": "M-LSD",
"lineart": "Lineart",
"t2i_adapter": "$t(controlnet.controlAdapter) #{{number}} ($t(common.t2iAdapter))",
"t2i_adapter": "$t(controlnet.controlAdapter_one) #{{number}} ($t(common.t2iAdapter))",
"megaControl": "Mega Control (超级控制)",
"depthZoe": "Depth (Zoe)",
"colorMap": "Color",
"openPose": "Openpose",
"controlAdapter": "Control Adapter",
"controlAdapter_other": "Control Adapters",
"lineartAnime": "Lineart Anime",
"canny": "Canny"
},
@ -1141,7 +1169,7 @@
"queuedCount": "{{pending}} 待处理",
"front": "前",
"pruneTooltip": "修剪 {{item_count}} 个已完成的项目",
"batchQueuedDesc": "在队列的 {{direction}} 中添加了 {{item_count}} 个会话",
"batchQueuedDesc_other": "在队列的 {{direction}} 中添加了 {{count}} 个会话",
"graphQueued": "节点图已加入队列",
"back": "后",
"session": "会话",
@ -1192,7 +1220,10 @@
"steps": "步数",
"scheduler": "调度器",
"seamless": "无缝",
"fit": "图生图适应"
"fit": "图生图匹配",
"recallParameters": "召回参数",
"noRecallParameters": "未找到要召回的参数",
"vae": "VAE"
},
"models": {
"noMatchingModels": "无相匹配的模型",
@ -1201,7 +1232,9 @@
"noLoRAsAvailable": "无可用 LoRA",
"noModelsAvailable": "无可用模型",
"selectModel": "选择一个模型",
"selectLoRA": "选择一个 LoRA"
"selectLoRA": "选择一个 LoRA",
"noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
"noLoRAsInstalled": "无已安装的 LoRA"
},
"boards": {
"autoAddBoard": "自动添加面板",
@ -1469,5 +1502,18 @@
"clear": "清除",
"maxCacheSize": "最大缓存大小",
"cacheSize": "缓存大小"
},
"hrf": {
"enableHrf": "启用高分辨率修复",
"upscaleMethod": "放大方法",
"enableHrfTooltip": "使用较低的分辨率进行初始生成，放大到基础分辨率后进行图生图。",
"metadata": {
"strength": "高分辨率修复强度",
"enabled": "高分辨率修复已启用",
"method": "高分辨率修复方法"
},
"hrf": "高分辨率修复",
"hrfStrength": "高分辨率修复强度",
"strengthTooltip": "值越低细节越少，但可以减少部分潜在的伪影。"
}
}

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@ -1,87 +0,0 @@
# Generated axios API client
- [Generated axios API client](#generated-axios-api-client)
- [Generation](#generation)
- [Generate the API client from the nodes web server](#generate-the-api-client-from-the-nodes-web-server)
- [Generate the API client from JSON](#generate-the-api-client-from-json)
- [Getting the JSON from the nodes web server](#getting-the-json-from-the-nodes-web-server)
- [Getting the JSON with a python script](#getting-the-json-with-a-python-script)
- [Generate the API client](#generate-the-api-client)
- [The generated client](#the-generated-client)
- [API client customisation](#api-client-customisation)
This API client is generated by an [openapi code generator](https://github.com/ferdikoomen/openapi-typescript-codegen).
All files in `invokeai/frontend/web/src/services/api/` are made by the generator.
## Generation
The axios client may be generated by from the OpenAPI schema from the nodes web server, or from JSON.
### Generate the API client from the nodes web server
We need to start the nodes web server, which serves the OpenAPI schema to the generator.
1. Start the nodes web server.
```bash
# from the repo root
python scripts/invokeai-web.py
```
2. Generate the API client.
```bash
# from invokeai/frontend/web/
yarn api:web
```
### Generate the API client from JSON
The JSON can be acquired from the nodes web server, or with a python script.
#### Getting the JSON from the nodes web server
Start the nodes web server as described above, then download the file.
```bash
# from invokeai/frontend/web/
curl http://localhost:9090/openapi.json -o openapi.json
```
#### Getting the JSON with a python script
Run this python script from the repo root, so it can access the nodes server modules.
The script will output `openapi.json` in the repo root. Then we need to move it to `invokeai/frontend/web/`.
```bash
# from the repo root
python invokeai/app/util/generate_openapi_json.py
mv invokeai/app/util/openapi.json invokeai/frontend/web/services/fixtures/
```
#### Generate the API client
Now we can generate the API client from the JSON.
```bash
# from invokeai/frontend/web/
yarn api:file
```
## The generated client
The client will be written to `invokeai/frontend/web/services/api/`:
- `axios` client
- TS types
- An easily parseable schema, which we can use to generate UI
## API client customisation
The generator has a default `request.ts` file that implements a base `axios` client. The generated client uses this base client.
One shortcoming of this is base client is it does not provide response headers unless the response body is empty. To fix this, we provide our own lightly-patched `request.ts`.
To access the headers, call `getHeaders(response)` on any response from the generated api client. This function is exported from `invokeai/frontend/web/src/services/util/getHeaders.ts`.

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@ -1,21 +0,0 @@
# Events
Events via `socket.io`
## `actions.ts`
Redux actions for all socket events. Payloads all include a timestamp, and optionally some other data.
Any reducer (or middleware) can respond to the actions.
## `middleware.ts`
Redux middleware for events.
Handles dispatching the event actions. Only put logic here if it can't really go anywhere else.
For example, on connect we want to load images to the gallery if it's not populated. This requires dispatching a thunk, so we need to directly dispatch this in the middleware.
## `types.ts`
Hand-written types for the socket events. Cannot generate these from the server, but fortunately they are few and simple.

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@ -1,17 +0,0 @@
# Node Editor Design
WIP
nodes
everything in `src/features/nodes/`
have a look at `state.nodes.invocation`
- on socket connect, if no schema saved, fetch `localhost:9090/openapi.json`, save JSON to `state.nodes.schema`
- on fulfilled schema fetch, `parseSchema()` the schema. this outputs a `Record<string, Invocation>` which is saved to `state.nodes.invocations` - `Invocation` is like a template for the node
- when you add a node, the the `Invocation` template is passed to `InvocationComponent.tsx` to build the UI component for that node
- inputs/outputs have field types - and each field type gets an `FieldComponent` which includes a dispatcher to write state changes to redux `nodesSlice`
- `reactflow` sends changes to nodes/edges to redux
- to invoke, `buildNodesGraph()` state, then send this
- changed onClick Invoke button actions to build the schema, then when schema builds it dispatches the actual network request to create the session - see `session.ts`

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@ -1,17 +0,0 @@
# Package Scripts
WIP walkthrough of `package.json` scripts.
## `theme` & `theme:watch`
These run the Chakra CLI to generate types for the theme, or watch for code change and re-generate the types.
The CLI essentially monkeypatches Chakra's files in `node_modules`.
## `postinstall`
The `postinstall` script patches a few packages and runs the Chakra CLI to generate types for the theme.
### Patch `@chakra-ui/cli`
See: <https://github.com/chakra-ui/chakra-ui/issues/7394>

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@ -1,43 +1,118 @@
# InvokeAI Web UI
<!-- @import "[TOC]" {cmd="toc" depthFrom=1 depthTo=6 orderedList=false} -->
<!-- code_chunk_output -->
- [InvokeAI Web UI](#invokeai-web-ui)
- [Stack](#stack)
- [Core Libraries](#core-libraries)
- [Redux Toolkit](#redux-toolkit)
- [Socket\.IO](#socketio)
- [Chakra UI](#chakra-ui)
- [KonvaJS](#konvajs)
- [Vite](#vite)
- [i18next & Weblate](#i18next--weblate)
- [openapi-typescript](#openapi-typescript)
- [reactflow](#reactflow)
- [zod](#zod)
- [Client Types Generation](#client-types-generation)
- [Package Scripts](#package-scripts)
- [Contributing](#contributing)
- [Dev Environment](#dev-environment)
- [VSCode Remote Dev](#vscode-remote-dev)
- [Production builds](#production-builds)
The UI is a fairly straightforward Typescript React app. The only really fancy stuff is the Unified Canvas.
<!-- /code_chunk_output -->
Code in `invokeai/frontend/web/` if you want to have a look.
The UI is a fairly straightforward Typescript React app.
## Stack
## Core Libraries
State management is Redux via [Redux Toolkit](https://github.com/reduxjs/redux-toolkit). We lean heavily on RTK:
- `createAsyncThunk` for HTTP requests
- `createEntityAdapter` for fetching images and models
- `createListenerMiddleware` for workflows
InvokeAI's UI is made possible by a number of excellent open-source libraries. The most heavily-used are listed below, but there are many others.
The API client and associated types are generated from the OpenAPI schema. See API_CLIENT.md.
### Redux Toolkit
Communication with server is a mix of HTTP and [socket.io](https://github.com/socketio/socket.io-client) (with a simple socket.io redux middleware to help).
[Redux Toolkit] is used for state management and fetching/caching:
[Chakra-UI](https://github.com/chakra-ui/chakra-ui) for components and styling.
- `RTK-Query` for data fetching and caching
- `createAsyncThunk` for a couple other HTTP requests
- `createEntityAdapter` to normalize things like images and models
- `createListenerMiddleware` for async workflows
[Konva](https://github.com/konvajs/react-konva) for the canvas, but we are pushing the limits of what is feasible with it (and HTML canvas in general). We plan to rebuild it with [PixiJS](https://github.com/pixijs/pixijs) to take advantage of WebGL's improved raster handling.
We use [redux-remember] for persistence.
[Vite](https://vitejs.dev/) for bundling.
### Socket\.IO
Localisation is via [i18next](https://github.com/i18next/react-i18next), but translation happens on our [Weblate](https://hosted.weblate.org/engage/invokeai/) project. Only the English source strings should be changed on this repo.
[Socket.IO] is used for server-to-client events, like generation process and queue state changes.
### Chakra UI
[Chakra UI] is our primary UI library, but we also use a few components from [Mantine v6].
### KonvaJS
[KonvaJS] powers the canvas. In the future, we'd like to explore [PixiJS] or WebGPU.
### Vite
[Vite] is our bundler.
### i18next & Weblate
We use [i18next] for localization, but translation to languages other than English happens on our [Weblate] project. **Only the English source strings should be changed on this repo.**
### openapi-typescript
[openapi-typescript] is used to generate types from the server's OpenAPI schema. See TYPES_CODEGEN.md.
### reactflow
[reactflow] powers the Workflow Editor.
### zod
[zod] schemas are used to model data structures and provide runtime validation.
## Client Types Generation
We use [openapi-typescript] to generate types from the app's OpenAPI schema.
The generated types are written to `invokeai/frontend/web/src/services/api/schema.d.ts`. This file is committed to the repo.
The server must be started and available at <http://127.0.0.1:9090>.
```sh
# from the repo root, start the server
python scripts/invokeai-web.py
# from invokeai/frontend/web/, run the script
yarn typegen
```
## Package Scripts
See `package.json` for all scripts.
Run with `yarn <script name>`.
- `dev`: run the frontend in dev mode, enabling hot reloading
- `build`: run all checks (madge, eslint, prettier, tsc) and then build the frontend
- `typegen`: generate types from the OpenAPI schema (see [Client Types Generation](#client-types-generation))
- `lint:madge`: check frontend for circular dependencies
- `lint:eslint`: check frontend for code quality
- `lint:prettier`: check frontend for code formatting
- `lint:tsc`: check frontend for type issues
- `lint`: run all checks concurrently
- `fix`: run `eslint` and `prettier`, fixing fixable issues
## Contributing
Thanks for your interest in contributing to the InvokeAI Web UI!
We encourage you to ping @psychedelicious and @blessedcoolant on [Discord](https://discord.gg/ZmtBAhwWhy) if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
We encourage you to ping @psychedelicious and @blessedcoolant on [discord] if you want to contribute, just to touch base and ensure your work doesn't conflict with anything else going on. The project is very active.
### Dev Environment
Install [node](https://nodejs.org/en/download/) and [yarn classic](https://classic.yarnpkg.com/lang/en/).
Install [node] and [yarn classic].
From `invokeai/frontend/web/` run `yarn install` to get everything set up.
@ -60,3 +135,20 @@ For a number of technical and logistical reasons, we need to commit UI build art
If you submit a PR, there is a good chance we will ask you to include a separate commit with a build of the app.
To build for production, run `yarn build`.
[node]: https://nodejs.org/en/download/
[yarn classic]: https://classic.yarnpkg.com/lang/en/
[discord]: https://discord.gg/ZmtBAhwWhy
[Redux Toolkit]: https://github.com/reduxjs/redux-toolkit
[redux-remember]: https://github.com/zewish/redux-remember
[Socket.IO]: https://github.com/socketio/socket.io
[Chakra UI]: https://github.com/chakra-ui/chakra-ui
[Mantine v6]: https://v6.mantine.dev/
[KonvaJS]: https://github.com/konvajs/react-konva
[PixiJS]: https://github.com/pixijs/pixijs
[Vite]: https://github.com/vitejs/vite
[i18next]: https://github.com/i18next/react-i18next
[Weblate]: https://hosted.weblate.org/engage/invokeai/
[openapi-typescript]: https://github.com/drwpow/openapi-typescript
[reactflow]: https://github.com/xyflow/xyflow
[zod]: https://github.com/colinhacks/zod

