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---
title: Model Merging
---
# :material-image-off: Model Merging
## How to Merge Models
As of version 2.3, InvokeAI comes with a script that allows you to
merge two or three diffusers-type models into a new merged model. The
resulting model will combine characteristics of the original, and can
be used to teach an old model new tricks.
You may run the merge script by starting the invoke launcher
(`invoke.sh` or `invoke.bat`) and choosing the option for _merge
models_. This will launch a text-based interactive user interface that
prompts you to select the models to merge, how to merge them, and the
merged model name.
Alternatively you may activate InvokeAI's virtual environment from the
command line, and call the script via `merge_models_fe.py` (the "fe"
stands for "front end"). There is also a version that accepts
command-line arguments, which you can run with the command
`merge_models.py`.
The user interface for the text-based interactive script is
straightforward. It shows you a series of setting fields. Use control-N (^N)
to move to the next field, and control-P (^P) to move to the previous
one. You can also use TAB and shift-TAB to move forward and
backward. Once you are in a multiple choice field, use the up and down
cursor arrows to move to your desired selection, and press <SPACE> or
<ENTER> to select it. Change text fields by typing in them, and adjust
scrollbars using the left and right arrow keys.
Once you are happy with your settings, press the OK button. Note that
there may be two pages of settings, depending on the height of your
screen, and the OK button may be on the second page. Advance past the
last field of the first page to get to the second page, and reverse
this to get back.
If the merge runs successfully, it will create a new diffusers model
under the selected name and register it with InvokeAI.
## The Settings
* Model Selection -- there are three multiple choice fields that
display all the diffusers-style models that InvokeAI knows about.
If you do not see the model you are looking for, then it is probably
a legacy checkpoint model and needs to be converted using the
`invoke.py` command-line client and its `!optimize` command. You
must select at least two models to merge. The third can be left at
"None" if you desire.
* Alpha -- This is the ratio to use when combining models. It ranges
from 0 to 1. The higher the value, the more weight is given to the
2d and (optionally) 3d models. So if you have two models named "A"
and "B", an alpha value of 0.25 will give you a merged model that is
25% A and 75% B.
* Interpolation Method -- This is the method used to combine
weights. The options are "weighted_sum" (the default), "sigmoid",
"inv_sigmoid" and "add_difference". Each produces slightly different
results. When three models are in use, only "add_difference" is
available. (TODO: cite a reference that describes what these
interpolation methods actually do and how to decide among them).
* Force -- Not all models are compatible with each other. The merge
script will check for compatibility and refuse to merge ones that
are incompatible. Set this checkbox to try merging anyway.
* Name for merged model - This is the name for the new model. Please
use InvokeAI conventions - only alphanumeric letters and the
characters ".+-".
## Caveats
This is a new script and may contain bugs.

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docs/index.md
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@ -93,9 +93,15 @@ getting InvokeAI up and running on your system. For alternative installation and
upgrade instructions, please see: upgrade instructions, please see:
[InvokeAI Installation Overview](installation/) [InvokeAI Installation Overview](installation/)
Linux users who wish to make use of the PyPatchMatch inpainting functions will Users who wish to make use of the **PyPatchMatch** inpainting functions
need to perform a bit of extra work to enable this module. Instructions can be will need to perform a bit of extra work to enable this
found at [Installing PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md). module. Instructions can be found at [Installing
PyPatchMatch](installation/060_INSTALL_PATCHMATCH.md).
If you have an NVIDIA card, you can benefit from the significant
memory savings and performance benefits provided by Facebook Lab's
**xFormers** module. Instructions for Linux and Windows users can be found
at [Installing xFormers](installation/070_INSTALL_XFORMERS.md).
## :fontawesome-solid-computer: Hardware Requirements ## :fontawesome-solid-computer: Hardware Requirements
@ -151,6 +157,8 @@ images in full-precision mode:
<!-- seperator --> <!-- seperator -->
- [Prompt Engineering](features/PROMPTS.md) - [Prompt Engineering](features/PROMPTS.md)
<!-- seperator --> <!-- seperator -->
- [Model Merging](features/MODEL_MERGING.md)
<!-- seperator -->
- Miscellaneous - Miscellaneous
- [NSFW Checker](features/NSFW.md) - [NSFW Checker](features/NSFW.md)
- [Embiggen upscaling](features/EMBIGGEN.md) - [Embiggen upscaling](features/EMBIGGEN.md)

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---
title: Installing xFormers
---
# :material-image-size-select-large: Installing xformers
xFormers is toolbox that integrates with the pyTorch and CUDA
libraries to provide accelerated performance and reduced memory
consumption for applications using the transformers machine learning
architecture. After installing xFormers, InvokeAI users who have
CUDA GPUs will see a noticeable decrease in GPU memory consumption and
an increase in speed.
xFormers can be installed into a working InvokeAI installation without
any code changes or other updates. This document explains how to
install xFormers.
## Pip Install
For both Windows and Linux, you can install `xformers` in just a
couple of steps from the command line.
If you are used to launching `invoke.sh` or `invoke.bat` to start
InvokeAI, then run the launcher and select the "developer's console"
to get to the command line. If you run invoke.py directly from the
command line, then just be sure to activate it's virtual environment.
Then run the following three commands:
```sh
pip install xformers==0.0.16rc425
pip install triton
python -m xformers.info output
```
The first command installs `xformers`, the second installs the
`triton` training accelerator, and the third prints out the `xformers`
installation status. If all goes well, you'll see a report like the
following:
```sh
xFormers 0.0.16rc425
memory_efficient_attention.cutlassF: available
memory_efficient_attention.cutlassB: available
memory_efficient_attention.flshattF: available
memory_efficient_attention.flshattB: available
memory_efficient_attention.smallkF: available
memory_efficient_attention.smallkB: available
memory_efficient_attention.tritonflashattF: available
memory_efficient_attention.tritonflashattB: available
swiglu.fused.p.cpp: available
is_triton_available: True
is_functorch_available: False
pytorch.version: 1.13.1+cu117
pytorch.cuda: available
gpu.compute_capability: 8.6
gpu.name: NVIDIA RTX A2000 12GB
build.info: available
build.cuda_version: 1107
build.python_version: 3.10.9
build.torch_version: 1.13.1+cu117
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
build.env.NVCC_FLAGS: None
build.env.XFORMERS_PACKAGE_FROM: wheel-v0.0.16rc425
source.privacy: open source
```
## Source Builds
`xformers` is currently under active development and at some point you
may wish to build it from sourcce to get the latest features and
bugfixes.
### Source Build on Linux
Note that xFormers only works with true NVIDIA GPUs and will not work
properly with the ROCm driver for AMD acceleration.
xFormers is not currently available as a pip binary wheel and must be
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 11.7
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 11.7
Downloads](https://developer.nvidia.com/cuda-11-7-0-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:
```
wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
sudo sh cuda_11.7.0_515.43.04_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 1.13 with CUDA 11.7 support
If you are using InvokeAI 2.3 or higher, these will already be
installed. If not, you can check whether you have the needed libraries
using a quick command. Activate the invokeai virtual environment,
either by entering the "developer's console", or manually with a
command similar to `source ~/invokeai/.venv/bin/activate` (depending
on where your `invokeai` directory is.
Then run the command:
```sh
python -c 'exec("import torch\nprint(torch.__version__)")'
```
If it prints __1.13.1+cu117__ you're good. If not, you can install the
most up to date libraries with this command:
```sh
pip install --upgrade --force-reinstall torch torchvision
```
#### 3. Install the triton module
This module isn't necessary for xFormers image inference optimization,
but avoids a startup warning.
```sh
pip install triton
```
#### 4. Install source code build prerequisites
To build xFormers from source, you will need the `build-essentials`
package. If you don't have it installed already, run:
```sh
sudo apt install build-essential
```
#### 5. Build xFormers
There is no pip wheel package for xFormers at this time (January
2023). Although there is a conda package, InvokeAI no longer
officially supports conda installations and you're on your own if you
wish to try this route.
Following the recipe provided at the [xFormers GitHub
page](https://github.com/facebookresearch/xformers), and with the
InvokeAI virtual environment active (see step 1) run the following
commands:
```sh
pip install ninja
export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.2;7.5;8.0;8.6"
pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers
```
The TORCH_CUDA_ARCH_LIST is a list of GPU architectures to compile
xFormer support for. You can speed up compilation by selecting
the architecture specific for your system. You'll find the list of
GPUs and their architectures at NVIDIA's [GPU Compute
Capability](https://developer.nvidia.com/cuda-gpus) table.
If the compile and install completes successfully, you can check that
xFormers is installed with this command:
```sh
python -m xformers.info
```
If suiccessful, the top of the listing should indicate "available" for
each of the `memory_efficient_attention` modules, as shown here:
```sh
memory_efficient_attention.cutlassF: available
memory_efficient_attention.cutlassB: available
memory_efficient_attention.flshattF: available
memory_efficient_attention.flshattB: available
memory_efficient_attention.smallkF: available
memory_efficient_attention.smallkB: available
memory_efficient_attention.tritonflashattF: available
memory_efficient_attention.tritonflashattB: available
[...]
```
You can now launch InvokeAI and enjoy the benefits of xFormers.
### Windows
To come
---
(c) Copyright 2023 Lincoln Stein and the InvokeAI Development Team

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@ -19,6 +19,8 @@ experience and preferences.
those who prefer the `conda` tool, and one suited to those who prefer those who prefer the `conda` tool, and one suited to those who prefer
`pip` and Python virtual environments. In our hands the pip install `pip` and Python virtual environments. In our hands the pip install
is faster and more reliable, but your mileage may vary. is faster and more reliable, but your mileage may vary.
Note that the conda installation method is currently deprecated and
will not be supported at some point in the future.
This method is recommended for users who have previously used `conda` This method is recommended for users who have previously used `conda`
or `pip` in the past, developers, and anyone who wishes to remain on or `pip` in the past, developers, and anyone who wishes to remain on

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<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>InvokeAI - A Stable Diffusion Toolkit</title> <title>InvokeAI - A Stable Diffusion Toolkit</title>
<link rel="shortcut icon" type="icon" href="./assets/favicon.0d253ced.ico" /> <link rel="shortcut icon" type="icon" href="./assets/favicon.0d253ced.ico" />
<script type="module" crossorigin src="./assets/index.1b59e83a.js"></script> <script type="module" crossorigin src="./assets/index.dd470915.js"></script>
<link rel="stylesheet" href="./assets/index.0dadf5d0.css"> <link rel="stylesheet" href="./assets/index.8badc8b4.css">
<script type="module">try{import.meta.url;import("_").catch(()=>1);}catch(e){}window.__vite_is_modern_browser=true;</script> <script type="module">try{import.meta.url;import("_").catch(()=>1);}catch(e){}window.__vite_is_modern_browser=true;</script>
<script type="module">!function(){if(window.__vite_is_modern_browser)return;console.warn("vite: loading legacy build because dynamic import or import.meta.url is unsupported, syntax error above should be ignored");var e=document.getElementById("vite-legacy-polyfill"),n=document.createElement("script");n.src=e.src,n.onload=function(){System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))},document.body.appendChild(n)}();</script> <script type="module">!function(){if(window.__vite_is_modern_browser)return;console.warn("vite: loading legacy build because dynamic import or import.meta.url is unsupported, syntax error above should be ignored");var e=document.getElementById("vite-legacy-polyfill"),n=document.createElement("script");n.src=e.src,n.onload=function(){System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))},document.body.appendChild(n)}();</script>
</head> </head>
@ -18,6 +18,6 @@
<script nomodule>!function(){var e=document,t=e.createElement("script");if(!("noModule"in t)&&"onbeforeload"in t){var n=!1;e.addEventListener("beforeload",(function(e){if(e.target===t)n=!0;else if(!e.target.hasAttribute("nomodule")||!n)return;e.preventDefault()}),!0),t.type="module",t.src=".",e.head.appendChild(t),t.remove()}}();</script> <script nomodule>!function(){var e=document,t=e.createElement("script");if(!("noModule"in t)&&"onbeforeload"in t){var n=!1;e.addEventListener("beforeload",(function(e){if(e.target===t)n=!0;else if(!e.target.hasAttribute("nomodule")||!n)return;e.preventDefault()}),!0),t.type="module",t.src=".",e.head.appendChild(t),t.remove()}}();</script>
<script nomodule crossorigin id="vite-legacy-polyfill" src="./assets/polyfills-legacy-dde3a68a.js"></script> <script nomodule crossorigin id="vite-legacy-polyfill" src="./assets/polyfills-legacy-dde3a68a.js"></script>
<script nomodule crossorigin id="vite-legacy-entry" data-src="./assets/index-legacy-474a75fe.js">System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))</script> <script nomodule crossorigin id="vite-legacy-entry" data-src="./assets/index-legacy-6edbec57.js">System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))</script>
</body> </body>
</html> </html>

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"langPortuguese": "Portuguese", "langPortuguese": "Portuguese",
"langFrench": "French", "langFrench": "French",
"langPolish": "Polish", "langPolish": "Polish",
"langSimplifiedChinese": "Simplified Chinese",
"langSpanish": "Spanish",
"langJapanese": "Japanese",
"text2img": "Text To Image", "text2img": "Text To Image",
"img2img": "Image To Image", "img2img": "Image To Image",
"unifiedCanvas": "Unified Canvas", "unifiedCanvas": "Unified Canvas",
@ -32,6 +35,7 @@
"upload": "Upload", "upload": "Upload",
"close": "Close", "close": "Close",
"load": "Load", "load": "Load",
"back": "Back",
"statusConnected": "Connected", "statusConnected": "Connected",
"statusDisconnected": "Disconnected", "statusDisconnected": "Disconnected",
"statusError": "Error", "statusError": "Error",

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"langPolish": "Polish", "langPolish": "Polish",
"langSimplifiedChinese": "Simplified Chinese", "langSimplifiedChinese": "Simplified Chinese",
"langSpanish": "Spanish", "langSpanish": "Spanish",
"langJapanese": "Japanese",
"text2img": "Text To Image", "text2img": "Text To Image",
"img2img": "Image To Image", "img2img": "Image To Image",
"unifiedCanvas": "Unified Canvas", "unifiedCanvas": "Unified Canvas",
@ -34,6 +35,7 @@
"upload": "Upload", "upload": "Upload",
"close": "Close", "close": "Close",
"load": "Load", "load": "Load",
"back": "Back",
"statusConnected": "Connected", "statusConnected": "Connected",
"statusDisconnected": "Disconnected", "statusDisconnected": "Disconnected",
"statusError": "Error", "statusError": "Error",

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{
"hotkeysLabel": "Hotkeys",
"themeLabel": "テーマ",
"languagePickerLabel": "言語選択",
"reportBugLabel": "バグ報告",
"githubLabel": "Github",
"discordLabel": "Discord",
"settingsLabel": "設定",
"darkTheme": "ダーク",
"lightTheme": "ライト",
"greenTheme": "緑",
"langEnglish": "English",
"langRussian": "Russian",
"langItalian": "Italian",
"langBrPortuguese": "Portuguese (Brazilian)",
"langGerman": "German",
"langPortuguese": "Portuguese",
"langFrench": "French",
"langPolish": "Polish",
"langSimplifiedChinese": "Simplified Chinese",
"langSpanish": "Spanish",
"text2img": "Text To Image",
"img2img": "Image To Image",
"unifiedCanvas": "Unified Canvas",
"nodes": "Nodes",
"nodesDesc": "現在、画像生成のためのノードベースシステムを開発中です。機能についてのアップデートにご期待ください。",
"postProcessing": "後処理",
"postProcessDesc1": "Invoke AIは、多彩な後処理の機能を備えています。アップスケーリングと顔修復は、すでにWebUI上で利用可能です。これらは、[Text To Image]および[Image To Image]タブの[詳細オプション]メニューからアクセスできます。また、現在の画像表示の上やビューア内の画像アクションボタンを使って、画像を直接処理することもできます。",
"postProcessDesc2": "より高度な後処理の機能を実現するための専用UIを近日中にリリース予定です。",
"postProcessDesc3": "Invoke AI CLIでは、この他にもEmbiggenをはじめとする様々な機能を利用することができます。",
"training": "追加学習",
"trainingDesc1": "Textual InversionとDreamboothを使って、WebUIから独自のEmbeddingとチェックポイントを追加学習するための専用ワークフローです。",
"trainingDesc2": "InvokeAIは、すでにメインスクリプトを使ったTextual Inversionによるカスタム埋め込み追加学習にも対応しています。",
"upload": "アップロード",
"close": "閉じる",
"load": "ロード",
"back": "戻る",
"statusConnected": "接続済",
"statusDisconnected": "切断済",
"statusError": "エラー",
"statusPreparing": "準備中",
"statusProcessingCanceled": "処理をキャンセル",
"statusProcessingComplete": "処理完了",
"statusGenerating": "生成中",
"statusGeneratingTextToImage": "Text To Imageで生成中",
"statusGeneratingImageToImage": "Image To Imageで生成中",
"statusGeneratingInpainting": "Generating Inpainting",
"statusGeneratingOutpainting": "Generating Outpainting",
"statusGenerationComplete": "生成完了",
"statusIterationComplete": "Iteration Complete",
"statusSavingImage": "画像を保存",
"statusRestoringFaces": "顔の修復",
"statusRestoringFacesGFPGAN": "顔の修復 (GFPGAN)",
"statusRestoringFacesCodeFormer": "顔の修復 (CodeFormer)",
"statusUpscaling": "アップスケーリング",
"statusUpscalingESRGAN": "アップスケーリング (ESRGAN)",
"statusLoadingModel": "モデルを読み込む",
"statusModelChanged": "モデルを変更"
}

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{
"generations": "Generations",
"showGenerations": "Show Generations",
"uploads": "アップロード",
"showUploads": "アップロードした画像を見る",
"galleryImageSize": "画像のサイズ",
"galleryImageResetSize": "サイズをリセット",