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@ -0,0 +1,350 @@
# Workflows - Design and Implementation
<!-- @import "[TOC]" {cmd="toc" depthFrom=1 depthTo=6 orderedList=false} -->
<!-- code_chunk_output -->
- [Workflows - Design and Implementation](#workflows---design-and-implementation)
- [Design](#design)
- [Linear UI](#linear-ui)
- [Workflow Editor](#workflow-editor)
- [Workflows](#workflows)
- [Workflow -> reactflow state -> InvokeAI graph](#workflow---reactflow-state---invokeai-graph)
- [Nodes vs Invocations](#nodes-vs-invocations)
- [Workflow Linear View](#workflow-linear-view)
- [OpenAPI Schema](#openapi-schema)
- [Field Instances and Templates](#field-instances-and-templates)
- [Stateful vs Stateless Fields](#stateful-vs-stateless-fields)
- [Collection and Polymorphic Fields](#collection-and-polymorphic-fields)
- [Implementation](#implementation)
- [zod Schemas and Types](#zod-schemas-and-types)
- [OpenAPI Schema Parsing](#openapi-schema-parsing)
- [Parsing Field Types](#parsing-field-types)
- [Primitive Types](#primitive-types)
- [Complex Types](#complex-types)
- [Collection Types](#collection-types)
- [Polymorphic Types](#polymorphic-types)
- [Optional Fields](#optional-fields)
- [Building Field Input Templates](#building-field-input-templates)
- [Building Field Output Templates](#building-field-output-templates)
- [Managing reactflow State](#managing-reactflow-state)
- [Building Nodes and Edges](#building-nodes-and-edges)
- [Building a Workflow](#building-a-workflow)
- [Loading a Workflow](#loading-a-workflow)
- [Workflow Migrations](#workflow-migrations)
<!-- /code_chunk_output -->
> This document describes, at a high level, the design and implementation of workflows in the InvokeAI frontend. There are a substantial number of implementation details not included, but which are hopefully clear from the code.
InvokeAI's backend uses graphs, composed of **nodes** and **edges**, to process data and generate images.
Nodes have any number of **input fields** and **output fields**. Edges connect nodes together via their inputs and outputs. Fields have data types which dictate how they may be connected.
During execution, a nodes' outputs may be passed along to any number of other nodes' inputs.
Workflows are an enriched abstraction over a graph.
## Design
InvokeAI provide two ways to build graphs in the frontend: the [Linear UI](#linear-ui) and [Workflow Editor](#workflow-editor).
To better understand the use case and challenges related to workflows, we will review both of these modes.
### Linear UI
This includes the **Text to Image**, **Image to Image** and **Unified Canvas** tabs.
The user-managed parameters on these tabs are stored as simple objects in the application state. When the user invokes, adding a generation to the queue, we internally build a graph from these parameters.
This logic can be fairly complex due to the range of features available and their interactions. Depending on the parameters selected, the graph may be very different. Building graphs in code can be challenging - you are trying to construct a non-linear structure in a linear context.
The simplest graph building logic is for **Text to Image** with a SD1.5 model: [buildLinearTextToImageGraph.ts]
There are many other graph builders in the same directory for different tabs or base models (e.g. SDXL). Some are pretty hairy.
In the Linear UI, we go straight from **simple application state** to **graph** via these builders.
### Workflow Editor
The Workflow Editor is a visual graph editor, allowing users to draw edges from node to node to construct a graph. This _far_ more approachable way to create complex graphs.
InvokeAI uses the [reactflow] library to power the Workflow Editor. It provides both a graph editor UI and manages its own internal graph state.
#### Workflows
A workflow is a representation of a graph plus additional metadata:
- Name
- Description
- Version
- Notes
- [Exposed fields](#workflow-linear-view)
- Author, tags, category, etc.
Workflows should have other qualities:
- Portable: you should be able to load a workflow created by another person.
- Resilient: you should be able to "upgrade" a workflow as the application changes.
- Abstract: as much as is possible, workflows should not be married to the specific implementation details of the application.
To support these qualities, workflows are serializable, have a versioned schemas, and represent graphs as minimally as possible. Fortunately, the reactflow state for nodes and edges works perfectly for this.
##### Workflow -> reactflow state -> InvokeAI graph
Given a workflow, we need to be able to derive reactflow state and/or an InvokeAI graph from it.
The first step - workflow to reactflow state - is very simple. The logic is in [nodesSlice.ts], in the `workflowLoaded` reducer.
The reactflow state is, however, structurally incompatible with our backend's graph structure. When a user invokes on a Workflow, we need to convert the reactflow state into an InvokeAI graph. This is far simpler than the graph building logic from the Linear UI:
[buildNodesGraph.ts]
##### Nodes vs Invocations
We often use the terms "node" and "invocation" interchangeably, but they may refer to different things in the frontend.
reactflow [has its own definitions][reactflow-concepts] of "node", "edge" and "handle" which are closely related to InvokeAI graph concepts.
- A reactflow node is related to an InvokeAI invocation. It has a "data" property, which holds the InvokeAI-specific invocation data.
- A reactflow edge is roughly equivalent to an InvokeAI edge.
- A reactflow handle is roughly equivalent to an InvokeAI input or output field.
##### Workflow Linear View
Graphs are very capable data structures, but not everyone wants to work with them all the time.
To allow less technical users - or anyone who wants a less visually noisy workspace - to benefit from the power of nodes, InvokeAI has a workflow feature called the Linear View.
A workflow input field can be added to this Linear View, and its input component can be presented similarly to the Linear UI tabs. Internally, we add the field to the workflow's list of exposed fields.
#### OpenAPI Schema
OpenAPI is a schema specification that can represent complex data structures and relationships. The backend is capable of generating an OpenAPI schema for all invocations.
When the UI connects, it requests this schema and parses each invocation into an **invocation template**. Invocation templates have a number of properties, like title, description and type, but the most important ones are their input and output **field templates**.
Invocation and field templates are the "source of truth" for graphs, because they indicate what the backend is able to process.
When a user adds a new node to their workflow, these templates are used to instantiate a node with fields instantiated from the input and output field templates.
##### Field Instances and Templates
Field templates consist of:
- Name: the identifier of the field, its variable name in python
- Type: derived from the field's type annotation in python (e.g. IntegerField, ImageField, MainModelField)
- Constraints: derived from the field's creation args in python (e.g. minimum value for an integer)
- Default value: optionally provided in the field's creation args (e.g. 42 for an integer)
Field instances are created from the templates and have name, type and optionally a value.
The type of the field determines the UI components that are rendered for it.
A field instance's name associates it with its template.
##### Stateful vs Stateless Fields
**Stateful** fields store their value in the frontend graph. Think primitives, model identifiers, images, etc. Fields are only stateful if the frontend allows the user to directly input a value for them.
Many field types, however, are **stateless**. An example is a `UNetField`, which contains some data describing a UNet. Users cannot directly provide this data - it is created and consumed in the backend.
Stateless fields do not store their value in the node, so their field instances do not have values.
"Custom" fields will always be treated as stateless fields.
##### Collection and Polymorphic Fields
Field types have a name and two flags which may identify it as a **collection** or **polymorphic** field.
If a field is annotated in python as a list, its field type is parsed and flagged as a collection type (e.g. `list[int]`).
If it is annotated as a union of a type and list, the type will be flagged as a polymorphic type (e.g. `Union[int, list[int]]`). Fields may not be unions of different types (e.g. `Union[int, list[str]]` and `Union[int, str]` are not allowed).
## Implementation
The majority of data structures in the backend are [pydantic] models. Pydantic provides OpenAPI schemas for all models and we then generate TypeScript types from those.
The OpenAPI schema is parsed at runtime into our invocation templates.
Workflows and all related data are modeled in the frontend using [zod]. Related types are inferred from the zod schemas.
> In python, invocations are pydantic models with fields. These fields become node inputs. The invocation's `invoke()` function returns a pydantic model - its output. Like the invocation itself, the output model has any number of fields, which become node outputs.
### zod Schemas and Types
The zod schemas, inferred types, and type guards are in [types/].
Roughly order from lowest-level to highest:
- `common.ts`: stateful field data, and couple other misc types
- `field.ts`: fields - types, values, instances, templates
- `invocation.ts`: invocations and other node types
- `workflow.ts`: workflows and constituents
We customize the OpenAPI schema to include additional properties on invocation and field schemas. To facilitate parsing this schema into templates, we modify/wrap the types from [openapi-types] in `openapi.ts`.
### OpenAPI Schema Parsing
The entrypoint for OpenAPI schema parsing is [parseSchema.ts].
General logic flow:
- Iterate over all invocation schema objects
- Extract relevant invocation-level attributes (e.g. title, type, version, etc)
- Iterate over the invocation's input fields
- [Parse each field's type](#parsing-field-types)
- [Build a field input template](#building-field-input-templates) from the type - either a stateful template or "generic" stateless template
- Iterate over the invocation's output fields
- Parse the field's type (same as inputs)
- [Build a field output template](#building-field-output-templates)
- Assemble the attributes and fields into an invocation template
Most of these involve very straightforward `reduce`s, but the less intuitive steps are detailed below.
#### Parsing Field Types
Field types are represented as structured objects:
```ts
type FieldType = {
name: string;
isCollection: boolean;
isCollectionOrScalar: boolean;
};
```
The parsing logic is in `parseFieldType.ts`.
There are 4 general cases for field type parsing.
##### Primitive Types
When a field is annotated as a primitive values (e.g. `int`, `str`, `float`), the field type parsing is fairly straightforward. The field is represented by a simple OpenAPI **schema object**, which has a `type` property.
We create a field type name from this `type` string (e.g. `string` -> `StringField`).
##### Complex Types
When a field is annotated as a pydantic model (e.g. `ImageField`, `MainModelField`, `ControlField`), it is represented as a **reference object**. Reference objects are pointers to another schema or reference object within the schema.
We need to **dereference** the schema to pull these out. Dereferencing may require recursion. We use the reference object's name directly for the field type name.
> Unfortunately, at this time, we've had limited success using external libraries to deference at runtime, so we do this ourselves.
##### Collection Types
When a field is annotated as a list of a single type, the schema object has an `items` property. They may be a schema object or reference object and must be parsed to determine the item type.
We use the item type for field type name, adding `isCollection: true` to the field type.
##### Collection or Scalar Types
When a field is annotated as a union of a type and list of that type, the schema object has an `anyOf` property, which holds a list of valid types for the union.
After verifying that the union has two members (a type and list of the same type), we use the type for field type name, adding `isCollectionOrScalar: true` to the field type.
##### Optional Fields
In OpenAPI v3.1, when an object is optional, it is put into an `anyOf` along with a primitive schema object with `type: 'null'`.
Handling this adds a fair bit of complexity, as we now must filter out the `'null'` types and work with the remaining types as described above.
If there is a single remaining schema object, we must recursively call to `parseFieldType()` to get parse it.
#### Building Field Input Templates
Now that we have a field type, we can build an input template for the field.
Stateful fields all get a function to build their template, while stateless fields are constructed directly. This is possible because stateless fields have no default value or constraints.
See [buildFieldInputTemplate.ts].
#### Building Field Output Templates
Field outputs are similar to stateless fields - they do not have any value in the frontend. When building their templates, we don't need a special function for each field type.
See [buildFieldOutputTemplate.ts].
### Managing reactflow State
As described above, the workflow editor state is the essentially the reactflow state, plus some extra metadata.
We provide reactflow with an array of nodes and edges via redux, and a number of [event handlers][reactflow-events]. These handlers dispatch redux actions, managing nodes and edges.
The pieces of redux state relevant to workflows are:
- `state.nodes.nodes`: the reactflow nodes state
- `state.nodes.edges`: the reactflow edges state
- `state.nodes.workflow`: the workflow metadata
#### Building Nodes and Edges
A reactflow node has a few important top-level properties:
- `id`: unique identifier
- `type`: a string that maps to a react component to render the node
- `position`: XY coordinates
- `data`: arbitrary data
When the user adds a node, we build **invocation node data**, storing it in `data`. Invocation properties (e.g. type, version, label, etc.) are copied from the invocation template. Inputs and outputs are built from the invocation template's field templates.
See [buildInvocationNode.ts].
Edges are managed by reactflow, but briefly, they consist of:
- `source`: id of the source node
- `sourceHandle`: id of the source node handle (output field)
- `target`: id of the target node
- `targetHandle`: id of the target node handle (input field)
> Edge creation is gated behind validation logic. This validation compares the input and output field types and overall graph state.
#### Building a Workflow
Building a workflow entity is as simple as dropping the nodes, edges and metadata into an object.
Each node and edge is parsed with a zod schema, which serves to strip out any unneeded data.
See [buildWorkflow.ts].
#### Loading a Workflow
Workflows may be loaded from external sources or the user's local instance. In all cases, the workflow needs to be handled with care, as an untrusted object.
Loading has a few stages which may throw or warn if there are problems:
- Parsing the workflow data structure itself, [migrating](#workflow-migrations) it if necessary (throws)
- Check for a template for each node (warns)
- Check each node's version against its template (warns)
- Validate the source and target of each edge (warns)
This validation occurs in [validateWorkflow.ts].
If there are no fatal errors, the workflow is then stored in redux state.
### Workflow Migrations
When the workflow schema changes, we may need to perform some data migrations. This occurs as workflows are loaded. zod schemas for each workflow schema version is retained to facilitate migrations.
Previous schemas are in folders in `invokeai/frontend/web/src/features/nodes/types/`, eg `v1/`.
Migration logic is in [migrations.ts].
<!-- links -->
[pydantic]: https://github.com/pydantic/pydantic 'pydantic'
[zod]: https://github.com/colinhacks/zod 'zod'
[openapi-types]: https://github.com/kogosoftwarellc/open-api/tree/main/packages/openapi-types 'openapi-types'
[reactflow]: https://github.com/xyflow/xyflow 'reactflow'
[reactflow-concepts]: https://reactflow.dev/learn/concepts/terms-and-definitions
[reactflow-events]: https://reactflow.dev/api-reference/react-flow#event-handlers
[buildWorkflow.ts]: ../src/features/nodes/util/workflow/buildWorkflow.ts
[nodesSlice.ts]: ../src/features/nodes/store/nodesSlice.ts
[buildLinearTextToImageGraph.ts]: ../src/features/nodes/util/graph/buildLinearTextToImageGraph.ts
[buildNodesGraph.ts]: ../src/features/nodes/util/graph/buildNodesGraph.ts
[buildInvocationNode.ts]: ../src/features/nodes/util/node/buildInvocationNode.ts
[validateWorkflow.ts]: ../src/features/nodes/util/workflow/validateWorkflow.ts
[migrations.ts]: ../src/features/nodes/util/workflow/migrations.ts
[parseSchema.ts]: ../src/features/nodes/util/schema/parseSchema.ts
[buildFieldInputTemplate.ts]: ../src/features/nodes/util/schema/buildFieldInputTemplate.ts
[buildFieldOutputTemplate.ts]: ../src/features/nodes/util/schema/buildFieldOutputTemplate.ts