"gallerySettings": "ギャラリーの設定",
"maintainAspectRatio": "アスペクト比を維持",
"autoSwitchNewImages": "Auto-Switch to New Images",
"singleColumnLayout": "シングルカラムレイアウト",
"pinGallery": "ギャラリーにピン留め",
"allImagesLoaded": "すべての画像を読み込む",
"loadMore": "さらに読み込む",
"noImagesInGallery": "ギャラリーに画像がありません"
}

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{
"keyboardShortcuts": "キーボードショートカット",
"appHotkeys": "アプリのホットキー",
"generalHotkeys": "Generalのホットキー",
"galleryHotkeys": "ギャラリーのホットキー",
"unifiedCanvasHotkeys": "Unified Canvasのホットキー",
"invoke": {
"title": "Invoke",
"desc": "画像を生成"
},
"cancel": {
"title": "キャンセル",
"desc": "画像の生成をキャンセル"
},
"focusPrompt": {
"title": "Focus Prompt",
"desc": "プロンプトテキストボックスにフォーカス"
},
"toggleOptions": {
"title": "オプションパネルのトグル",
"desc": "オプションパネルの開閉"
},
"pinOptions": {
"title": "ピン",
"desc": "オプションパネルを固定"
},
"toggleViewer": {
"title": "ビュワーのトグル",
"desc": "ビュワーを開閉"
},
"toggleGallery": {
"title": "ギャラリーのトグル",
"desc": "ギャラリードロワーの開閉"
},
"maximizeWorkSpace": {
"title": "作業領域の最大化",
"desc": "パネルを閉じて、作業領域を最大に"
},
"changeTabs": {
"title": "タブの切替",
"desc": "他の作業領域と切替"
},
"consoleToggle": {
"title": "コンソールのトグル",
"desc": "コンソールの開閉"
},
"setPrompt": {
"title": "プロンプトをセット",
"desc": "現在の画像のプロンプトを使用"
},
"setSeed": {
"title": "シード値をセット",
"desc": "現在の画像のシード値を使用"
},
"setParameters": {
"title": "パラメータをセット",
"desc": "現在の画像のすべてのパラメータを使用"
},
"restoreFaces": {
"title": "顔の修復",
"desc": "現在の画像を修復"
},
"upscale": {
"title": "アップスケール",
"desc": "現在の画像をアップスケール"
},
"showInfo": {
"title": "情報を見る",
"desc": "現在の画像のメタデータ情報を表示"
},
"sendToImageToImage": {
"title": "Image To Imageに転送",
"desc": "現在の画像をImage to Imageに転送"
},
"deleteImage": {
"title": "画像を削除",
"desc": "現在の画像を削除"
},
"closePanels": {
"title": "パネルを閉じる",
"desc": "開いているパネルを閉じる"
},
"previousImage": {
"title": "前の画像",
"desc": "ギャラリー内の1つ前の画像を表示"
},
"nextImage": {
"title": "次の画像",
"desc": "ギャラリー内の1つ後の画像を表示"
},
"toggleGalleryPin": {
"title": "ギャラリードロワーの固定",
"desc": "ギャラリーをUIにピン留め/解除"
},
"increaseGalleryThumbSize": {
"title": "ギャラリーの画像を拡大",
"desc": "ギャラリーのサムネイル画像を拡大"
},
"decreaseGalleryThumbSize": {
"title": "ギャラリーの画像サイズを縮小",
"desc": "ギャラリーのサムネイル画像を縮小"
},
"selectBrush": {
"title": "ブラシを選択",
"desc": "ブラシを選択"
},
"selectEraser": {
"title": "消しゴムを選択",
"desc": "消しゴムを選択"
},
"decreaseBrushSize": {
"title": "ブラシサイズを縮小",
"desc": "ブラシ/消しゴムのサイズを縮小"
},
"increaseBrushSize": {
"title": "ブラシサイズを拡大",
"desc": "ブラシ/消しゴムのサイズを拡大"
},
"decreaseBrushOpacity": {
"title": "ブラシの不透明度を下げる",
"desc": "キャンバスブラシの不透明度を下げる"
},
"increaseBrushOpacity": {
"title": "ブラシの不透明度を上げる",
"desc": "キャンバスブラシの不透明度を上げる"
},
"moveTool": {
"title": "Move Tool",
"desc": "Allows canvas navigation"
},
"fillBoundingBox": {
"title": "バウンディングボックスを塗りつぶす",
"desc": "ブラシの色でバウンディングボックス領域を塗りつぶす"
},
"eraseBoundingBox": {
"title": "バウンディングボックスを消す",
"desc": "バウンディングボックス領域を消す"
},
"colorPicker": {
"title": "カラーピッカーを選択",
"desc": "カラーピッカーを選択"
},
"toggleSnap": {
"title": "Toggle Snap",
"desc": "Toggles Snap to Grid"
},
"quickToggleMove": {
"title": "Quick Toggle Move",
"desc": "Temporarily toggles Move mode"
},
"toggleLayer": {
"title": "レイヤーを切替",
"desc": "マスク/ベースレイヤの選択を切替"
},
"clearMask": {
"title": "マスクを消す",
"desc": "マスク全体を消す"
},
"hideMask": {
"title": "マスクを非表示",
"desc": "マスクを表示/非表示"
},
"showHideBoundingBox": {
"title": "バウンディングボックスを表示/非表示",
"desc": "バウンディングボックスの表示/非表示を切替"
},
"mergeVisible": {
"title": "Merge Visible",
"desc": "Merge all visible layers of canvas"
},
"saveToGallery": {
"title": "ギャラリーに保存",
"desc": "現在のキャンバスをギャラリーに保存"
},
"copyToClipboard": {
"title": "クリップボードにコピー",
"desc": "現在のキャンバスをクリップボードにコピー"
},
"downloadImage": {
"title": "画像をダウンロード",
"desc": "現在の画像をダウンロード"
},
"undoStroke": {
"title": "Undo Stroke",
"desc": "Undo a brush stroke"
},
"redoStroke": {
"title": "Redo Stroke",
"desc": "Redo a brush stroke"
},
"resetView": {
"title": "キャンバスをリセット",
"desc": "キャンバスをリセット"
},
"previousStagingImage": {
"title": "Previous Staging Image",
"desc": "Previous Staging Area Image"
},
"nextStagingImage": {
"title": "Next Staging Image",
"desc": "Next Staging Area Image"
},
"acceptStagingImage": {
"title": "Accept Staging Image",
"desc": "Accept Current Staging Area Image"
}
}

19
frontend/dist/locales/modelmanager/en-US.json vendored
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@ -1,12 +1,18 @@
{ {
"modelManager": "Model Manager", "modelManager": "Model Manager",
"model": "Model", "model": "Model",
"allModels": "All Models",
"checkpointModels": "Checkpoints",
"diffusersModels": "Diffusers",
"safetensorModels": "SafeTensors",
"modelAdded": "Model Added", "modelAdded": "Model Added",
"modelUpdated": "Model Updated", "modelUpdated": "Model Updated",
"modelEntryDeleted": "Model Entry Deleted", "modelEntryDeleted": "Model Entry Deleted",
"cannotUseSpaces": "Cannot Use Spaces", "cannotUseSpaces": "Cannot Use Spaces",
"addNew": "Add New", "addNew": "Add New",
"addNewModel": "Add New Model", "addNewModel": "Add New Model",
"addCheckpointModel": "Add Checkpoint / Safetensor Model",
"addDiffuserModel": "Add Diffusers",
"addManually": "Add Manually", "addManually": "Add Manually",
"manual": "Manual", "manual": "Manual",
"name": "Name", "name": "Name",
@ -17,8 +23,12 @@
"configValidationMsg": "Path to the config file of your model.", "configValidationMsg": "Path to the config file of your model.",
"modelLocation": "Model Location", "modelLocation": "Model Location",
"modelLocationValidationMsg": "Path to where your model is located.", "modelLocationValidationMsg": "Path to where your model is located.",
"repo_id": "Repo ID",
"repoIDValidationMsg": "Online repository of your model",
"vaeLocation": "VAE Location", "vaeLocation": "VAE Location",
"vaeLocationValidationMsg": "Path to where your VAE is located.", "vaeLocationValidationMsg": "Path to where your VAE is located.",
"vaeRepoID": "VAE Repo ID",
"vaeRepoIDValidationMsg": "Online repository of your VAE",
"width": "Width", "width": "Width",
"widthValidationMsg": "Default width of your model.", "widthValidationMsg": "Default width of your model.",
"height": "Height", "height": "Height",
@ -34,6 +44,7 @@
"checkpointFolder": "Checkpoint Folder", "checkpointFolder": "Checkpoint Folder",
"clearCheckpointFolder": "Clear Checkpoint Folder", "clearCheckpointFolder": "Clear Checkpoint Folder",
"findModels": "Find Models", "findModels": "Find Models",
"scanAgain": "Scan Again",
"modelsFound": "Models Found", "modelsFound": "Models Found",
"selectFolder": "Select Folder", "selectFolder": "Select Folder",
"selected": "Selected", "selected": "Selected",
@ -42,9 +53,15 @@
"showExisting": "Show Existing", "showExisting": "Show Existing",
"addSelected": "Add Selected", "addSelected": "Add Selected",
"modelExists": "Model Exists", "modelExists": "Model Exists",
"selectAndAdd": "Select and Add Models Listed Below",
"noModelsFound": "No Models Found",
"delete": "Delete", "delete": "Delete",
"deleteModel": "Delete Model", "deleteModel": "Delete Model",
"deleteConfig": "Delete Config", "deleteConfig": "Delete Config",
"deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?", "deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?",
"deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to." "deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to.",
"formMessageDiffusersModelLocation": "Diffusers Model Location",
"formMessageDiffusersModelLocationDesc": "Please enter at least one.",
"formMessageDiffusersVAELocation": "VAE Location",
"formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above."