6
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@ -19,7 +19,6 @@
"dist"
],
"scripts": {
"prepare": "cd ../../../ && husky install invokeai/frontend/web/.husky",
"dev": "concurrently \"vite dev\" \"yarn run theme:watch\"",
"dev:host": "concurrently \"vite dev --host\" \"yarn run theme:watch\"",
"build": "yarn run lint && vite build",
@ -30,7 +29,7 @@
"lint:prettier": "prettier --check .",
"lint:tsc": "tsc --noEmit",
"lint": "concurrently -g -n eslint,prettier,tsc,madge -c cyan,green,magenta,yellow \"yarn run lint:eslint\" \"yarn run lint:prettier\" \"yarn run lint:tsc\" \"yarn run lint:madge\"",
"fix": "eslint --fix . && prettier --loglevel warn --write . && tsc --noEmit",
"fix": "eslint --fix . && prettier --log-level warn --write .",
"lint-staged": "lint-staged",
"postinstall": "patch-package && yarn run theme",
"theme": "chakra-cli tokens src/theme/theme.ts",
@ -81,7 +80,6 @@
"lodash-es": "^4.17.21",
"nanostores": "^0.9.4",
"new-github-issue-url": "^1.0.0",
"openapi-fetch": "^0.8.1",
"overlayscrollbars": "^2.4.4",
"overlayscrollbars-react": "^0.5.3",
"patch-package": "^8.0.0",
@ -134,6 +132,8 @@
"concurrently": "^8.2.2",
"eslint": "^8.53.0",
"eslint-config-prettier": "^9.0.0",
"eslint-plugin-i18next": "^6.0.3",
"eslint-plugin-path": "^1.2.2",
"eslint-plugin-react": "^7.33.2",
"eslint-plugin-react-hooks": "^4.6.0",
"husky": "^8.0.3",