} }

16
frontend/dist/locales/modelmanager/en.json vendored
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@ -1,12 +1,18 @@
{ {
"modelManager": "Model Manager", "modelManager": "Model Manager",
"model": "Model", "model": "Model",
"allModels": "All Models",
"checkpointModels": "Checkpoints",
"diffusersModels": "Diffusers",
"safetensorModels": "SafeTensors",
"modelAdded": "Model Added", "modelAdded": "Model Added",
"modelUpdated": "Model Updated", "modelUpdated": "Model Updated",
"modelEntryDeleted": "Model Entry Deleted", "modelEntryDeleted": "Model Entry Deleted",
"cannotUseSpaces": "Cannot Use Spaces", "cannotUseSpaces": "Cannot Use Spaces",
"addNew": "Add New", "addNew": "Add New",
"addNewModel": "Add New Model", "addNewModel": "Add New Model",
"addCheckpointModel": "Add Checkpoint / Safetensor Model",
"addDiffuserModel": "Add Diffusers",
"addManually": "Add Manually", "addManually": "Add Manually",
"manual": "Manual", "manual": "Manual",
"name": "Name", "name": "Name",
@ -17,8 +23,12 @@
"configValidationMsg": "Path to the config file of your model.", "configValidationMsg": "Path to the config file of your model.",
"modelLocation": "Model Location", "modelLocation": "Model Location",
"modelLocationValidationMsg": "Path to where your model is located.", "modelLocationValidationMsg": "Path to where your model is located.",
"repo_id": "Repo ID",
"repoIDValidationMsg": "Online repository of your model",
"vaeLocation": "VAE Location", "vaeLocation": "VAE Location",
"vaeLocationValidationMsg": "Path to where your VAE is located.", "vaeLocationValidationMsg": "Path to where your VAE is located.",
"vaeRepoID": "VAE Repo ID",
"vaeRepoIDValidationMsg": "Online repository of your VAE",
"width": "Width", "width": "Width",
"widthValidationMsg": "Default width of your model.", "widthValidationMsg": "Default width of your model.",
"height": "Height", "height": "Height",
@ -49,5 +59,9 @@
"deleteModel": "Delete Model", "deleteModel": "Delete Model",
"deleteConfig": "Delete Config", "deleteConfig": "Delete Config",
"deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?", "deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?",
"deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to." "deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to.",
"formMessageDiffusersModelLocation": "Diffusers Model Location",
"formMessageDiffusersModelLocationDesc": "Please enter at least one.",
"formMessageDiffusersVAELocation": "VAE Location",
"formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above."
} }

68
frontend/dist/locales/modelmanager/ja.json vendored Normal file
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@ -0,0 +1,68 @@
{
"modelManager": "モデルマネージャ",
"model": "モデル",
"allModels": "すべてのモデル",
"checkpointModels": "Checkpoints",
"diffusersModels": "Diffusers",
"safetensorModels": "SafeTensors",
"modelAdded": "モデルを追加",
"modelUpdated": "モデルをアップデート",
"modelEntryDeleted": "Model Entry Deleted",
"cannotUseSpaces": "Cannot Use Spaces",
"addNew": "新規に追加",
"addNewModel": "新規モデル追加",
"addCheckpointModel": "Checkpointを追加 / Safetensorモデル",
"addDiffuserModel": "Diffusersを追加",
"addManually": "手動で追加",
"manual": "手動",
"name": "名前",
"nameValidationMsg": "モデルの名前を入力",
"description": "概要",
"descriptionValidationMsg": "モデルの概要を入力",
"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。",
"repo_id": "Repo ID",
"repoIDValidationMsg": "モデルのリモートリポジトリ",
"vaeLocation": "VAEの場所",
"vaeLocationValidationMsg": "Vaeが配置されている場所へのパス",
"vaeRepoID": "VAE Repo ID",
"vaeRepoIDValidationMsg": "Vaeのリモートリポジトリ",
"width": "幅",
"widthValidationMsg": "モデルのデフォルトの幅",
"height": "高さ",
"heightValidationMsg": "モデルのデフォルトの高さ",
"addModel": "モデルを追加",
"updateModel": "モデルをアップデート",
"availableModels": "モデルを有効化",
"search": "検索",
"load": "Load",
"active": "active",
"notLoaded": "読み込まれていません",
"cached": "キャッシュ済",
"checkpointFolder": "Checkpointフォルダ",
"clearCheckpointFolder": "Checkpointフォルダ内を削除",
"findModels": "モデルを見つける",
"scanAgain": "再度スキャン",
"modelsFound": "モデルを発見",
"selectFolder": "フォルダを選択",
"selected": "選択済",
"selectAll": "すべて選択",
"deselectAll": "すべて選択解除",
"showExisting": "既存を表示",
"addSelected": "選択済を追加",
"modelExists": "モデルの有無",
"selectAndAdd": "以下のモデルを選択し、追加できます。",
"noModelsFound": "モデルが見つかりません。",
"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。",
"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。"
}

63
frontend/dist/locales/options/ja.json vendored Normal file
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@ -0,0 +1,63 @@
{
"images": "画像",
"steps": "ステップ数",
"cfgScale": "CFG Scale",
"width": "幅",
"height": "高さ",
"sampler": "Sampler",
"seed": "シード値",
"randomizeSeed": "ランダムなシード値",
"shuffle": "シャッフル",
"noiseThreshold": "Noise Threshold",
"perlinNoise": "Perlin Noise",
"variations": "Variations",
"variationAmount": "Variation Amount",
"seedWeights": "シード値の重み",
"faceRestoration": "顔の修復",
"restoreFaces": "顔の修復",
"type": "Type",
"strength": "強度",
"upscaling": "アップスケーリング",
"upscale": "アップスケール",
"upscaleImage": "画像をアップスケール",
"scale": "Scale",
"otherOptions": "その他のオプション",
"seamlessTiling": "Seamless Tiling",
"hiresOptim": "High Res Optimization",
"imageFit": "Fit Initial Image To Output Size",
"codeformerFidelity": "Fidelity",
"seamSize": "Seam Size",
"seamBlur": "Seam Blur",
"seamStrength": "Seam Strength",
"seamSteps": "Seam Steps",
"inpaintReplace": "Inpaint Replace",
"scaleBeforeProcessing": "処理前のスケール",
"scaledWidth": "幅のスケール",
"scaledHeight": "高さのスケール",
"infillMethod": "Infill Method",
"tileSize": "Tile Size",
"boundingBoxHeader": "バウンディングボックス",
"seamCorrectionHeader": "Seam Correction",
"infillScalingHeader": "Infill and Scaling",
"img2imgStrength": "Image To Imageの強度",
"toggleLoopback": "Toggle Loopback",
"invoke": "Invoke",
"cancel": "キャンセル",
"promptPlaceholder": "Type prompt here. [negative tokens], (upweight)++, (downweight)--, swap and blend are available (see docs)",
"sendTo": "転送",
"sendToImg2Img": "Image to Imageに転送",
"sendToUnifiedCanvas": "Unified Canvasに転送",
"copyImageToLink": "Copy Image To Link",
"downloadImage": "画像をダウンロード",
"openInViewer": "ビュワーを開く",
"closeViewer": "ビュワーを閉じる",
"usePrompt": "プロンプトを使用",
"useSeed": "シード値を使用",
"useAll": "すべてを使用",
"useInitImg": "Use Initial Image",
"info": "情報",
"deleteImage": "画像を削除",
"initialImage": "Inital Image",
"showOptionsPanel": "オプションパネルを表示"
}

14
frontend/dist/locales/settings/ja.json vendored Normal file
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@ -0,0 +1,14 @@
{
"models": "モデル",
"displayInProgress": "生成中の画像を表示する",
"saveSteps": "nステップごとに画像を保存",
"confirmOnDelete": "削除時に確認",
"displayHelpIcons": "ヘルプアイコンを表示",
"useCanvasBeta": "キャンバスレイアウト(Beta)を使用する",
"enableImageDebugging": "画像のデバッグを有効化",
"resetWebUI": "WebUIをリセット",
"resetWebUIDesc1": "WebUIのリセットは、画像と保存された設定のキャッシュをリセットするだけです。画像を削除するわけではありません。",
"resetWebUIDesc2": "もしギャラリーに画像が表示されないなど、何か問題が発生した場合はGitHubにissueを提出する前にリセットを試してください。",
"resetComplete": "WebUIはリセットされました。F5を押して再読み込みしてください。"
}

32
frontend/dist/locales/toast/ja.json vendored Normal file
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@ -0,0 +1,32 @@
{
"tempFoldersEmptied": "Temp Folder Emptied",
"uploadFailed": "アップロード失敗",
"uploadFailedMultipleImagesDesc": "一度にアップロードできる画像は1枚のみです。",
"uploadFailedUnableToLoadDesc": "ファイルを読み込むことができません。",
"downloadImageStarted": "画像ダウンロード開始",
"imageCopied": "画像をコピー",
"imageLinkCopied": "画像のURLをコピー",
"imageNotLoaded": "画像を読み込めません。",
"imageNotLoadedDesc": "Image To Imageに転送する画像が見つかりません。",
"imageSavedToGallery": "画像をギャラリーに保存する",
"canvasMerged": "Canvas Merged",
"sentToImageToImage": "Image To Imageに転送",
"sentToUnifiedCanvas": "Unified Canvasに転送",
"parametersSet": "Parameters Set",
"parametersNotSet": "Parameters Not Set",
"parametersNotSetDesc": "この画像にはメタデータがありません。",
"parametersFailed": "パラメータ読み込みの不具合",
"parametersFailedDesc": "initイメージを読み込めません。",
"seedSet": "Seed Set",
"seedNotSet": "Seed Not Set",
"seedNotSetDesc": "この画像のシード値が見つかりません。",
"promptSet": "Prompt Set",
"promptNotSet": "Prompt Not Set",
"promptNotSetDesc": "この画像のプロンプトが見つかりませんでした。",
"upscalingFailed": "アップスケーリング失敗",
"faceRestoreFailed": "顔の修復に失敗",
"metadataLoadFailed": "メタデータの読み込みに失敗。",
"initialImageSet": "Initial Image Set",
"initialImageNotSet": "Initial Image Not Set",
"initialImageNotSetDesc": "Could not load initial image"
}

16
frontend/dist/locales/tooltip/it.json vendored
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@ -1 +1,15 @@
{} {
"feature": {
"prompt": "Questo è il campo del prompt. Il prompt include oggetti di generazione e termini stilistici. Puoi anche aggiungere il peso (importanza del token) nel prompt, ma i comandi e i parametri dell'interfaccia a linea di comando non funzioneranno.",
"gallery": "Galleria visualizza le generazioni dalla cartella degli output man mano che vengono create. Le impostazioni sono memorizzate all'interno di file e accessibili dal menu contestuale.",
"other": "Queste opzioni abiliteranno modalità di elaborazione alternative per Invoke. 'Piastrella senza cuciture' creerà modelli ripetuti nell'output. 'Ottimizzzazione Alta risoluzione' è la generazione in due passaggi con 'Immagine a Immagine': usa questa impostazione quando vuoi un'immagine più grande e più coerente senza artefatti. Ci vorrà più tempo del solito 'Testo a Immagine'.",
"seed": "Il valore del Seme influenza il rumore iniziale da cui è formata l'immagine. Puoi usare i semi già esistenti dalle immagini precedenti. 'Soglia del rumore' viene utilizzato per mitigare gli artefatti a valori CFG elevati (provare l'intervallo 0-10) e Perlin per aggiungere il rumore Perlin durante la generazione: entrambi servono per aggiungere variazioni ai risultati.",
"variations": "Prova una variazione con un valore compreso tra 0.1 e 1.0 per modificare il risultato per un dato seme. Variazioni interessanti del seme sono comprese tra 0.1 e 0.3.",
"upscale": "Utilizza ESRGAN per ingrandire l'immagine subito dopo la generazione.",
"faceCorrection": "Correzione del volto con GFPGAN o Codeformer: l'algoritmo rileva i volti nell'immagine e corregge eventuali difetti. Un valore alto cambierà maggiormente l'immagine, dando luogo a volti più attraenti. Codeformer con una maggiore fedeltà preserva l'immagine originale a scapito di una correzione facciale più forte.",
"imageToImage": "Da Immagine a Immagine carica qualsiasi immagine come iniziale, che viene quindi utilizzata per generarne una nuova in base al prompt. Più alto è il valore, più cambierà l'immagine risultante. Sono possibili valori da 0.0 a 1.0, l'intervallo consigliato è 0.25-0.75",
"boundingBox": "Il riquadro di selezione è lo stesso delle impostazioni Larghezza e Altezza per da Testo a Immagine o da Immagine a Immagine. Verrà elaborata solo l'area nella casella.",
"seamCorrection": "Controlla la gestione delle giunzioni visibili che si verificano tra le immagini generate sulla tela.",
"infillAndScaling": "Gestisce i metodi di riempimento (utilizzati su aree mascherate o cancellate dell'area di disegno) e il ridimensionamento (utile per i riquadri di selezione di piccole dimensioni)."
}
}

16
frontend/dist/locales/tooltip/ja.json vendored Normal file
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@ -0,0 +1,16 @@
{
"feature": {
"prompt": "これはプロンプトフィールドです。プロンプトには生成オブジェクトや文法用語が含まれます。プロンプトにも重み(Tokenの重要度)を付けることができますが、CLIコマンドやパラメータは機能しません。",
"gallery": "ギャラリーは、出力先フォルダから生成物を表示します。設定はファイル内に保存され、コンテキストメニューからアクセスできます。.",
"other": "These options will enable alternative processing modes for Invoke. 'Seamless tiling' will create repeating patterns in the output. 'High resolution' is generation in two steps with img2img: use this setting when you want a larger and more coherent image without artifacts. It will take longer that usual txt2img.",
"seed": "シード値は、画像が形成される際の初期ノイズに影響します。以前の画像から既に存在するシードを使用することができます。ノイズしきい値は高いCFG値でのアーティファクトを軽減するために使用され、Perlinは生成中にPerlinノイズを追加します(0-10の範囲を試してみてください): どちらも出力にバリエーションを追加するのに役立ちます。",
"variations": "0.1から1.0の間の値で試し、付与されたシードに対する結果を変えてみてください。面白いバリュエーションは0.1〜0.3の間です。",
"upscale": "生成直後の画像をアップスケールするには、ESRGANを使用します。",
"faceCorrection": "GFPGANまたはCodeformerによる顔の修復: 画像内の顔を検出し不具合を修正するアルゴリズムです。高い値を設定すると画像がより変化し、より魅力的な顔になります。Codeformerは顔の修復を犠牲にして、元の画像をできる限り保持します。",
"imageToImage": "Image To Imageは任意の画像を初期値として読み込み、プロンプトとともに新しい画像を生成するために使用されます。値が高いほど結果画像はより変化します。0.0から1.0までの値が可能で、推奨範囲は0.25から0.75です。",
"boundingBox": "バウンディングボックスは、Text To ImageまたはImage To Imageの幅/高さの設定と同じです。ボックス内の領域のみが処理されます。",
"seamCorrection": "キャンバス上の生成された画像間に発生する可視可能な境界の処理を制御します。",
"infillAndScaling": "Manage infill methods (used on masked or erased areas of the canvas) and scaling (useful for small bounding box sizes)."
}
}

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{
"feature": {
"prompt": "Questo è il campo del prompt. Il prompt include oggetti di generazione e termini stilistici. Puoi anche aggiungere il peso (importanza del token) nel prompt, ma i comandi e i parametri dell'interfaccia a linea di comando non funzioneranno.",
"gallery": "Galleria visualizza le generazioni dalla cartella degli output man mano che vengono create. Le impostazioni sono memorizzate all'interno di file e accessibili dal menu contestuale.",
"other": "Queste opzioni abiliteranno modalità di elaborazione alternative per Invoke. 'Piastrella senza cuciture' creerà modelli ripetuti nell'output. 'Ottimizzzazione Alta risoluzione' è la generazione in due passaggi con 'Immagine a Immagine': usa questa impostazione quando vuoi un'immagine più grande e più coerente senza artefatti. Ci vorrà più tempo del solito 'Testo a Immagine'.",
"seed": "Il valore del Seme influenza il rumore iniziale da cui è formata l'immagine. Puoi usare i semi già esistenti dalle immagini precedenti. 'Soglia del rumore' viene utilizzato per mitigare gli artefatti a valori CFG elevati (provare l'intervallo 0-10) e Perlin per aggiungere il rumore Perlin durante la generazione: entrambi servono per aggiungere variazioni ai risultati.",
"variations": "Prova una variazione con un valore compreso tra 0.1 e 1.0 per modificare il risultato per un dato seme. Variazioni interessanti del seme sono comprese tra 0.1 e 0.3.",
"upscale": "Utilizza ESRGAN per ingrandire l'immagine subito dopo la generazione.",
"faceCorrection": "Correzione del volto con GFPGAN o Codeformer: l'algoritmo rileva i volti nell'immagine e corregge eventuali difetti. Un valore alto cambierà maggiormente l'immagine, dando luogo a volti più attraenti. Codeformer con una maggiore fedeltà preserva l'immagine originale a scapito di una correzione facciale più forte.",
"imageToImage": "Da Immagine a Immagine carica qualsiasi immagine come iniziale, che viene quindi utilizzata per generarne una nuova in base al prompt. Più alto è il valore, più cambierà l'immagine risultante. Sono possibili valori da 0.0 a 1.0, l'intervallo consigliato è 0.25-0.75",
"boundingBox": "Il riquadro di selezione è lo stesso delle impostazioni Larghezza e Altezza per dat Testo a Immagine o da Immagine a Immagine. Verrà elaborata solo l'area nella casella.",
"seamCorrection": "Controlla la gestione delle giunzioni visibili che si verificano tra le immagini generate sulla tela.",
"infillAndScaling": "Gestisce i metodi di riempimento (utilizzati su aree mascherate o cancellate dell'area di disegno) e il ridimensionamento (utile per i riquadri di selezione di piccole dimensioni)."