198
invokeai/frontend/web/public/locales/de.json
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@ -90,7 +90,16 @@
"openInNewTab": "In einem neuem Tab öffnen",
"statusProcessing": "wird bearbeitet",
"linear": "Linear",
"imagePrompt": "Bild Prompt"
"imagePrompt": "Bild Prompt",
"checkpoint": "Checkpoint",
"inpaint": "inpaint",
"simple": "Einfach",
"template": "Vorlage",
"outputs": "Ausgabe",
"data": "Daten",
"safetensors": "Safetensors",
"outpaint": "outpaint",
"details": "Details"
},
"gallery": {
"generations": "Erzeugungen",
@ -110,10 +119,17 @@
"preparingDownload": "bereite Download vor",
"preparingDownloadFailed": "Problem beim Download vorbereiten",
"deleteImage": "Lösche Bild",
"images": "Bilder",
"copy": "Kopieren",
"download": "Runterladen",
"setCurrentImage": "Setze aktuelle Bild"
"setCurrentImage": "Setze aktuelle Bild",
"featuresWillReset": "Wenn Sie dieses Bild löschen, werden diese Funktionen sofort zurückgesetzt.",
"deleteImageBin": "Gelöschte Bilder werden an den Papierkorb Ihres Betriebssystems gesendet.",
"unableToLoad": "Galerie kann nicht geladen werden",
"downloadSelection": "Auswahl herunterladen",
"currentlyInUse": "Dieses Bild wird derzeit in den folgenden Funktionen verwendet:",
"deleteImagePermanent": "Gelöschte Bilder können nicht wiederhergestellt werden.",
"autoAssignBoardOnClick": "Board per Klick automatisch zuweisen",
"noImageSelected": "Kein Bild ausgewählt"
},
"hotkeys": {
"keyboardShortcuts": "Tastenkürzel",
@ -323,7 +339,8 @@
},
"nodesHotkeys": "Knoten Tastenkürzel",
"addNodes": {
"title": "Knotenpunkt hinzufügen"
"title": "Knotenpunkt hinzufügen",
"desc": "Öffnet das Menü zum Hinzufügen von Knoten"
}
},
"modelManager": {
@ -429,7 +446,46 @@
"customConfigFileLocation": "Benutzerdefinierte Konfiguration Datei Speicherort",
"baseModel": "Basis Modell",
"convertToDiffusers": "Konvertiere zu Diffusers",
"diffusersModels": "Diffusers"
"diffusersModels": "Diffusers",
"noCustomLocationProvided": "Kein benutzerdefinierter Standort angegeben",
"onnxModels": "Onnx",
"vaeRepoID": "VAE-Repo-ID",
"weightedSum": "Gewichtete Summe",
"syncModelsDesc": "Wenn Ihre Modelle nicht mit dem Backend synchronisiert sind, können Sie sie mit dieser Option aktualisieren. Dies ist im Allgemeinen praktisch, wenn Sie Ihre models.yaml-Datei manuell aktualisieren oder Modelle zum InvokeAI-Stammordner hinzufügen, nachdem die Anwendung gestartet wurde.",
"vae": "VAE",
"noModels": "Keine Modelle gefunden",
"statusConverting": "Konvertieren",
"sigmoid": "Sigmoid",
"predictionType": "Vorhersagetyp (für Stable Diffusion 2.x-Modelle und gelegentliche Stable Diffusion 1.x-Modelle)",
"selectModel": "Wählen Sie Modell aus",
"repo_id": "Repo-ID",
"modelSyncFailed": "Modellsynchronisierung fehlgeschlagen",
"quickAdd": "Schnell hinzufügen",
"simpleModelDesc": "Geben Sie einen Pfad zu einem lokalen Diffusers-Modell, einem lokalen Checkpoint-/Safetensors-Modell, einer HuggingFace-Repo-ID oder einer Checkpoint-/Diffusers-Modell-URL an.",
"modelDeleted": "Modell gelöscht",
"inpainting": "v1 Inpainting",
"modelUpdateFailed": "Modellaktualisierung fehlgeschlagen",
"useCustomConfig": "Benutzerdefinierte Konfiguration verwenden",
"settings": "Einstellungen",
"modelConversionFailed": "Modellkonvertierung fehlgeschlagen",
"syncModels": "Modelle synchronisieren",
"mergedModelSaveLocation": "Speicherort",
"modelType": "Modelltyp",
"modelsMerged": "Modelle zusammengeführt",
"modelsMergeFailed": "Modellzusammenführung fehlgeschlagen",
"convertToDiffusersHelpText1": "Dieses Modell wird in das 🧨 Diffusers-Format konvertiert.",
"modelsSynced": "Modelle synchronisiert",
"vaePrecision": "VAE-Präzision",
"mergeModels": "Modelle zusammenführen",
"interpolationType": "Interpolationstyp",
"oliveModels": "Olives",
"variant": "Variante",
"loraModels": "LoRAs",
"modelDeleteFailed": "Modell konnte nicht gelöscht werden",
"mergedModelName": "Zusammengeführter Modellname",
"checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
"formMessageDiffusersModelLocation": "Diffusers Modell Speicherort",
"noModelSelected": "Kein Modell ausgewählt"
},
"parameters": {
"images": "Bilder",
@ -639,7 +695,8 @@
"exitViewer": "Betrachten beenden",
"menu": "Menü",
"loadMore": "Mehr laden",
"invokeProgressBar": "Invoke Fortschrittsanzeige"
"invokeProgressBar": "Invoke Fortschrittsanzeige",
"mode": "Modus"
},
"boards": {
"autoAddBoard": "Automatisches Hinzufügen zum Ordner",
@ -657,7 +714,11 @@
"changeBoard": "Ordner wechseln",
"loading": "Laden...",
"clearSearch": "Suche leeren",
"bottomMessage": "Durch das Löschen dieses Ordners und seiner Bilder werden alle Funktionen zurückgesetzt, die sie derzeit verwenden."
"bottomMessage": "Durch das Löschen dieses Ordners und seiner Bilder werden alle Funktionen zurückgesetzt, die sie derzeit verwenden.",
"deleteBoardOnly": "Nur Ordner löschen",
"deleteBoard": "Löschen Ordner",
"deleteBoardAndImages": "Löschen Ordner und Bilder",
"deletedBoardsCannotbeRestored": "Gelöschte Ordner könnte nicht wiederhergestellt werden"
},
"controlnet": {
"showAdvanced": "Zeige Erweitert",
@ -716,7 +777,34 @@
"saveControlImage": "Speichere Referenz Bild",
"safe": "Speichern",
"ipAdapterImageFallback": "Kein IP Adapter Bild ausgewählt",
"resetIPAdapterImage": "Zurücksetzen vom IP Adapter Bild"
"resetIPAdapterImage": "Zurücksetzen vom IP Adapter Bild",
"pidi": "PIDI",
"normalBae": "Normales BAE",
"mlsdDescription": "Minimalistischer Liniensegmentdetektor",
"openPoseDescription": "Schätzung der menschlichen Pose mit Openpose",
"control": "Kontrolle",
"coarse": "Coarse",
"crop": "Zuschneiden",
"pidiDescription": "PIDI-Bildverarbeitung",
"mediapipeFace": "Mediapipe Gesichter",
"mlsd": "M-LSD",
"controlMode": "Steuermodus",
"cannyDescription": "Canny Ecken Erkennung",
"lineart": "Lineart",
"lineartAnimeDescription": "Lineart-Verarbeitung im Anime-Stil",
"minConfidence": "Minimales Vertrauen",
"megaControl": "Mega-Kontrolle",
"autoConfigure": "Prozessor automatisch konfigurieren",
"normalBaeDescription": "Normale BAE-Verarbeitung",
"noneDescription": "Es wurde keine Verarbeitung angewendet",
"openPose": "Openpose",
"lineartAnime": "Lineart Anime",
"mediapipeFaceDescription": "Gesichtserkennung mit Mediapipe",
"canny": "Canny",
"hedDescription": "Ganzheitlich verschachtelte Kantenerkennung",
"scribble": "Scribble",
"maxFaces": "Maximal Anzahl Gesichter",
"unstarImage": "Markierung aufheben"
},
"queue": {
"status": "Status",
@ -758,7 +846,20 @@
"enqueueing": "Stapel in der Warteschlange",
"queueMaxExceeded": "Maximum von {{max_queue_size}} Elementen erreicht, würde {{skip}} Elemente überspringen",
"cancelBatchFailed": "Problem beim Abbruch vom Stapel",
"clearQueueAlertDialog2": "bist du sicher die Warteschlange zu leeren?"
"clearQueueAlertDialog2": "bist du sicher die Warteschlange zu leeren?",
"pruneSucceeded": "{{item_count}} abgeschlossene Elemente aus der Warteschlange entfernt",
"pauseSucceeded": "Prozessor angehalten",
"cancelFailed": "Problem beim Stornieren des Auftrags",
"pauseFailed": "Problem beim Anhalten des Prozessors",
"front": "Vorne",
"pruneTooltip": "Bereinigen Sie {{item_count}} abgeschlossene Aufträge",
"resumeFailed": "Problem beim wieder aufnehmen von Prozessor",
"pruneFailed": "Problem beim leeren der Warteschlange",
"pauseTooltip": "Pause von Prozessor",
"back": "Hinten",
"resumeSucceeded": "Prozessor wieder aufgenommen",
"resumeTooltip": "Prozessor wieder aufnehmen",
"time": "Zeit"
},
"metadata": {
"negativePrompt": "Negativ Beschreibung",
@ -773,7 +874,21 @@
"noMetaData": "Keine Meta-Data gefunden",
"width": "Breite",
"createdBy": "Erstellt von",
"steps": "Schritte"
"steps": "Schritte",
"seamless": "Nahtlos",
"positivePrompt": "Positiver Prompt",
"generationMode": "Generierungsmodus",
"Threshold": "Noise Schwelle",
"seed": "Samen",
"perlin": "Perlin Noise",
"hiresFix": "Optimierung für hohe Auflösungen",
"initImage": "Erstes Bild",
"variations": "Samengewichtspaare",
"vae": "VAE",
"workflow": "Arbeitsablauf",
"scheduler": "Scheduler",
"noRecallParameters": "Es wurden keine Parameter zum Abrufen gefunden",
"recallParameters": "Recall Parameters"
},
"popovers": {
"noiseUseCPU": {
@ -811,11 +926,72 @@
"misses": "Cache Nötig",
"hits": "Cache Treffer",
"enable": "Aktivieren",
"clear": "Leeren"
"clear": "Leeren",
"maxCacheSize": "Maximale Cache Größe",
"cacheSize": "Cache Größe"
},
"embedding": {
"noMatchingEmbedding": "Keine passenden Embeddings",
"addEmbedding": "Embedding hinzufügen",
"incompatibleModel": "Inkompatibles Basismodell:"
},
"nodes": {
"booleanPolymorphicDescription": "Eine Sammlung boolescher Werte.",
"colorFieldDescription": "Eine RGBA-Farbe.",
"conditioningCollection": "Konditionierungssammlung",
"addNode": "Knoten hinzufügen",
"conditioningCollectionDescription": "Konditionierung kann zwischen Knoten weitergegeben werden.",
"colorPolymorphic": "Farbpolymorph",
"colorCodeEdgesHelp": "Farbkodieren Sie Kanten entsprechend ihren verbundenen Feldern",
"animatedEdges": "Animierte Kanten",
"booleanCollectionDescription": "Eine Sammlung boolescher Werte.",
"colorField": "Farbe",
"collectionItem": "Objekt in Sammlung",
"animatedEdgesHelp": "Animieren Sie ausgewählte Kanten und Kanten, die mit ausgewählten Knoten verbunden sind",
"cannotDuplicateConnection": "Es können keine doppelten Verbindungen erstellt werden",
"booleanPolymorphic": "Boolesche Polymorphie",
"colorPolymorphicDescription": "Eine Sammlung von Farben.",
"clipFieldDescription": "Tokenizer- und text_encoder-Untermodelle.",
"clipField": "Clip",
"colorCollection": "Eine Sammlung von Farben.",
"boolean": "Boolesche Werte",
"currentImage": "Aktuelles Bild",
"booleanDescription": "Boolesche Werte sind wahr oder falsch.",
"collection": "Sammlung",
"cannotConnectInputToInput": "Eingang kann nicht mit Eingang verbunden werden",
"conditioningField": "Konditionierung",
"cannotConnectOutputToOutput": "Ausgang kann nicht mit Ausgang verbunden werden",
"booleanCollection": "Boolesche Werte Sammlung",
"cannotConnectToSelf": "Es kann keine Verbindung zu sich selbst hergestellt werden",
"colorCodeEdges": "Farbkodierte Kanten",
"addNodeToolTip": "Knoten hinzufügen (Umschalt+A, Leertaste)",
"boardField": "Ordner",
"boardFieldDescription": "Ein Galerie Ordner"
},
"hrf": {
"enableHrf": "Aktivieren Sie die Korrektur für hohe Auflösungen",
"upscaleMethod": "Vergrößerungsmethoden",
"enableHrfTooltip": "Generieren Sie mit einer niedrigeren Anfangsauflösung, skalieren Sie auf die Basisauflösung hoch und führen Sie dann Image-to-Image aus.",
"metadata": {
"strength": "Hochauflösender Fix Stärke",
"enabled": "Hochauflösender Fix aktiviert",
"method": "Hochauflösender Fix Methode"
},
"hrf": "Hochauflösender Fix",
"hrfStrength": "Hochauflösende Fix Stärke",
"strengthTooltip": "Niedrigere Werte führen zu weniger Details, wodurch potenzielle Artefakte reduziert werden können."
},
"models": {
"noMatchingModels": "Keine passenden Modelle",
"loading": "lade",
"noMatchingLoRAs": "Keine passenden LoRAs",
"noLoRAsAvailable": "Keine LoRAs verfügbar",
"noModelsAvailable": "Keine Modelle verfügbar",
"selectModel": "Wählen ein Modell aus",
"noRefinerModelsInstalled": "Keine SDXL Refiner-Modelle installiert",
"noLoRAsInstalled": "Keine LoRAs installiert",
"selectLoRA": "Wählen ein LoRA aus",
"esrganModel": "ESRGAN Modell",
"addLora": "LoRA hinzufügen"
}
}

107
invokeai/frontend/web/public/locales/en.json
View File

@ -1,16 +1,19 @@
{
"accessibility": {
"copyMetadataJson": "Copy metadata JSON",
"createIssue": "Create Issue",
"exitViewer": "Exit Viewer",
"flipHorizontally": "Flip Horizontally",
"flipVertically": "Flip Vertically",
"invokeProgressBar": "Invoke progress bar",
"menu": "Menu",
"mode": "Mode",
"modelSelect": "Model Select",
"modifyConfig": "Modify Config",
"nextImage": "Next Image",
"previousImage": "Previous Image",
"reset": "Reset",
"resetUI": "$t(accessibility.reset) UI",
"rotateClockwise": "Rotate Clockwise",
"rotateCounterClockwise": "Rotate Counter-Clockwise",
"showGalleryPanel": "Show Gallery Panel",
@ -30,9 +33,15 @@
"cancel": "Cancel",
"changeBoard": "Change Board",
"clearSearch": "Clear Search",
"deleteBoard": "Delete Board",
"deleteBoardAndImages": "Delete Board and Images",
"deleteBoardOnly": "Delete Board Only",
"deletedBoardsCannotbeRestored": "Deleted boards cannot be restored",
"loading": "Loading...",
"menuItemAutoAdd": "Auto-add to this Board",
"move": "Move",
"movingImagesToBoard_one": "Moving {{count}} image to board:",
"movingImagesToBoard_other": "Moving {{count}} images to board:",
"myBoard": "My Board",
"noMatching": "No matching Boards",
"searchBoard": "Search Boards...",
@ -44,27 +53,39 @@
"common": {
"accept": "Accept",
"advanced": "Advanced",
"ai": "ai",
"areYouSure": "Are you sure?",
"auto": "Auto",
"back": "Back",
"batch": "Batch Manager",
"cancel": "Cancel",
"copyError": "$t(gallery.copy) Error",
"close": "Close",
"on": "On",
"checkpoint": "Checkpoint",
"communityLabel": "Community",
"controlNet": "ControlNet",
"controlAdapter": "Control Adapter",
"data": "Data",
"details": "Details",
"ipAdapter": "IP Adapter",
"t2iAdapter": "T2I Adapter",
"darkMode": "Dark Mode",
"discordLabel": "Discord",
"dontAskMeAgain": "Don't ask me again",
"error": "Error",
"file": "File",
"folder": "Folder",
"format": "format",
"generate": "Generate",
"githubLabel": "Github",
"hotkeysLabel": "Hotkeys",
"imagePrompt": "Image Prompt",
"imageFailedToLoad": "Unable to Load Image",
"img2img": "Image To Image",
"inpaint": "inpaint",
"input": "Input",
"installed": "Installed",
"langArabic": "العربية",
"langBrPortuguese": "Português do Brasil",
"langDutch": "Nederlands",
@ -92,7 +113,10 @@
"nodeEditor": "Node Editor",
"nodes": "Workflow Editor",
"nodesDesc": "A node based system for the generation of images is under development currently. Stay tuned for updates about this amazing feature.",
"notInstalled": "Not $t(common.installed)",
"openInNewTab": "Open in New Tab",
"outpaint": "outpaint",
"outputs": "Outputs",
"postProcessDesc1": "Invoke AI offers a wide variety of post processing features. Image Upscaling and Face Restoration are already available in the WebUI. You can access them from the Advanced Options menu of the Text To Image and Image To Image tabs. You can also process images directly, using the image action buttons above the current image display or in the viewer.",
"postProcessDesc2": "A dedicated UI will be released soon to facilitate more advanced post processing workflows.",
"postProcessDesc3": "The Invoke AI Command Line Interface offers various other features including Embiggen.",
@ -100,7 +124,10 @@
"postProcessing": "Post Processing",
"random": "Random",
"reportBugLabel": "Report Bug",
"safetensors": "Safetensors",
"settingsLabel": "Settings",
"simple": "Simple",
"somethingWentWrong": "Something went wrong",
"statusConnected": "Connected",
"statusConvertingModel": "Converting Model",
"statusDisconnected": "Disconnected",
@ -127,11 +154,13 @@
"statusSavingImage": "Saving Image",
"statusUpscaling": "Upscaling",
"statusUpscalingESRGAN": "Upscaling (ESRGAN)",
"template": "Template",
"training": "Training",
"trainingDesc1": "A dedicated workflow for training your own embeddings and checkpoints using Textual Inversion and Dreambooth from the web interface.",
"trainingDesc2": "InvokeAI already supports training custom embeddourings using Textual Inversion using the main script.",
"txt2img": "Text To Image",
"unifiedCanvas": "Unified Canvas",
"unknown": "Unknown",
"upload": "Upload"
},
"controlnet": {
@ -214,6 +243,7 @@
"setControlImageDimensions": "Set Control Image Dimensions To W/H",
"showAdvanced": "Show Advanced",
"toggleControlNet": "Toggle this ControlNet",
"unstarImage": "Unstar Image",
"w": "W",
"weight": "Weight",
"enableIPAdapter": "Enable IP Adapter",
@ -237,6 +267,7 @@
"embedding": {
"addEmbedding": "Add Embedding",
"incompatibleModel": "Incompatible base model:",
"noEmbeddingsLoaded": "No Embeddings Loaded",
"noMatchingEmbedding": "No matching Embeddings"
},
"queue": {
@ -279,6 +310,7 @@
"next": "Next",
"status": "Status",
"total": "Total",
"time": "Time",
"pending": "Pending",
"in_progress": "In Progress",
"completed": "Completed",
@ -286,6 +318,7 @@
"canceled": "Canceled",
"completedIn": "Completed in",
"batch": "Batch",
"batchFieldValues": "Batch Field Values",
"item": "Item",
"session": "Session",
"batchValues": "Batch Values",
@ -313,7 +346,8 @@
"enableFailed": "Problem Enabling Invocation Cache",
"disable": "Disable",
"disableSucceeded": "Invocation Cache Disabled",
"disableFailed": "Problem Disabling Invocation Cache"
"disableFailed": "Problem Disabling Invocation Cache",
"useCache": "Use Cache"
},
"gallery": {
"allImagesLoaded": "All Images Loaded",
@ -322,6 +356,9 @@
"autoSwitchNewImages": "Auto-Switch to New Images",
"copy": "Copy",
"currentlyInUse": "This image is currently in use in the following features:",
"drop": "Drop",
"dropOrUpload": "$t(gallery.drop) or Upload",
"dropToUpload": "$t(gallery.drop) to Upload",
"deleteImage": "Delete Image",
"deleteImageBin": "Deleted images will be sent to your operating system's Bin.",
"deleteImagePermanent": "Deleted images cannot be restored.",
@ -331,10 +368,11 @@
"galleryImageSize": "Image Size",
"gallerySettings": "Gallery Settings",
"generations": "Generations",
"images": "Images",
"image": "image",
"loading": "Loading",
"loadMore": "Load More",
"maintainAspectRatio": "Maintain Aspect Ratio",
"noImageSelected": "No Image Selected",
"noImagesInGallery": "No Images to Display",
"setCurrentImage": "Set as Current Image",
"showGenerations": "Show Generations",
@ -342,6 +380,7 @@
"singleColumnLayout": "Single Column Layout",
"unableToLoad": "Unable to load Gallery",
"uploads": "Uploads",
"deleteSelection": "Delete Selection",
"downloadSelection": "Download Selection",
"preparingDownload": "Preparing Download",
"preparingDownloadFailed": "Problem Preparing Download"
@ -583,7 +622,7 @@
"strength": "Image to image strength",
"Threshold": "Noise Threshold",
"variations": "Seed-weight pairs",
"vae": "VAE",
"vae": "VAE",
"width": "Width",
"workflow": "Workflow"
},
@ -606,10 +645,12 @@
"cannotUseSpaces": "Cannot Use Spaces",
"checkpointFolder": "Checkpoint Folder",
"checkpointModels": "Checkpoints",
"checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
"clearCheckpointFolder": "Clear Checkpoint Folder",
"closeAdvanced": "Close Advanced",
"config": "Config",
"configValidationMsg": "Path to the config file of your model.",
"conversionNotSupported": "Conversion Not Supported",
"convert": "Convert",
"convertingModelBegin": "Converting Model. Please wait.",
"convertToDiffusers": "Convert To Diffusers",
@ -685,6 +726,7 @@
"nameValidationMsg": "Enter a name for your model",
"noCustomLocationProvided": "No Custom Location Provided",
"noModels": "No Models Found",
"noModelSelected": "No Model Selected",
"noModelsFound": "No Models Found",
"none": "none",
"notLoaded": "not loaded",
@ -730,8 +772,11 @@
"widthValidationMsg": "Default width of your model."
},
"models": {
"addLora": "Add LoRA",
"esrganModel": "ESRGAN Model",
"loading": "loading",
"noLoRAsAvailable": "No LoRAs available",
"noLoRAsLoaded": "No LoRAs Loaded",
"noMatchingLoRAs": "No matching LoRAs",
"noMatchingModels": "No matching Models",
"noModelsAvailable": "No models available",
@ -743,6 +788,7 @@
"nodes": {
"addNode": "Add Node",
"addNodeToolTip": "Add Node (Shift+A, Space)",
"addLinearView": "Add to Linear View",
"animatedEdges": "Animated Edges",