}
}

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{
"layer": "Layer",
"base": "Base",
"mask": "マスク",
"maskingOptions": "マスクのオプション",
"enableMask": "マスクを有効化",
"preserveMaskedArea": "マスク領域の保存",
"clearMask": "マスクを解除",
"brush": "ブラシ",
"eraser": "消しゴム",
"fillBoundingBox": "バウンディングボックスの塗りつぶし",
"eraseBoundingBox": "バウンディングボックスの消去",
"colorPicker": "カラーピッカー",
"brushOptions": "ブラシオプション",
"brushSize": "サイズ",
"move": "Move",
"resetView": "Reset View",
"mergeVisible": "Merge Visible",
"saveToGallery": "ギャラリーに保存",
"copyToClipboard": "クリップボードにコピー",
"downloadAsImage": "画像としてダウンロード",
"undo": "取り消し",
"redo": "やり直し",
"clearCanvas": "キャンバスを片付ける",
"canvasSettings": "キャンバスの設定",
"showIntermediates": "Show Intermediates",
"showGrid": "グリッドを表示",
"snapToGrid": "Snap to Grid",
"darkenOutsideSelection": "外周を暗くする",
"autoSaveToGallery": "ギャラリーに自動保存",
"saveBoxRegionOnly": "ボックス領域のみ保存",
"limitStrokesToBox": "Limit Strokes to Box",
"showCanvasDebugInfo": "キャンバスのデバッグ情報を表示",
"clearCanvasHistory": "キャンバスの履歴を削除",
"clearHistory": "履歴を削除",
"clearCanvasHistoryMessage": "履歴を消去すると現在のキャンバスは残りますが、取り消しややり直しの履歴は不可逆的に消去されます。",
"clearCanvasHistoryConfirm": "履歴を削除しますか?",
"emptyTempImageFolder": "Empty Temp Image Folde",
"emptyFolder": "空のフォルダ",
"emptyTempImagesFolderMessage": "一時フォルダを空にすると、Unified Canvasも完全にリセットされます。これには、すべての取り消し/やり直しの履歴、ステージング領域の画像、およびキャンバスのベースレイヤーが含まれます。",
"emptyTempImagesFolderConfirm": "一時フォルダを削除しますか?",
"activeLayer": "Active Layer",
"canvasScale": "Canvas Scale",
"boundingBox": "バウンディングボックス",
"scaledBoundingBox": "Scaled Bounding Box",
"boundingBoxPosition": "バウンディングボックスの位置",
"canvasDimensions": "キャンバスの大きさ",
"canvasPosition": "キャンバスの位置",
"cursorPosition": "カーソルの位置",
"previous": "前",
"next": "次",
"accept": "同意",
"showHide": "表示/非表示",
"discardAll": "すべて破棄",
"betaClear": "Clear",
"betaDarkenOutside": "Darken Outside",
"betaLimitToBox": "Limit To Box",
"betaPreserveMasked": "Preserve Masked"
}

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@ -19,6 +19,7 @@
"langPolish": "Polish", "langPolish": "Polish",
"langSimplifiedChinese": "Simplified Chinese", "langSimplifiedChinese": "Simplified Chinese",
"langSpanish": "Spanish", "langSpanish": "Spanish",
"langJapanese": "Japanese",
"text2img": "Text To Image", "text2img": "Text To Image",
"img2img": "Image To Image", "img2img": "Image To Image",
"unifiedCanvas": "Unified Canvas", "unifiedCanvas": "Unified Canvas",

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"langPolish": "Polish", "langPolish": "Polish",
"langSimplifiedChinese": "Simplified Chinese", "langSimplifiedChinese": "Simplified Chinese",
"langSpanish": "Spanish", "langSpanish": "Spanish",
"langJapanese": "Japanese",
"text2img": "Text To Image", "text2img": "Text To Image",
"img2img": "Image To Image", "img2img": "Image To Image",
"unifiedCanvas": "Unified Canvas", "unifiedCanvas": "Unified Canvas",

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{
"hotkeysLabel": "Hotkeys",
"themeLabel": "テーマ",
"languagePickerLabel": "言語選択",
"reportBugLabel": "バグ報告",
"githubLabel": "Github",
"discordLabel": "Discord",
"settingsLabel": "設定",
"darkTheme": "ダーク",
"lightTheme": "ライト",
"greenTheme": "緑",
"langEnglish": "English",
"langRussian": "Russian",
"langItalian": "Italian",
"langBrPortuguese": "Portuguese (Brazilian)",
"langGerman": "German",
"langPortuguese": "Portuguese",
"langFrench": "French",
"langPolish": "Polish",
"langSimplifiedChinese": "Simplified Chinese",
"langSpanish": "Spanish",
"text2img": "Text To Image",
"img2img": "Image To Image",
"unifiedCanvas": "Unified Canvas",
"nodes": "Nodes",
"nodesDesc": "現在、画像生成のためのノードベースシステムを開発中です。機能についてのアップデートにご期待ください。",
"postProcessing": "後処理",
"postProcessDesc1": "Invoke AIは、多彩な後処理の機能を備えています。アップスケーリングと顔修復は、すでにWebUI上で利用可能です。これらは、[Text To Image]および[Image To Image]タブの[詳細オプション]メニューからアクセスできます。また、現在の画像表示の上やビューア内の画像アクションボタンを使って、画像を直接処理することもできます。",
"postProcessDesc2": "より高度な後処理の機能を実現するための専用UIを近日中にリリース予定です。",
"postProcessDesc3": "Invoke AI CLIでは、この他にもEmbiggenをはじめとする様々な機能を利用することができます。",
"training": "追加学習",
"trainingDesc1": "Textual InversionとDreamboothを使って、WebUIから独自のEmbeddingとチェックポイントを追加学習するための専用ワークフローです。",
"trainingDesc2": "InvokeAIは、すでにメインスクリプトを使ったTextual Inversionによるカスタム埋め込み追加学習にも対応しています。",
"upload": "アップロード",
"close": "閉じる",
"load": "ロード",
"back": "戻る",
"statusConnected": "接続済",
"statusDisconnected": "切断済",
"statusError": "エラー",
"statusPreparing": "準備中",
"statusProcessingCanceled": "処理をキャンセル",
"statusProcessingComplete": "処理完了",
"statusGenerating": "生成中",
"statusGeneratingTextToImage": "Text To Imageで生成中",
"statusGeneratingImageToImage": "Image To Imageで生成中",
"statusGeneratingInpainting": "Generating Inpainting",
"statusGeneratingOutpainting": "Generating Outpainting",
"statusGenerationComplete": "生成完了",
"statusIterationComplete": "Iteration Complete",
"statusSavingImage": "画像を保存",
"statusRestoringFaces": "顔の修復",
"statusRestoringFacesGFPGAN": "顔の修復 (GFPGAN)",
"statusRestoringFacesCodeFormer": "顔の修復 (CodeFormer)",
"statusUpscaling": "アップスケーリング",
"statusUpscalingESRGAN": "アップスケーリング (ESRGAN)",
"statusLoadingModel": "モデルを読み込む",
"statusModelChanged": "モデルを変更"
}

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{
"generations": "Generations",
"showGenerations": "Show Generations",
"uploads": "アップロード",
"showUploads": "アップロードした画像を見る",
"galleryImageSize": "画像のサイズ",
"galleryImageResetSize": "サイズをリセット",
"gallerySettings": "ギャラリーの設定",
"maintainAspectRatio": "アスペクト比を維持",
"autoSwitchNewImages": "Auto-Switch to New Images",
"singleColumnLayout": "シングルカラムレイアウト",
"pinGallery": "ギャラリーにピン留め",
"allImagesLoaded": "すべての画像を読み込む",
"loadMore": "さらに読み込む",
"noImagesInGallery": "ギャラリーに画像がありません"
}

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{
"keyboardShortcuts": "キーボードショートカット",
"appHotkeys": "アプリのホットキー",
"generalHotkeys": "Generalのホットキー",
"galleryHotkeys": "ギャラリーのホットキー",
"unifiedCanvasHotkeys": "Unified Canvasのホットキー",
"invoke": {
"title": "Invoke",
"desc": "画像を生成"
},
"cancel": {
"title": "キャンセル",
"desc": "画像の生成をキャンセル"
},
"focusPrompt": {
"title": "Focus Prompt",
"desc": "プロンプトテキストボックスにフォーカス"
},
"toggleOptions": {
"title": "オプションパネルのトグル",
"desc": "オプションパネルの開閉"
},
"pinOptions": {
"title": "ピン",
"desc": "オプションパネルを固定"
},
"toggleViewer": {
"title": "ビュワーのトグル",
"desc": "ビュワーを開閉"
},
"toggleGallery": {
"title": "ギャラリーのトグル",
"desc": "ギャラリードロワーの開閉"
},
"maximizeWorkSpace": {
"title": "作業領域の最大化",
"desc": "パネルを閉じて、作業領域を最大に"
},
"changeTabs": {
"title": "タブの切替",
"desc": "他の作業領域と切替"
},
"consoleToggle": {
"title": "コンソールのトグル",
"desc": "コンソールの開閉"
},
"setPrompt": {
"title": "プロンプトをセット",
"desc": "現在の画像のプロンプトを使用"
},
"setSeed": {
"title": "シード値をセット",
"desc": "現在の画像のシード値を使用"
},
"setParameters": {
"title": "パラメータをセット",
"desc": "現在の画像のすべてのパラメータを使用"
},
"restoreFaces": {
"title": "顔の修復",
"desc": "現在の画像を修復"
},
"upscale": {
"title": "アップスケール",
"desc": "現在の画像をアップスケール"
},
"showInfo": {
"title": "情報を見る",
"desc": "現在の画像のメタデータ情報を表示"
},
"sendToImageToImage": {
"title": "Image To Imageに転送",
"desc": "現在の画像をImage to Imageに転送"
},
"deleteImage": {
"title": "画像を削除",
"desc": "現在の画像を削除"
},
"closePanels": {
"title": "パネルを閉じる",
"desc": "開いているパネルを閉じる"
},
"previousImage": {
"title": "前の画像",
"desc": "ギャラリー内の1つ前の画像を表示"
},
"nextImage": {
"title": "次の画像",
"desc": "ギャラリー内の1つ後の画像を表示"
},
"toggleGalleryPin": {
"title": "ギャラリードロワーの固定",
"desc": "ギャラリーをUIにピン留め/解除"
},
"increaseGalleryThumbSize": {
"title": "ギャラリーの画像を拡大",
"desc": "ギャラリーのサムネイル画像を拡大"
},
"decreaseGalleryThumbSize": {
"title": "ギャラリーの画像サイズを縮小",
"desc": "ギャラリーのサムネイル画像を縮小"
},
"selectBrush": {
"title": "ブラシを選択",
"desc": "ブラシを選択"
},
"selectEraser": {
"title": "消しゴムを選択",
"desc": "消しゴムを選択"
},
"decreaseBrushSize": {
"title": "ブラシサイズを縮小",
"desc": "ブラシ/消しゴムのサイズを縮小"
},
"increaseBrushSize": {
"title": "ブラシサイズを拡大",
"desc": "ブラシ/消しゴムのサイズを拡大"
},
"decreaseBrushOpacity": {
"title": "ブラシの不透明度を下げる",
"desc": "キャンバスブラシの不透明度を下げる"
},
"increaseBrushOpacity": {
"title": "ブラシの不透明度を上げる",
"desc": "キャンバスブラシの不透明度を上げる"
},
"moveTool": {
"title": "Move Tool",
"desc": "Allows canvas navigation"
},
"fillBoundingBox": {
"title": "バウンディングボックスを塗りつぶす",
"desc": "ブラシの色でバウンディングボックス領域を塗りつぶす"
},
"eraseBoundingBox": {
"title": "バウンディングボックスを消す",
"desc": "バウンディングボックス領域を消す"
},
"colorPicker": {
"title": "カラーピッカーを選択",
"desc": "カラーピッカーを選択"
},
"toggleSnap": {
"title": "Toggle Snap",
"desc": "Toggles Snap to Grid"
},
"quickToggleMove": {
"title": "Quick Toggle Move",
"desc": "Temporarily toggles Move mode"
},
"toggleLayer": {
"title": "レイヤーを切替",
"desc": "マスク/ベースレイヤの選択を切替"
},
"clearMask": {
"title": "マスクを消す",
"desc": "マスク全体を消す"
},
"hideMask": {
"title": "マスクを非表示",
"desc": "マスクを表示/非表示"
},
"showHideBoundingBox": {
"title": "バウンディングボックスを表示/非表示",
"desc": "バウンディングボックスの表示/非表示を切替"
},
"mergeVisible": {
"title": "Merge Visible",
"desc": "Merge all visible layers of canvas"
},
"saveToGallery": {
"title": "ギャラリーに保存",
"desc": "現在のキャンバスをギャラリーに保存"
},
"copyToClipboard": {
"title": "クリップボードにコピー",
"desc": "現在のキャンバスをクリップボードにコピー"
},
"downloadImage": {
"title": "画像をダウンロード",
"desc": "現在の画像をダウンロード"
},
"undoStroke": {
"title": "Undo Stroke",
"desc": "Undo a brush stroke"
},
"redoStroke": {
"title": "Redo Stroke",
"desc": "Redo a brush stroke"
},
"resetView": {
"title": "キャンバスをリセット",
"desc": "キャンバスをリセット"
},
"previousStagingImage": {
"title": "Previous Staging Image",
"desc": "Previous Staging Area Image"
},
"nextStagingImage": {
"title": "Next Staging Image",
"desc": "Next Staging Area Image"
},
"acceptStagingImage": {
"title": "Accept Staging Image",
"desc": "Accept Current Staging Area Image"
}
}

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{
"modelManager": "モデルマネージャ",
"model": "モデル",
"allModels": "すべてのモデル",
"checkpointModels": "Checkpoints",
"diffusersModels": "Diffusers",
"safetensorModels": "SafeTensors",
"modelAdded": "モデルを追加",
"modelUpdated": "モデルをアップデート",
"modelEntryDeleted": "Model Entry Deleted",
"cannotUseSpaces": "Cannot Use Spaces",