"animatedEdgesHelp": "Animate selected edges and edges connected to selected nodes",
"boardField": "Board",
@ -757,9 +803,12 @@
"cannotConnectOutputToOutput": "Cannot connect output to output",
"cannotConnectToSelf": "Cannot connect to self",
"cannotDuplicateConnection": "Cannot create duplicate connections",
"nodePack": "Node pack",
"clipField": "Clip",
"clipFieldDescription": "Tokenizer and text_encoder submodels.",
"collection": "Collection",
"collectionFieldType": "{{name}} Collection",
"collectionOrScalarFieldType": "{{name}} Collection|Scalar",
"collectionDescription": "TODO",
"collectionItem": "Collection Item",
"collectionItemDescription": "TODO",
@ -846,10 +895,15 @@
"mainModelField": "Model",
"mainModelFieldDescription": "TODO",
"maybeIncompatible": "May be Incompatible With Installed",
"mismatchedVersion": "Has Mismatched Version",
"mismatchedVersion": "Invalid node: node {{node}} of type {{type}} has mismatched version (try updating?)",
"missingCanvaInitImage": "Missing canvas init image",
"missingCanvaInitMaskImages": "Missing canvas init and mask images",
"missingTemplate": "Missing Template",
"missingTemplate": "Invalid node: node {{node}} of type {{type}} missing template (not installed?)",
"sourceNodeDoesNotExist": "Invalid edge: source/output node {{node}} does not exist",
"targetNodeDoesNotExist": "Invalid edge: target/input node {{node}} does not exist",
"sourceNodeFieldDoesNotExist": "Invalid edge: source/output field {{node}}.{{field}} does not exist",
"targetNodeFieldDoesNotExist": "Invalid edge: target/input field {{node}}.{{field}} does not exist",
"deletedInvalidEdge": "Deleted invalid edge {{source}} -> {{target}}",
"noConnectionData": "No connection data",
"noConnectionInProgress": "No connection in progress",
"node": "Node",
@ -863,6 +917,7 @@
"noMatchingNodes": "No matching nodes",
"noNodeSelected": "No node selected",
"nodeOpacity": "Node Opacity",
"nodeVersion": "Node Version",
"noOutputRecorded": "No outputs recorded",
"noOutputSchemaName": "No output schema name found in ref object",
"notes": "Notes",
@ -870,6 +925,7 @@
"oNNXModelField": "ONNX Model",
"oNNXModelFieldDescription": "ONNX model field.",
"outputField": "Output Field",
"outputFieldInInput": "Output field in input",
"outputFields": "Output Fields",
"outputNode": "Output node",
"outputSchemaNotFound": "Output schema not found",
@ -907,26 +963,46 @@
"stringDescription": "Strings are text.",
"stringPolymorphic": "String Polymorphic",
"stringPolymorphicDescription": "A collection of strings.",
"unableToLoadWorkflow": "Unable to Validate Workflow",
"unableToLoadWorkflow": "Unable to Load Workflow",
"unableToParseEdge": "Unable to parse edge",
"unableToParseNode": "Unable to parse node",
"unableToUpdateNode": "Unable to update node",
"unableToValidateWorkflow": "Unable to Validate Workflow",
"unableToMigrateWorkflow": "Unable to Migrate Workflow",
"unknownErrorValidatingWorkflow": "Unknown error validating workflow",
"inputFieldTypeParseError": "Unable to parse type of input field {{node}}.{{field}} ({{message}})",
"outputFieldTypeParseError": "Unable to parse type of output field {{node}}.{{field}} ({{message}})",
"unableToExtractSchemaNameFromRef": "unable to extract schema name from ref",
"unsupportedArrayItemType": "unsupported array item type \"{{type}}\"",
"unsupportedAnyOfLength": "too many union members ({{count}})",
"unsupportedMismatchedUnion": "mismatched CollectionOrScalar type with base types {{firstType}} and {{secondType}}",
"unableToParseFieldType": "unable to parse field type",
"uNetField": "UNet",
"uNetFieldDescription": "UNet submodel.",
"unhandledInputProperty": "Unhandled input property",
"unhandledOutputProperty": "Unhandled output property",
"unknownField": "Unknown Field",
"unknownField": "Unknown field",
"unknownFieldType": "$t(nodes.unknownField) type: {{type}}",
"unknownNode": "Unknown Node",
"unknownNodeType": "Unknown node type",
"unknownTemplate": "Unknown Template",
"unknownInput": "Unknown input: {{name}}",
"unkownInvocation": "Unknown Invocation type",
"unknownOutput": "Unknown output: {{name}}",
"updateNode": "Update Node",
"updateApp": "Update App",
"updateAllNodes": "Update Nodes",
"allNodesUpdated": "All Nodes Updated",
"unableToUpdateNodes_one": "Unable to update {{count}} node",
"unableToUpdateNodes_other": "Unable to update {{count}} nodes",
"vaeField": "Vae",
"vaeFieldDescription": "Vae submodel.",
"vaeModelField": "VAE",
"vaeModelFieldDescription": "TODO",
"validateConnections": "Validate Connections and Graph",
"validateConnectionsHelp": "Prevent invalid connections from being made, and invalid graphs from being invoked",
"unableToGetWorkflowVersion": "Unable to get workflow schema version",
"unrecognizedWorkflowVersion": "Unrecognized workflow schema version {{version}}",
"version": "Version",
"versionUnknown": " Version Unknown",
"workflow": "Workflow",
@ -1007,6 +1083,7 @@
"maskAdjustmentsHeader": "Mask Adjustments",
"maskBlur": "Blur",
"maskBlurMethod": "Blur Method",
"maskEdge": "Mask Edge",
"negativePromptPlaceholder": "Negative Prompt",
"noiseSettings": "Noise",
"noiseThreshold": "Noise Threshold",
@ -1054,7 +1131,9 @@
"upscale": "Upscale (Shift + U)",
"upscaleImage": "Upscale Image",
"upscaling": "Upscaling",
"unmasked": "Unmasked",
"useAll": "Use All",
"useSize": "Use Size",
"useCpuNoise": "Use CPU Noise",
"cpuNoise": "CPU Noise",
"gpuNoise": "GPU Noise",
@ -1075,6 +1154,7 @@
"dynamicPrompts": "Dynamic Prompts",
"enableDynamicPrompts": "Enable Dynamic Prompts",
"maxPrompts": "Max Prompts",
"promptsPreview": "Prompts Preview",
"promptsWithCount_one": "{{count}} Prompt",
"promptsWithCount_other": "{{count}} Prompts",
"seedBehaviour": {
@ -1114,7 +1194,10 @@
"displayHelpIcons": "Display Help Icons",
"displayInProgress": "Display Progress Images",
"enableImageDebugging": "Enable Image Debugging",
"enableInformationalPopovers": "Enable Informational Popovers",
"enableInvisibleWatermark": "Enable Invisible Watermark",
"enableNodesEditor": "Enable Nodes Editor",
"enableNSFWChecker": "Enable NSFW Checker",
"experimental": "Experimental",
"favoriteSchedulers": "Favorite Schedulers",
"favoriteSchedulersPlaceholder": "No schedulers favorited",
@ -1140,7 +1223,8 @@
"clearIntermediatesWithCount_other": "Clear {{count}} Intermediates",
"intermediatesCleared_one": "Cleared {{count}} Intermediate",
"intermediatesCleared_other": "Cleared {{count}} Intermediates",
"intermediatesClearedFailed": "Problem Clearing Intermediates"
"intermediatesClearedFailed": "Problem Clearing Intermediates",
"reloadingIn": "Reloading in"
},
"toast": {
"addedToBoard": "Added to board",
@ -1168,6 +1252,7 @@
"initialImageNotSet": "Initial Image Not Set",
"initialImageNotSetDesc": "Could not load initial image",
"initialImageSet": "Initial Image Set",
"invalidUpload": "Invalid Upload",
"loadedWithWarnings": "Workflow Loaded with Warnings",
"maskSavedAssets": "Mask Saved to Assets",
"maskSentControlnetAssets": "Mask Sent to ControlNet & Assets",
@ -1214,7 +1299,8 @@
"sentToImageToImage": "Sent To Image To Image",
"sentToUnifiedCanvas": "Sent to Unified Canvas",
"serverError": "Server Error",
"setCanvasInitialImage": "Set as canvas initial image",
"setAsCanvasInitialImage": "Set as canvas initial image",
"setCanvasInitialImage": "Set canvas initial image",
"setControlImage": "Set as control image",
"setIPAdapterImage": "Set as IP Adapter Image",
"setInitialImage": "Set as initial image",
@ -1475,7 +1561,7 @@
"clearCanvasHistoryConfirm": "Are you sure you want to clear the canvas history?",
"clearCanvasHistoryMessage": "Clearing the canvas history leaves your current canvas intact, but irreversibly clears the undo and redo history.",
"clearHistory": "Clear History",
"clearMask": "Clear Mask",
"clearMask": "Clear Mask (Shift+C)",
"colorPicker": "Color Picker",
"copyToClipboard": "Copy to Clipboard",
"cursorPosition": "Cursor Position",
@ -1502,6 +1588,7 @@
"redo": "Redo",
"resetView": "Reset View",
"saveBoxRegionOnly": "Save Box Region Only",
"saveMask": "Save $t(unifiedCanvas.mask)",
"saveToGallery": "Save To Gallery",
"scaledBoundingBox": "Scaled Bounding Box",
"showCanvasDebugInfo": "Show Additional Canvas Info",