"addNew": "新規に追加",
"addNewModel": "新規モデル追加",
"addCheckpointModel": "Checkpointを追加 / Safetensorモデル",
"addDiffuserModel": "Diffusersを追加",
"addManually": "手動で追加",
"manual": "手動",
"name": "名前",
"nameValidationMsg": "モデルの名前を入力",
"description": "概要",
"descriptionValidationMsg": "モデルの概要を入力",
"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。",
"repo_id": "Repo ID",
"repoIDValidationMsg": "モデルのリモートリポジトリ",
"vaeLocation": "VAEの場所",
"vaeLocationValidationMsg": "Vaeが配置されている場所へのパス",
"vaeRepoID": "VAE Repo ID",
"vaeRepoIDValidationMsg": "Vaeのリモートリポジトリ",
"width": "幅",
"widthValidationMsg": "モデルのデフォルトの幅",
"height": "高さ",
"heightValidationMsg": "モデルのデフォルトの高さ",
"addModel": "モデルを追加",
"updateModel": "モデルをアップデート",
"availableModels": "モデルを有効化",
"search": "検索",
"load": "Load",
"active": "active",
"notLoaded": "読み込まれていません",
"cached": "キャッシュ済",
"checkpointFolder": "Checkpointフォルダ",
"clearCheckpointFolder": "Checkpointフォルダ内を削除",
"findModels": "モデルを見つける",
"scanAgain": "再度スキャン",
"modelsFound": "モデルを発見",
"selectFolder": "フォルダを選択",
"selected": "選択済",
"selectAll": "すべて選択",
"deselectAll": "すべて選択解除",
"showExisting": "既存を表示",
"addSelected": "選択済を追加",
"modelExists": "モデルの有無",
"selectAndAdd": "以下のモデルを選択し、追加できます。",
"noModelsFound": "モデルが見つかりません。",
"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。",
"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。"
}

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{
"images": "画像",
"steps": "ステップ数",
"cfgScale": "CFG Scale",
"width": "幅",
"height": "高さ",
"sampler": "Sampler",
"seed": "シード値",
"randomizeSeed": "ランダムなシード値",
"shuffle": "シャッフル",
"noiseThreshold": "Noise Threshold",
"perlinNoise": "Perlin Noise",
"variations": "Variations",
"variationAmount": "Variation Amount",
"seedWeights": "シード値の重み",
"faceRestoration": "顔の修復",
"restoreFaces": "顔の修復",
"type": "Type",
"strength": "強度",
"upscaling": "アップスケーリング",
"upscale": "アップスケール",
"upscaleImage": "画像をアップスケール",
"scale": "Scale",
"otherOptions": "その他のオプション",
"seamlessTiling": "Seamless Tiling",
"hiresOptim": "High Res Optimization",
"imageFit": "Fit Initial Image To Output Size",
"codeformerFidelity": "Fidelity",
"seamSize": "Seam Size",
"seamBlur": "Seam Blur",
"seamStrength": "Seam Strength",
"seamSteps": "Seam Steps",
"inpaintReplace": "Inpaint Replace",
"scaleBeforeProcessing": "処理前のスケール",
"scaledWidth": "幅のスケール",
"scaledHeight": "高さのスケール",
"infillMethod": "Infill Method",
"tileSize": "Tile Size",
"boundingBoxHeader": "バウンディングボックス",
"seamCorrectionHeader": "Seam Correction",
"infillScalingHeader": "Infill and Scaling",
"img2imgStrength": "Image To Imageの強度",
"toggleLoopback": "Toggle Loopback",
"invoke": "Invoke",
"cancel": "キャンセル",
"promptPlaceholder": "Type prompt here. [negative tokens], (upweight)++, (downweight)--, swap and blend are available (see docs)",
"sendTo": "転送",
"sendToImg2Img": "Image to Imageに転送",
"sendToUnifiedCanvas": "Unified Canvasに転送",
"copyImageToLink": "Copy Image To Link",
"downloadImage": "画像をダウンロード",
"openInViewer": "ビュワーを開く",
"closeViewer": "ビュワーを閉じる",
"usePrompt": "プロンプトを使用",
"useSeed": "シード値を使用",
"useAll": "すべてを使用",
"useInitImg": "Use Initial Image",
"info": "情報",
"deleteImage": "画像を削除",
"initialImage": "Inital Image",
"showOptionsPanel": "オプションパネルを表示"
}

14
frontend/public/locales/settings/ja.json Normal file
View File

@ -0,0 +1,14 @@
{
"models": "モデル",
"displayInProgress": "生成中の画像を表示する",
"saveSteps": "nステップごとに画像を保存",
"confirmOnDelete": "削除時に確認",
"displayHelpIcons": "ヘルプアイコンを表示",
"useCanvasBeta": "キャンバスレイアウト(Beta)を使用する",
"enableImageDebugging": "画像のデバッグを有効化",
"resetWebUI": "WebUIをリセット",
"resetWebUIDesc1": "WebUIのリセットは、画像と保存された設定のキャッシュをリセットするだけです。画像を削除するわけではありません。",
"resetWebUIDesc2": "もしギャラリーに画像が表示されないなど、何か問題が発生した場合はGitHubにissueを提出する前にリセットを試してください。",
"resetComplete": "WebUIはリセットされました。F5を押して再読み込みしてください。"
}

32
frontend/public/locales/toast/ja.json Normal file
View File

@ -0,0 +1,32 @@
{
"tempFoldersEmptied": "Temp Folder Emptied",
"uploadFailed": "アップロード失敗",
"uploadFailedMultipleImagesDesc": "一度にアップロードできる画像は1枚のみです。",
"uploadFailedUnableToLoadDesc": "ファイルを読み込むことができません。",
"downloadImageStarted": "画像ダウンロード開始",
"imageCopied": "画像をコピー",
"imageLinkCopied": "画像のURLをコピー",
"imageNotLoaded": "画像を読み込めません。",
"imageNotLoadedDesc": "Image To Imageに転送する画像が見つかりません。",
"imageSavedToGallery": "画像をギャラリーに保存する",
"canvasMerged": "Canvas Merged",
"sentToImageToImage": "Image To Imageに転送",
"sentToUnifiedCanvas": "Unified Canvasに転送",
"parametersSet": "Parameters Set",
"parametersNotSet": "Parameters Not Set",
"parametersNotSetDesc": "この画像にはメタデータがありません。",
"parametersFailed": "パラメータ読み込みの不具合",
"parametersFailedDesc": "initイメージを読み込めません。",
"seedSet": "Seed Set",
"seedNotSet": "Seed Not Set",
"seedNotSetDesc": "この画像のシード値が見つかりません。",
"promptSet": "Prompt Set",
"promptNotSet": "Prompt Not Set",
"promptNotSetDesc": "この画像のプロンプトが見つかりませんでした。",
"upscalingFailed": "アップスケーリング失敗",
"faceRestoreFailed": "顔の修復に失敗",
"metadataLoadFailed": "メタデータの読み込みに失敗。",
"initialImageSet": "Initial Image Set",
"initialImageNotSet": "Initial Image Not Set",
"initialImageNotSetDesc": "Could not load initial image"
}

16
frontend/public/locales/tooltip/ja.json Normal file
View File

@ -0,0 +1,16 @@
{
"feature": {
"prompt": "これはプロンプトフィールドです。プロンプトには生成オブジェクトや文法用語が含まれます。プロンプトにも重み(Tokenの重要度)を付けることができますが、CLIコマンドやパラメータは機能しません。",
"gallery": "ギャラリーは、出力先フォルダから生成物を表示します。設定はファイル内に保存され、コンテキストメニューからアクセスできます。.",
"other": "These options will enable alternative processing modes for Invoke. 'Seamless tiling' will create repeating patterns in the output. 'High resolution' is generation in two steps with img2img: use this setting when you want a larger and more coherent image without artifacts. It will take longer that usual txt2img.",
"seed": "シード値は、画像が形成される際の初期ノイズに影響します。以前の画像から既に存在するシードを使用することができます。ノイズしきい値は高いCFG値でのアーティファクトを軽減するために使用され、Perlinは生成中にPerlinノイズを追加します(0-10の範囲を試してみてください): どちらも出力にバリエーションを追加するのに役立ちます。",
"variations": "0.1から1.0の間の値で試し、付与されたシードに対する結果を変えてみてください。面白いバリュエーションは0.1〜0.3の間です。",
"upscale": "生成直後の画像をアップスケールするには、ESRGANを使用します。",
"faceCorrection": "GFPGANまたはCodeformerによる顔の修復: 画像内の顔を検出し不具合を修正するアルゴリズムです。高い値を設定すると画像がより変化し、より魅力的な顔になります。Codeformerは顔の修復を犠牲にして、元の画像をできる限り保持します。",
"imageToImage": "Image To Imageは任意の画像を初期値として読み込み、プロンプトとともに新しい画像を生成するために使用されます。値が高いほど結果画像はより変化します。0.0から1.0までの値が可能で、推奨範囲は0.25から0.75です。",
"boundingBox": "バウンディングボックスは、Text To ImageまたはImage To Imageの幅/高さの設定と同じです。ボックス内の領域のみが処理されます。",
"seamCorrection": "キャンバス上の生成された画像間に発生する可視可能な境界の処理を制御します。",
"infillAndScaling": "Manage infill methods (used on masked or erased areas of the canvas) and scaling (useful for small bounding box sizes)."
}
}

60
frontend/public/locales/unifiedcanvas/ja.json Normal file
View File

@ -0,0 +1,60 @@
{
"layer": "Layer",
"base": "Base",
"mask": "マスク",
"maskingOptions": "マスクのオプション",
"enableMask": "マスクを有効化",
"preserveMaskedArea": "マスク領域の保存",
"clearMask": "マスクを解除",
"brush": "ブラシ",
"eraser": "消しゴム",
"fillBoundingBox": "バウンディングボックスの塗りつぶし",
"eraseBoundingBox": "バウンディングボックスの消去",
"colorPicker": "カラーピッカー",
"brushOptions": "ブラシオプション",
"brushSize": "サイズ",
"move": "Move",
"resetView": "Reset View",
"mergeVisible": "Merge Visible",
"saveToGallery": "ギャラリーに保存",
"copyToClipboard": "クリップボードにコピー",
"downloadAsImage": "画像としてダウンロード",
"undo": "取り消し",
"redo": "やり直し",
"clearCanvas": "キャンバスを片付ける",
"canvasSettings": "キャンバスの設定",
"showIntermediates": "Show Intermediates",
"showGrid": "グリッドを表示",
"snapToGrid": "Snap to Grid",
"darkenOutsideSelection": "外周を暗くする",
"autoSaveToGallery": "ギャラリーに自動保存",
"saveBoxRegionOnly": "ボックス領域のみ保存",
"limitStrokesToBox": "Limit Strokes to Box",
"showCanvasDebugInfo": "キャンバスのデバッグ情報を表示",
"clearCanvasHistory": "キャンバスの履歴を削除",
"clearHistory": "履歴を削除",
"clearCanvasHistoryMessage": "履歴を消去すると現在のキャンバスは残りますが、取り消しややり直しの履歴は不可逆的に消去されます。",
"clearCanvasHistoryConfirm": "履歴を削除しますか?",
"emptyTempImageFolder": "Empty Temp Image Folde",
"emptyFolder": "空のフォルダ",
"emptyTempImagesFolderMessage": "一時フォルダを空にすると、Unified Canvasも完全にリセットされます。これには、すべての取り消し/やり直しの履歴、ステージング領域の画像、およびキャンバスのベースレイヤーが含まれます。",
"emptyTempImagesFolderConfirm": "一時フォルダを削除しますか?",
"activeLayer": "Active Layer",
"canvasScale": "Canvas Scale",
"boundingBox": "バウンディングボックス",
"scaledBoundingBox": "Scaled Bounding Box",
"boundingBoxPosition": "バウンディングボックスの位置",
"canvasDimensions": "キャンバスの大きさ",
"canvasPosition": "キャンバスの位置",
"cursorPosition": "カーソルの位置",
"previous": "前",
"next": "次",
"accept": "同意",
"showHide": "表示/非表示",
"discardAll": "すべて破棄",
"betaClear": "Clear",
"betaDarkenOutside": "Darken Outside",
"betaLimitToBox": "Limit To Box",
"betaPreserveMasked": "Preserve Masked"
}

1
frontend/src/features/system/components/LanguagePicker.tsx
View File

@ -20,6 +20,7 @@ export default function LanguagePicker() {
pl: t('common:langPolish'), pl: t('common:langPolish'),
zh_cn: t('common:langSimplifiedChinese'), zh_cn: t('common:langSimplifiedChinese'),
es: t('common:langSpanish'), es: t('common:langSpanish'),
ja: t('common:langJapanese'),
}; };
const renderLanguagePicker = () => { const renderLanguagePicker = () => {

10
installer/templates/invoke.bat.in
View File

@ -10,8 +10,9 @@ echo Do you want to generate images using the
echo 1. command-line echo 1. command-line
echo 2. browser-based UI echo 2. browser-based UI
echo 3. run textual inversion training echo 3. run textual inversion training
echo 4. open the developer console echo 4. merge models (diffusers type only)
echo 5. re-run the configure script to download new models echo 5. open the developer console
echo 6. re-run the configure script to download new models
set /P restore="Please enter 1, 2, 3, 4 or 5: [5] " set /P restore="Please enter 1, 2, 3, 4 or 5: [5] "
if not defined restore set restore=2 if not defined restore set restore=2
IF /I "%restore%" == "1" ( IF /I "%restore%" == "1" (
@ -24,6 +25,9 @@ IF /I "%restore%" == "1" (
echo Starting textual inversion training.. echo Starting textual inversion training..