1
invokeai/frontend/web/public/locales/es.json
View File

@ -98,7 +98,6 @@
"deleteImage": "Eliminar Imagen",
"deleteImageBin": "Las imágenes eliminadas se enviarán a la papelera de tu sistema operativo.",
"deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
"images": "Imágenes",
"assets": "Activos",
"autoAssignBoardOnClick": "Asignación automática de tableros al hacer clic"
},

57
invokeai/frontend/web/public/locales/it.json
View File

@ -89,7 +89,9 @@
"t2iAdapter": "Adattatore T2I",
"controlAdapter": "Adattatore di Controllo",
"controlNet": "ControlNet",
"auto": "Automatico"
"auto": "Automatico",
"simple": "Semplice",
"details": "Dettagli"
},
"gallery": {
"generations": "Generazioni",
@ -108,7 +110,6 @@
"deleteImage": "Elimina l'immagine",
"deleteImagePermanent": "Le immagini eliminate non possono essere ripristinate.",
"deleteImageBin": "Le immagini eliminate verranno spostate nel Cestino del tuo sistema operativo.",
"images": "Immagini",
"assets": "Risorse",
"autoAssignBoardOnClick": "Assegna automaticamente la bacheca al clic",
"featuresWillReset": "Se elimini questa immagine, quelle funzionalità verranno immediatamente ripristinate.",
@ -120,7 +121,8 @@
"setCurrentImage": "Imposta come immagine corrente",
"preparingDownload": "Preparazione del download",
"preparingDownloadFailed": "Problema durante la preparazione del download",
"downloadSelection": "Scarica gli elementi selezionati"
"downloadSelection": "Scarica gli elementi selezionati",
"noImageSelected": "Nessuna immagine selezionata"
},
"hotkeys": {
"keyboardShortcuts": "Tasti rapidi",
@ -395,7 +397,7 @@
"deleteModel": "Elimina modello",
"deleteConfig": "Elimina configurazione",
"deleteMsg1": "Sei sicuro di voler eliminare questo modello da InvokeAI?",
"deleteMsg2": "Questo eliminerà il modello dal disco se si trova nella cartella principale di InvokeAI. Se utilizzi una cartella personalizzata, il modello NON verrà eliminato dal disco.",
"deleteMsg2": "Questo eliminerà il modello dal disco se si trova nella cartella principale di InvokeAI. Se invece utilizzi una cartella personalizzata, il modello NON verrà eliminato dal disco.",
"formMessageDiffusersModelLocation": "Ubicazione modelli diffusori",
"formMessageDiffusersModelLocationDesc": "Inseriscine almeno uno.",
"formMessageDiffusersVAELocation": "Ubicazione file VAE",
@ -429,7 +431,7 @@
"mergedModelSaveLocation": "Ubicazione salvataggio",
"convertToDiffusersHelpText1": "Questo modello verrà convertito nel formato 🧨 Diffusore.",
"custom": "Personalizzata",
"convertToDiffusersHelpText3": "Il file checkpoint su disco SARÀ eliminato se si trova nella cartella principale di InvokeAI. Se si trova in una posizione personalizzata, NON verrà eliminato.",
"convertToDiffusersHelpText3": "Il file Checkpoint su disco verrà eliminato se si trova nella cartella principale di InvokeAI. Se si trova invece in una posizione personalizzata, NON verrà eliminato.",
"v1": "v1",
"pathToCustomConfig": "Percorso alla configurazione personalizzata",
"modelThree": "Modello 3",
@ -456,7 +458,7 @@
"modelDeleteFailed": "Impossibile eliminare il modello",
"noCustomLocationProvided": "Nessuna posizione personalizzata fornita",
"convertingModelBegin": "Conversione del modello. Attendere prego.",
"importModels": "Importa modelli",
"importModels": "Importa Modelli",
"modelsSynced": "Modelli sincronizzati",
"modelSyncFailed": "Sincronizzazione modello non riuscita",
"settings": "Impostazioni",
@ -474,7 +476,8 @@
"closeAdvanced": "Chiudi Avanzate",
"modelType": "Tipo di modello",
"customConfigFileLocation": "Posizione del file di configurazione personalizzato",
"vaePrecision": "Precisione VAE"
"vaePrecision": "Precisione VAE",
"noModelSelected": "Nessun modello selezionato"
},
"parameters": {
"images": "Immagini",
@ -601,7 +604,9 @@
"seamlessX": "Senza cuciture X",
"seamlessY": "Senza cuciture Y",
"imageActions": "Azioni Immagine",
"aspectRatioFree": "Libere"
"aspectRatioFree": "Libere",
"maskEdge": "Maschera i bordi",
"unmasked": "No maschera"
},
"settings": {
"models": "Modelli",
@ -642,7 +647,10 @@
"clearIntermediatesWithCount_one": "Cancella {{count}} immagine intermedia",
"clearIntermediatesWithCount_many": "Cancella {{count}} immagini intermedie",
"clearIntermediatesWithCount_other": "Cancella {{count}} immagini intermedie",
"clearIntermediatesDisabled": "La coda deve essere vuota per cancellare le immagini intermedie"
"clearIntermediatesDisabled": "La coda deve essere vuota per cancellare le immagini intermedie",
"enableNSFWChecker": "Abilita controllo NSFW",
"enableInvisibleWatermark": "Abilita filigrana invisibile",
"enableInformationalPopovers": "Abilita testo informativo a comparsa"
},
"toast": {
"tempFoldersEmptied": "Cartella temporanea svuotata",
@ -727,7 +735,8 @@
"setCanvasInitialImage": "Imposta come immagine iniziale della tela",
"workflowLoaded": "Flusso di lavoro caricato",
"setIPAdapterImage": "Imposta come immagine per l'Adattatore IP",
"problemSavingMaskDesc": "Impossibile salvare la maschera"
"problemSavingMaskDesc": "Impossibile salvare la maschera",
"setAsCanvasInitialImage": "Imposta come immagine iniziale della tela"
},
"tooltip": {
"feature": {
@ -828,7 +837,8 @@
"modifyConfig": "Modifica configurazione",
"menu": "Menu",
"showGalleryPanel": "Mostra il pannello Galleria",
"loadMore": "Carica altro"
"loadMore": "Carica altro",
"mode": "Modalità"
},
"ui": {
"hideProgressImages": "Nascondi avanzamento immagini",
@ -1026,7 +1036,11 @@
"unableToParseEdge": "Impossibile analizzare il bordo",
"latentsCollectionDescription": "Le immagini latenti possono essere passate tra i nodi.",
"imageCollection": "Raccolta Immagini",
"loRAModelField": "LoRA"
"loRAModelField": "LoRA",
"updateAllNodes": "Aggiorna tutti i nodi",
"unableToUpdateNodes_one": "Impossibile aggiornare {{count}} nodo",
"unableToUpdateNodes_many": "Impossibile aggiornare {{count}} nodi",
"unableToUpdateNodes_other": "Impossibile aggiornare {{count}} nodi"
},
"boards": {
"autoAddBoard": "Aggiungi automaticamente bacheca",
@ -1044,7 +1058,11 @@
"noMatching": "Nessuna bacheca corrispondente",
"selectBoard": "Seleziona una Bacheca",
"uncategorized": "Non categorizzato",
"downloadBoard": "Scarica la bacheca"
"downloadBoard": "Scarica la bacheca",
"deleteBoardOnly": "Elimina solo la Bacheca",
"deleteBoard": "Elimina Bacheca",
"deleteBoardAndImages": "Elimina Bacheca e Immagini",
"deletedBoardsCannotbeRestored": "Le bacheche eliminate non possono essere ripristinate"
},
"controlnet": {
"contentShuffleDescription": "Rimescola il contenuto di un'immagine",
@ -1085,7 +1103,7 @@
"none": "Nessuno",
"incompatibleBaseModel": "Modello base incompatibile:",
"pidiDescription": "Elaborazione immagini PIDI",
"fill": "Riempire",
"fill": "Riempie",
"colorMapDescription": "Genera una mappa dei colori dall'immagine",
"lineartAnimeDescription": "Elaborazione lineart in stile anime",
"imageResolution": "Risoluzione dell'immagine",
@ -1179,7 +1197,9 @@
"clearQueueAlertDialog2": "Sei sicuro di voler cancellare la coda?",
"item": "Elemento",
"graphFailedToQueue": "Impossibile mettere in coda il grafico",
"queueMaxExceeded": "È stato superato il limite massimo di {{max_queue_size}} e {{skip}} elementi verrebbero saltati"
"queueMaxExceeded": "È stato superato il limite massimo di {{max_queue_size}} e {{skip}} elementi verrebbero saltati",
"batchFieldValues": "Valori Campi Lotto",
"time": "Tempo"
},
"embedding": {
"noMatchingEmbedding": "Nessun Incorporamento corrispondente",
@ -1195,7 +1215,9 @@
"selectModel": "Seleziona un modello",
"selectLoRA": "Seleziona un LoRA",
"noRefinerModelsInstalled": "Nessun modello SDXL Refiner installato",
"noLoRAsInstalled": "Nessun LoRA installato"
"noLoRAsInstalled": "Nessun LoRA installato",
"esrganModel": "Modello ESRGAN",
"addLora": "Aggiungi LoRA"
},
"invocationCache": {
"disable": "Disabilita",
@ -1227,7 +1249,8 @@
"promptsWithCount_one": "{{count}} Prompt",
"promptsWithCount_many": "{{count}} Prompt",
"promptsWithCount_other": "{{count}} Prompt",
"dynamicPrompts": "Prompt dinamici"
"dynamicPrompts": "Prompt dinamici",
"promptsPreview": "Anteprima dei prompt"
},
"popovers": {
"paramScheduler": {

22
invokeai/frontend/web/public/locales/ja.json
View File

@ -438,7 +438,15 @@
"useSeed": "シード値を使用",
"useAll": "すべてを使用",
"info": "情報",
"showOptionsPanel": "オプションパネルを表示"
"showOptionsPanel": "オプションパネルを表示",
"aspectRatioFree": "自由",
"invoke": {
"noControlImageForControlAdapter": "コントロールアダプター #{{number}} に画像がありません",
"noModelForControlAdapter": "コントロールアダプター #{{number}} のモデルが選択されていません。"
},
"aspectRatio": "縦横比",
"iterations": "生成回数",
"general": "基本設定"
},
"settings": {
"models": "モデル",
@ -603,7 +611,7 @@
"delete": "削除",
"controlAdapter_other": "コントロールアダプター",
"colorMapTileSize": "タイルサイズ",
"ipAdapterImageFallback": "IP Adapterの画像が選択されていません",
"ipAdapterImageFallback": "IPアダプターの画像が選択されていません",
"mediapipeFaceDescription": "Mediapipeを使用して顔を検出",
"depthZoeDescription": "Zoeを使用して深度マップを生成",
"setControlImageDimensions": "コントロール画像のサイズを幅と高さにセット",
@ -652,7 +660,7 @@
"queueTotal": "合計 {{total}}",
"resumeSucceeded": "処理が再開されました",
"resumeTooltip": "処理を再開",
"resume": "再会",
"resume": "再開",
"status": "ステータス",
"pruneSucceeded": "キューから完了アイテム{{item_count}}件を削除しました",
"cancelTooltip": "現在のアイテムをキャンセル",
@ -812,5 +820,13 @@
"clear": "クリア",
"maxCacheSize": "最大キャッシュサイズ",
"cacheSize": "キャッシュサイズ"
},
"popovers": {
"paramRatio": {
"heading": "縦横比",
"paragraphs": [
"生成された画像の縦横比。"
]
}
}
}

1
invokeai/frontend/web/public/locales/nl.json
View File

@ -110,7 +110,6 @@
"deleteImageBin": "Verwijderde afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
"deleteImagePermanent": "Verwijderde afbeeldingen kunnen niet worden hersteld.",
"assets": "Eigen onderdelen",
"images": "Afbeeldingen",
"autoAssignBoardOnClick": "Ken automatisch bord toe bij klikken",
"featuresWillReset": "Als je deze afbeelding verwijdert, dan worden deze functies onmiddellijk teruggezet.",
"loading": "Bezig met laden",

1
invokeai/frontend/web/public/locales/ru.json
View File

@ -101,7 +101,6 @@
"deleteImagePermanent": "Удаленные изображения невозможно восстановить.",
"deleteImageBin": "Удаленные изображения будут отправлены в корзину вашей операционной системы.",
"deleteImage": "Удалить изображение",
"images": "Изображения",
"assets": "Ресурсы",
"autoAssignBoardOnClick": "Авто-назначение доски по клику"
},