python .venv\Scripts\textual_inversion_fe.py --web %* python .venv\Scripts\textual_inversion_fe.py --web %*
) ELSE IF /I "%restore%" == "4" ( ) ELSE IF /I "%restore%" == "4" (
echo Starting model merging script..
python .venv\Scripts\merge_models_fe.py --web %*
) ELSE IF /I "%restore%" == "5" (
echo Developer Console echo Developer Console
echo Python command is: echo Python command is:
where python where python
@ -35,7 +39,7 @@ IF /I "%restore%" == "1" (
echo ************************* echo *************************
echo *** Type `exit` to quit this shell and deactivate the Python virtual environment *** echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
call cmd /k call cmd /k
) ELSE IF /I "%restore%" == "5" ( ) ELSE IF /I "%restore%" == "6" (
echo Running configure_invokeai.py... echo Running configure_invokeai.py...
python .venv\Scripts\configure_invokeai.py --web %* python .venv\Scripts\configure_invokeai.py --web %*
) ELSE ( ) ELSE (

8
installer/templates/invoke.sh.in
View File

@ -20,16 +20,18 @@ if [ "$0" != "bash" ]; then
echo "1. command-line" echo "1. command-line"
echo "2. browser-based UI" echo "2. browser-based UI"
echo "3. run textual inversion training" echo "3. run textual inversion training"
echo "4. open the developer console" echo "4. merge models (diffusers type only)"
echo "5. re-run the configure script to download new models" echo "5. re-run the configure script to download new models"
echo "6. open the developer console"
read -p "Please enter 1, 2, 3, 4 or 5: [1] " yn read -p "Please enter 1, 2, 3, 4 or 5: [1] " yn
choice=${yn:='2'} choice=${yn:='2'}
case $choice in case $choice in
1 ) printf "\nStarting the InvokeAI command-line..\n"; .venv/bin/python .venv/bin/invoke.py $*;; 1 ) printf "\nStarting the InvokeAI command-line..\n"; .venv/bin/python .venv/bin/invoke.py $*;;
2 ) printf "\nStarting the InvokeAI browser-based UI..\n"; .venv/bin/python .venv/bin/invoke.py --web $*;; 2 ) printf "\nStarting the InvokeAI browser-based UI..\n"; .venv/bin/python .venv/bin/invoke.py --web $*;;
3 ) printf "\nStarting Textual Inversion:\n"; .venv/bin/python .venv/bin/textual_inversion_fe.py $*;; 3 ) printf "\nStarting Textual Inversion:\n"; .venv/bin/python .venv/bin/textual_inversion_fe.py $*;;
4 ) printf "\nDeveloper Console:\n"; file_name=$(basename "${BASH_SOURCE[0]}"); bash --init-file "$file_name";; 4 ) printf "\nMerging Models:\n"; .venv/bin/python .venv/bin/merge_models_fe.py $*;;
5 ) printf "\nRunning configure_invokeai.py:\n"; .venv/bin/python .venv/bin/configure_invokeai.py $*;; 5 ) printf "\nDeveloper Console:\n"; file_name=$(basename "${BASH_SOURCE[0]}"); bash --init-file "$file_name";;
6 ) printf "\nRunning configure_invokeai.py:\n"; .venv/bin/python .venv/bin/configure_invokeai.py $*;;
* ) echo "Invalid selection"; exit;; * ) echo "Invalid selection"; exit;;
esac esac
else # in developer console else # in developer console

14
ldm/generate.py
View File

@ -146,7 +146,7 @@ class Generate:
gfpgan=None, gfpgan=None,
codeformer=None, codeformer=None,
esrgan=None, esrgan=None,
free_gpu_mem=False, free_gpu_mem: bool=False,
safety_checker:bool=False, safety_checker:bool=False,
max_loaded_models:int=2, max_loaded_models:int=2,
# these are deprecated; if present they override values in the conf file # these are deprecated; if present they override values in the conf file
@ -445,7 +445,11 @@ class Generate:
self._set_sampler() self._set_sampler()
# apply the concepts library to the prompt # apply the concepts library to the prompt
prompt = self.huggingface_concepts_library.replace_concepts_with_triggers(prompt, lambda concepts: self.load_huggingface_concepts(concepts)) prompt = self.huggingface_concepts_library.replace_concepts_with_triggers(
prompt,
lambda concepts: self.load_huggingface_concepts(concepts),
self.model.textual_inversion_manager.get_all_trigger_strings()
)
# bit of a hack to change the cached sampler's karras threshold to # bit of a hack to change the cached sampler's karras threshold to
# whatever the user asked for # whatever the user asked for
@ -460,10 +464,13 @@ class Generate:
init_image = None init_image = None
mask_image = None mask_image = None
try:
if self.free_gpu_mem and self.model.cond_stage_model.device != self.model.device: if self.free_gpu_mem and self.model.cond_stage_model.device != self.model.device:
self.model.cond_stage_model.device = self.model.device self.model.cond_stage_model.device = self.model.device
self.model.cond_stage_model.to(self.model.device) self.model.cond_stage_model.to(self.model.device)
except AttributeError:
print(">> Warning: '--free_gpu_mem' is not yet supported when generating image using model based on HuggingFace Diffuser.")
pass
try: try:
uc, c, extra_conditioning_info = get_uc_and_c_and_ec( uc, c, extra_conditioning_info = get_uc_and_c_and_ec(
@ -531,6 +538,7 @@ class Generate:
inpaint_height = inpaint_height, inpaint_height = inpaint_height,
inpaint_width = inpaint_width, inpaint_width = inpaint_width,
enable_image_debugging = enable_image_debugging, enable_image_debugging = enable_image_debugging,
free_gpu_mem=self.free_gpu_mem,
) )
if init_color: if init_color:

6
ldm/invoke/CLI.py
View File

@ -573,7 +573,7 @@ def import_model(model_path:str, gen, opt, completer):
if model_path.startswith(('http:','https:','ftp:')): if model_path.startswith(('http:','https:','ftp:')):
model_name = import_ckpt_model(model_path, gen, opt, completer) model_name = import_ckpt_model(model_path, gen, opt, completer)
elif os.path.exists(model_path) and model_path.endswith('.ckpt') and os.path.isfile(model_path): elif os.path.exists(model_path) and model_path.endswith(('.ckpt','.safetensors')) and os.path.isfile(model_path):
model_name = import_ckpt_model(model_path, gen, opt, completer) model_name = import_ckpt_model(model_path, gen, opt, completer)
elif re.match('^[\w.+-]+/[\w.+-]+$',model_path): elif re.match('^[\w.+-]+/[\w.+-]+$',model_path):
model_name = import_diffuser_model(model_path, gen, opt, completer) model_name = import_diffuser_model(model_path, gen, opt, completer)
@ -627,9 +627,9 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
model_description=default_description model_description=default_description
) )
config_file = None config_file = None
default = Path(Globals.root,'configs/stable-diffusion/v1-inference.yaml')
completer.complete_extensions(('.yaml','.yml')) completer.complete_extensions(('.yaml','.yml'))
completer.set_line('configs/stable-diffusion/v1-inference.yaml') completer.set_line(str(default))
done = False done = False
while not done: while not done:
config_file = input('Configuration file for this model: ').strip() config_file = input('Configuration file for this model: ').strip()

2
ldm/invoke/ckpt_generator/base.py
View File

@ -56,9 +56,11 @@ class CkptGenerator():
image_callback=None, step_callback=None, threshold=0.0, perlin=0.0, image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
safety_checker:dict=None, safety_checker:dict=None,
attention_maps_callback = None, attention_maps_callback = None,
free_gpu_mem: bool=False,
**kwargs): **kwargs):
scope = choose_autocast(self.precision) scope = choose_autocast(self.precision)
self.safety_checker = safety_checker self.safety_checker = safety_checker
self.free_gpu_mem = free_gpu_mem
attention_maps_images = [] attention_maps_images = []
attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image()) attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
make_image = self.get_make_image( make_image = self.get_make_image(

25
ldm/invoke/ckpt_to_diffuser.py
View File

@ -21,7 +21,7 @@ import os
import re import re
import torch import torch
from pathlib import Path from pathlib import Path
from ldm.invoke.globals import Globals from ldm.invoke.globals import Globals, global_cache_dir
from safetensors.torch import load_file from safetensors.torch import load_file
try: try:
@ -637,7 +637,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
def convert_ldm_clip_checkpoint(checkpoint): def convert_ldm_clip_checkpoint(checkpoint):
text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14",cache_dir=global_cache_dir('hub'))
keys = list(checkpoint.keys()) keys = list(checkpoint.keys())
@ -677,7 +677,8 @@ textenc_pattern = re.compile("|".join(protected.keys()))
def convert_paint_by_example_checkpoint(checkpoint): def convert_paint_by_example_checkpoint(checkpoint):
config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14") cache_dir = global_cache_dir('hub')
config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
model = PaintByExampleImageEncoder(config) model = PaintByExampleImageEncoder(config)
keys = list(checkpoint.keys()) keys = list(checkpoint.keys())
@ -744,7 +745,8 @@ def convert_paint_by_example_checkpoint(checkpoint):
def convert_open_clip_checkpoint(checkpoint): def convert_open_clip_checkpoint(checkpoint):
text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder") cache_dir=global_cache_dir('hub')
text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder", cache_dir=cache_dir)
keys = list(checkpoint.keys()) keys = list(checkpoint.keys())
@ -795,6 +797,7 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
): ):
checkpoint = load_file(checkpoint_path) if Path(checkpoint_path).suffix == '.safetensors' else torch.load(checkpoint_path) checkpoint = load_file(checkpoint_path) if Path(checkpoint_path).suffix == '.safetensors' else torch.load(checkpoint_path)
cache_dir = global_cache_dir('hub')
# Sometimes models don't have the global_step item # Sometimes models don't have the global_step item
if "global_step" in checkpoint: if "global_step" in checkpoint:
@ -904,7 +907,7 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
if model_type == "FrozenOpenCLIPEmbedder": if model_type == "FrozenOpenCLIPEmbedder":
text_model = convert_open_clip_checkpoint(checkpoint) text_model = convert_open_clip_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer") tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer",cache_dir=global_cache_dir('diffusers'))
pipe = StableDiffusionPipeline( pipe = StableDiffusionPipeline(
vae=vae, vae=vae,
text_encoder=text_model, text_encoder=text_model,
@ -917,8 +920,8 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
) )
elif model_type == "PaintByExample": elif model_type == "PaintByExample":
vision_model = convert_paint_by_example_checkpoint(checkpoint) vision_model = convert_paint_by_example_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker") feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
pipe = PaintByExamplePipeline( pipe = PaintByExamplePipeline(
vae=vae, vae=vae,
image_encoder=vision_model, image_encoder=vision_model,
@ -929,9 +932,9 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
) )
elif model_type in ['FrozenCLIPEmbedder','WeightedFrozenCLIPEmbedder']: elif model_type in ['FrozenCLIPEmbedder','WeightedFrozenCLIPEmbedder']:
text_model = convert_ldm_clip_checkpoint(checkpoint) text_model = convert_ldm_clip_checkpoint(checkpoint)
tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir=cache_dir)
safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker") safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker") feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker",cache_dir=cache_dir)
pipe = StableDiffusionPipeline( pipe = StableDiffusionPipeline(
vae=vae, vae=vae,
text_encoder=text_model, text_encoder=text_model,
@ -944,7 +947,7 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
else: else:
text_config = create_ldm_bert_config(original_config) text_config = create_ldm_bert_config(original_config)
text_model = convert_ldm_bert_checkpoint(checkpoint, text_config) text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased",cache_dir=cache_dir)
pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler) pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
pipe.save_pretrained( pipe.save_pretrained(

12
ldm/invoke/concepts_lib.py
View File

@ -59,7 +59,7 @@ class HuggingFaceConceptsLibrary(object):
be downloaded. be downloaded.