76
invokeai/frontend/web/public/locales/zh_CN.json
View File

@ -90,7 +90,16 @@
"controlAdapter": "Control Adapter",
"controlNet": "ControlNet",
"on": "开",
"auto": "自动"
"auto": "自动",
"checkpoint": "Checkpoint",
"inpaint": "内补重绘",
"simple": "简单",
"template": "模板",
"outputs": "输出",
"data": "数据",
"safetensors": "Safetensors",
"outpaint": "外扩绘制",
"details": "详情"
},
"gallery": {
"generations": "生成的图像",
@ -109,7 +118,6 @@
"deleteImage": "删除图片",
"deleteImageBin": "被删除的图片会发送到你操作系统的回收站。",
"deleteImagePermanent": "删除的图片无法被恢复。",
"images": "图片",
"assets": "素材",
"autoAssignBoardOnClick": "点击后自动分配面板",
"featuresWillReset": "如果您删除该图像，这些功能会立即被重置。",
@ -121,7 +129,8 @@
"setCurrentImage": "设为当前图像",
"preparingDownload": "准备下载",
"preparingDownloadFailed": "准备下载时出现问题",
"downloadSelection": "下载所选内容"
"downloadSelection": "下载所选内容",
"noImageSelected": "无选中的图像"
},
"hotkeys": {
"keyboardShortcuts": "键盘快捷键",
@ -475,7 +484,9 @@
"oliveModels": "Olive",
"loraModels": "LoRA",
"alpha": "Alpha",
"vaePrecision": "VAE 精度"
"vaePrecision": "VAE 精度",
"checkpointOrSafetensors": "$t(common.checkpoint) / $t(common.safetensors)",
"noModelSelected": "无选中的模型"
},
"parameters": {
"images": "图像",
@ -602,7 +613,9 @@
"seamlessX&Y": "无缝 X & Y",
"aspectRatioFree": "自由",
"seamlessX": "无缝 X",
"seamlessY": "无缝 Y"
"seamlessY": "无缝 Y",
"maskEdge": "遮罩边缘",
"unmasked": "取消遮罩"
},
"settings": {
"models": "模型",
@ -639,7 +652,10 @@
"clearIntermediatesDesc1": "清除中间产物会重置您的画布和 ControlNet 状态。",
"intermediatesClearedFailed": "清除中间产物时出现问题",
"clearIntermediatesWithCount_other": "清除 {{count}} 个中间产物",
"clearIntermediatesDisabled": "队列为空才能清理中间产物"
"clearIntermediatesDisabled": "队列为空才能清理中间产物",
"enableNSFWChecker": "启用成人内容检测器",
"enableInvisibleWatermark": "启用不可见水印",
"enableInformationalPopovers": "启用信息弹窗"
},
"toast": {
"tempFoldersEmptied": "临时文件夹已清空",
@ -705,7 +721,7 @@
"modelAddFailed": "模型添加失败",
"problemDownloadingCanvas": "下载画布时出现问题",
"problemMergingCanvas": "合并画布时出现问题",
"setCanvasInitialImage": "设为画布初始图像",
"setCanvasInitialImage": "设定画布初始图像",
"imageUploaded": "图像已上传",
"addedToBoard": "已添加到面板",
"workflowLoaded": "工作流已加载",
@ -722,7 +738,8 @@
"canvasSavedGallery": "画布已保存到图库",
"imageUploadFailed": "图像上传失败",
"problemImportingMask": "导入遮罩时出现问题",
"baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{count}} 个不兼容的子模型"
"baseModelChangedCleared_other": "基础模型已更改, 已清除或禁用 {{count}} 个不兼容的子模型",
"setAsCanvasInitialImage": "设为画布初始图像"
},
"unifiedCanvas": {
"layer": "图层",
@ -808,7 +825,8 @@
"toggleAutoscroll": "切换自动缩放",
"menu": "菜单",
"showGalleryPanel": "显示图库浮窗",
"loadMore": "加载更多"
"loadMore": "加载更多",
"mode": "模式"
},
"ui": {
"showProgressImages": "显示处理中的图片",
@ -1031,7 +1049,9 @@
"integerPolymorphic": "整数多态",
"latentsPolymorphic": "Latents 多态",
"conditioningField": "条件",
"latentsField": "Latents"
"latentsField": "Latents",
"updateAllNodes": "更新所有节点",
"unableToUpdateNodes_other": "{{count}} 个节点无法完成更新"
},
"controlnet": {
"resize": "直接缩放",
@ -1117,7 +1137,8 @@
"openPose": "Openpose",
"controlAdapter_other": "Control Adapters",
"lineartAnime": "Lineart Anime",
"canny": "Canny"
"canny": "Canny",
"unstarImage": "取消收藏图像"
},
"queue": {
"status": "状态",
@ -1176,7 +1197,9 @@
"queueTotal": "总计 {{total}}",
"enqueueing": "队列中的批次",
"queueMaxExceeded": "超出最大值 {{max_queue_size}}，将跳过 {{skip}}",
"graphFailedToQueue": "节点图加入队列失败"
"graphFailedToQueue": "节点图加入队列失败",
"batchFieldValues": "批处理值",
"time": "时间"
},
"sdxl": {
"refinerStart": "Refiner 开始作用时机",
@ -1222,7 +1245,8 @@
"seamless": "无缝",
"fit": "图生图匹配",
"recallParameters": "召回参数",
"noRecallParameters": "未找到要召回的参数"
"noRecallParameters": "未找到要召回的参数",
"vae": "VAE"
},
"models": {
"noMatchingModels": "无相匹配的模型",
@ -1233,7 +1257,9 @@
"selectModel": "选择一个模型",
"selectLoRA": "选择一个 LoRA",
"noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
"noLoRAsInstalled": "无已安装的 LoRA"
"noLoRAsInstalled": "无已安装的 LoRA",
"esrganModel": "ESRGAN 模型",
"addLora": "添加 LoRA"
},
"boards": {
"autoAddBoard": "自动添加面板",
@ -1251,7 +1277,11 @@
"changeBoard": "更改面板",
"loading": "加载中...",
"clearSearch": "清除检索",
"downloadBoard": "下载面板"
"downloadBoard": "下载面板",
"deleteBoardOnly": "仅删除面板",
"deleteBoard": "删除面板",
"deleteBoardAndImages": "删除面板和图像",
"deletedBoardsCannotbeRestored": "已删除的面板无法被恢复"
},
"embedding": {
"noMatchingEmbedding": "不匹配的 Embedding",
@ -1270,7 +1300,8 @@
"combinatorial": "组合生成",
"maxPrompts": "最大提示词数",
"dynamicPrompts": "动态提示词",
"promptsWithCount_other": "{{count}} 个提示词"
"promptsWithCount_other": "{{count}} 个提示词",
"promptsPreview": "提示词预览"
},
"popovers": {
"compositingMaskAdjustments": {
@ -1501,5 +1532,18 @@
"clear": "清除",
"maxCacheSize": "最大缓存大小",
"cacheSize": "缓存大小"
},
"hrf": {
"enableHrf": "启用高分辨率修复",
"upscaleMethod": "放大方法",
"enableHrfTooltip": "使用较低的分辨率进行初始生成，放大到基础分辨率后进行图生图。",
"metadata": {
"strength": "高分辨率修复强度",
"enabled": "高分辨率修复已启用",
"method": "高分辨率修复方法"
},
"hrf": "高分辨率修复",
"hrfStrength": "高分辨率修复强度",
"strengthTooltip": "值越低细节越少，但可以减少部分潜在的伪影。"
}
}

46
invokeai/frontend/web/scripts/typegen.js
View File

@ -1,61 +1,19 @@
import fs from 'node:fs';
import openapiTS from 'openapi-typescript';
import { COLORS } from './colors.js';
const OPENAPI_URL = 'http://127.0.0.1:9090/openapi.json';
const OUTPUT_FILE = 'src/services/api/schema.d.ts';
async function main() {
process.stdout.write(
`Generating types "${OPENAPI_URL}" --> "${OUTPUT_FILE}"...\n\n`
`Generating types "${OPENAPI_URL}" --> "${OUTPUT_FILE}"...`
);
const types = await openapiTS(OPENAPI_URL, {
exportType: true,
transform: (schemaObject, metadata) => {
transform: (schemaObject) => {
if ('format' in schemaObject && schemaObject.format === 'binary') {
return schemaObject.nullable ? 'Blob | null' : 'Blob';
}
/**
* Because invocations may have required fields that accept connection input, the generated
* types may be incorrect.
*
* For example, the ImageResizeInvocation has a required `image` field, but because it accepts
* connection input, it should be optional on instantiation of the field.
*
* To handle this, the schema exposes an `input` property that can be used to determine if the
* field accepts connection input. If it does, we can make the field optional.
*/
if ('class' in schemaObject && schemaObject.class === 'invocation') {
// We only want to make fields optional if they are required
if (!Array.isArray(schemaObject?.required)) {
schemaObject.required = [];
}
schemaObject.required.forEach((prop) => {
const acceptsConnection = ['any', 'connection'].includes(
schemaObject.properties?.[prop]?.['input']
);
if (acceptsConnection) {
// remove this prop from the required array
const invocationName = metadata.path.split('/').pop();
console.log(
`Making connectable field optional: ${COLORS.fg.green}${invocationName}.${COLORS.fg.cyan}${prop}${COLORS.reset}`
);
schemaObject.required = schemaObject.required.filter(
(r) => r !== prop
);
}
});
return;
}
// Check if we are generating types for an invocation output
if ('class' in schemaObject && schemaObject.class === 'output') {
// modify output types
}
},
});
fs.writeFileSync(OUTPUT_FILE, types);

7
invokeai/frontend/web/src/app/components/App.tsx
View File

@ -1,11 +1,13 @@
import { Flex, Grid } from '@chakra-ui/react';
import { useStore } from '@nanostores/react';
import { useSocketIO } from 'app/hooks/useSocketIO';
import { useLogger } from 'app/logging/useLogger';
import { appStarted } from 'app/store/middleware/listenerMiddleware/listeners/appStarted';
import { $headerComponent } from 'app/store/nanostores/headerComponent';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { PartialAppConfig } from 'app/types/invokeai';
import ImageUploader from 'common/components/ImageUploader';
import { useClearStorage } from 'common/hooks/useClearStorage';
import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
import SiteHeader from 'features/system/components/SiteHeader';
@ -20,7 +22,6 @@ import AppErrorBoundaryFallback from './AppErrorBoundaryFallback';
import GlobalHotkeys from './GlobalHotkeys';
import PreselectedImage from './PreselectedImage';
import Toaster from './Toaster';
import { useClearStorage } from 'common/hooks/useClearStorage';
const DEFAULT_CONFIG = {};
@ -34,11 +35,13 @@ interface Props {
const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
const language = useAppSelector(languageSelector);
const logger = useLogger('system');
const dispatch = useAppDispatch();
const clearStorage = useClearStorage();
// singleton!
useSocketIO();
const handleReset = useCallback(() => {
clearStorage();
location.reload();

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