''' '''
if not concept_name in self.list_concepts(): if not concept_name in self.list_concepts():
print(f'This concept is not known to the Hugging Face library. Generation will continue without the concept.') print(f'This concept is not a local embedding trigger, nor is it a HuggingFace concept. Generation will continue without the concept.')
return None return None
return self.get_concept_file(concept_name.lower(),'learned_embeds.bin') return self.get_concept_file(concept_name.lower(),'learned_embeds.bin')
@ -115,13 +115,19 @@ class HuggingFaceConceptsLibrary(object):
return self.trigger_to_concept(match.group(1)) or f'<{match.group(1)}>' return self.trigger_to_concept(match.group(1)) or f'<{match.group(1)}>'
return self.match_trigger.sub(do_replace, prompt) return self.match_trigger.sub(do_replace, prompt)
def replace_concepts_with_triggers(self, prompt:str, load_concepts_callback: Callable[[list], any])->str: def replace_concepts_with_triggers(self,
prompt:str,
load_concepts_callback: Callable[[list], any],
excluded_tokens:list[str])->str:
''' '''
Given a prompt string that contains `<concept_name>` tags, replace Given a prompt string that contains `<concept_name>` tags, replace
these tags with the appropriate trigger. these tags with the appropriate trigger.
If any `<concept_name>` tags are found, `load_concepts_callback()` is called with a list If any `<concept_name>` tags are found, `load_concepts_callback()` is called with a list
of `concepts_name` strings. of `concepts_name` strings.
`excluded_tokens` are any tokens that should not be replaced, typically because they
are trigger tokens from a locally-loaded embedding.
''' '''
concepts = self.match_concept.findall(prompt) concepts = self.match_concept.findall(prompt)
if not concepts: if not concepts:
@ -129,6 +135,8 @@ class HuggingFaceConceptsLibrary(object):
load_concepts_callback(concepts) load_concepts_callback(concepts)
def do_replace(match)->str: def do_replace(match)->str:
if excluded_tokens and f'<{match.group(1)}>' in excluded_tokens:
return f'<{match.group(1)}>'
return self.concept_to_trigger(match.group(1)) or f'<{match.group(1)}>' return self.concept_to_trigger(match.group(1)) or f'<{match.group(1)}>'
return self.match_concept.sub(do_replace, prompt) return self.match_concept.sub(do_replace, prompt)

2
ldm/invoke/generator/base.py
View File

@ -62,9 +62,11 @@ class Generator:
def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None, def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
image_callback=None, step_callback=None, threshold=0.0, perlin=0.0, image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
safety_checker:dict=None, safety_checker:dict=None,
free_gpu_mem: bool=False,
**kwargs): **kwargs):
scope = nullcontext scope = nullcontext
self.safety_checker = safety_checker self.safety_checker = safety_checker
self.free_gpu_mem = free_gpu_mem
attention_maps_images = [] attention_maps_images = []
attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image()) attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
make_image = self.get_make_image( make_image = self.get_make_image(

4
ldm/invoke/globals.py
View File

@ -29,6 +29,7 @@ else:
# Where to look for the initialization file # Where to look for the initialization file
Globals.initfile = 'invokeai.init' Globals.initfile = 'invokeai.init'
Globals.models_file = 'models.yaml'
Globals.models_dir = 'models' Globals.models_dir = 'models'
Globals.config_dir = 'configs' Globals.config_dir = 'configs'
Globals.autoscan_dir = 'weights' Globals.autoscan_dir = 'weights'
@ -49,6 +50,9 @@ Globals.disable_xformers = False
# whether we are forcing full precision # whether we are forcing full precision
Globals.full_precision = False Globals.full_precision = False
def global_config_file()->Path:
return Path(Globals.root, Globals.config_dir, Globals.models_file)
def global_config_dir()->Path: def global_config_dir()->Path:
return Path(Globals.root, Globals.config_dir) return Path(Globals.root, Globals.config_dir)

62
ldm/invoke/merge_diffusers.py Normal file
View File

@ -0,0 +1,62 @@
'''
ldm.invoke.merge_diffusers exports a single function call merge_diffusion_models()
used to merge 2-3 models together and create a new InvokeAI-registered diffusion model.
'''
import os
from typing import List
from diffusers import DiffusionPipeline
from ldm.invoke.globals import global_config_file, global_models_dir, global_cache_dir
from ldm.invoke.model_manager import ModelManager
from omegaconf import OmegaConf
def merge_diffusion_models(models:List['str'],
merged_model_name:str,
alpha:float=0.5,
interp:str=None,
force:bool=False,
**kwargs):
'''
models - up to three models, designated by their InvokeAI models.yaml model name
merged_model_name = name for new model
alpha - The interpolation parameter. Ranges from 0 to 1. It affects the ratio in which the checkpoints are merged. A 0.8 alpha
would mean that the first model checkpoints would affect the final result far less than an alpha of 0.2
interp - The interpolation method to use for the merging. Supports "sigmoid", "inv_sigmoid", "add_difference" and None.
Passing None uses the default interpolation which is weighted sum interpolation. For merging three checkpoints, only "add_difference" is supported.
force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
**kwargs - the default DiffusionPipeline.get_config_dict kwargs:
cache_dir, resume_download, force_download, proxies, local_files_only, use_auth_token, revision, torch_dtype, device_map
'''
config_file = global_config_file()
model_manager = ModelManager(OmegaConf.load(config_file))
for mod in models:
assert (mod in model_manager.model_names()), f'** Unknown model "{mod}"'
assert (model_manager.model_info(mod).get('format',None) == 'diffusers'), f'** {mod} is not a diffusers model. It must be optimized before merging.'
model_ids_or_paths = [model_manager.model_name_or_path(x) for x in models]
pipe = DiffusionPipeline.from_pretrained(model_ids_or_paths[0],
cache_dir=kwargs.get('cache_dir',global_cache_dir()),
custom_pipeline='checkpoint_merger')
merged_pipe = pipe.merge(pretrained_model_name_or_path_list=model_ids_or_paths,
alpha=alpha,
interp=interp,
force=force,
**kwargs)
dump_path = global_models_dir() / 'merged_diffusers'
os.makedirs(dump_path,exist_ok=True)
dump_path = dump_path / merged_model_name
merged_pipe.save_pretrained (
dump_path,
safe_serialization=1
)
model_manager.import_diffuser_model(
dump_path,
model_name = merged_model_name,
description = f'Merge of models {", ".join(models)}'
)
print('REMINDER: When PR 2369 is merged, replace merge_diffusers.py line 56 with vae= argument to impormodel()')
if vae := model_manager.config[models[0]].get('vae',None):
print(f'>> Using configured VAE assigned to {models[0]}')
model_manager.config[merged_model_name]['vae'] = vae
model_manager.commit(config_file)

18
ldm/invoke/model_manager.py
View File

@ -37,7 +37,11 @@ from ldm.util import instantiate_from_config, ask_user
DEFAULT_MAX_MODELS=2 DEFAULT_MAX_MODELS=2
class ModelManager(object): class ModelManager(object):
def __init__(self, config:OmegaConf, device_type:str, precision:str, max_loaded_models=DEFAULT_MAX_MODELS): def __init__(self,
config:OmegaConf,
device_type:str='cpu',
precision:str='float16',
max_loaded_models=DEFAULT_MAX_MODELS):
''' '''
Initialize with the path to the models.yaml config file, Initialize with the path to the models.yaml config file,
the torch device type, and precision. The optional the torch device type, and precision. The optional
@ -143,7 +147,7 @@ class ModelManager(object):
Return true if this is a legacy (.ckpt) model Return true if this is a legacy (.ckpt) model
''' '''
info = self.model_info(model_name) info = self.model_info(model_name)
if 'weights' in info and info['weights'].endswith('.ckpt'): if 'weights' in info and info['weights'].endswith(('.ckpt','.safetensors')):
return True return True
return False return False
@ -362,8 +366,14 @@ class ModelManager(object):
vae = os.path.normpath(os.path.join(Globals.root,vae)) vae = os.path.normpath(os.path.join(Globals.root,vae))
if os.path.exists(vae): if os.path.exists(vae):
print(f' | Loading VAE weights from: {vae}') print(f' | Loading VAE weights from: {vae}')
vae_ckpt = None
vae_dict = None
if vae.endswith('.safetensors'):
vae_ckpt = safetensors.torch.load_file(vae)
vae_dict = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss"}
else:
vae_ckpt = torch.load(vae, map_location="cpu") vae_ckpt = torch.load(vae, map_location="cpu")
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} vae_dict = {k: v for k, v in vae_ckpt['state_dict'].items() if k[0:4] != "loss"}
model.first_stage_model.load_state_dict(vae_dict, strict=False) model.first_stage_model.load_state_dict(vae_dict, strict=False)
else: else:
print(f' | VAE file {vae} not found. Skipping.') print(f' | VAE file {vae} not found. Skipping.')
@ -536,7 +546,7 @@ class ModelManager(object):
format='diffusers', format='diffusers',
) )
if isinstance(repo_or_path,Path) and repo_or_path.exists(): if isinstance(repo_or_path,Path) and repo_or_path.exists():
new_config.update(path=repo_or_path) new_config.update(path=str(repo_or_path))
else: else:
new_config.update(repo_id=repo_or_path) new_config.update(repo_id=repo_or_path)

27
ldm/modules/encoders/modules.py
View File

@ -1,18 +1,16 @@
import math import math
import os.path from functools import partial
from typing import Optional from typing import Optional
import clip
import kornia
import torch import torch
import torch.nn as nn import torch.nn as nn
from functools import partial from einops import repeat
import clip
from einops import rearrange, repeat
from transformers import CLIPTokenizer, CLIPTextModel from transformers import CLIPTokenizer, CLIPTextModel
import kornia
from ldm.invoke.devices import choose_torch_device
from ldm.invoke.globals import Globals, global_cache_dir
#from ldm.modules.textual_inversion_manager import TextualInversionManager
from ldm.invoke.devices import choose_torch_device
from ldm.invoke.globals import global_cache_dir
from ldm.modules.x_transformer import ( from ldm.modules.x_transformer import (
Encoder, Encoder,
TransformerWrapper, TransformerWrapper,
@ -654,21 +652,22 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
per_token_weights += [weight] * len(this_fragment_token_ids) per_token_weights += [weight] * len(this_fragment_token_ids)
# leave room for bos/eos # leave room for bos/eos
if len(all_token_ids) > self.max_length - 2: max_token_count_without_bos_eos_markers = self.max_length - 2
excess_token_count = len(all_token_ids) - self.max_length - 2 if len(all_token_ids) > max_token_count_without_bos_eos_markers:
excess_token_count = len(all_token_ids) - max_token_count_without_bos_eos_markers
# TODO build nice description string of how the truncation was applied # TODO build nice description string of how the truncation was applied
# this should be done by calling self.tokenizer.convert_ids_to_tokens() then passing the result to # this should be done by calling self.tokenizer.convert_ids_to_tokens() then passing the result to
# self.tokenizer.convert_tokens_to_string() for the token_ids on each side of the truncation limit. # self.tokenizer.convert_tokens_to_string() for the token_ids on each side of the truncation limit.
print(f">> Prompt is {excess_token_count} token(s) too long and has been truncated") print(f">> Prompt is {excess_token_count} token(s) too long and has been truncated")
all_token_ids = all_token_ids[0:self.max_length] all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers]
per_token_weights = per_token_weights[0:self.max_length] per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers]
# pad out to a 77-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token] # pad out to a 77-entry array: [bos_token, <prompt tokens>, eos_token, pad_token…]
# (77 = self.max_length) # (77 = self.max_length)
all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id] all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
per_token_weights = [1.0] + per_token_weights + [1.0] per_token_weights = [1.0] + per_token_weights + [1.0]
pad_length = self.max_length - len(all_token_ids) pad_length = self.max_length - len(all_token_ids)
all_token_ids += [self.tokenizer.eos_token_id] * pad_length all_token_ids += [self.tokenizer.pad_token_id] * pad_length
per_token_weights += [1.0] * pad_length per_token_weights += [1.0] * pad_length
all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long).to(self.device) all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long).to(self.device)

11
ldm/modules/prompt_to_embeddings_converter.py
View File

@ -3,8 +3,9 @@ import math
import torch import torch
from transformers import CLIPTokenizer, CLIPTextModel from transformers import CLIPTokenizer, CLIPTextModel
from ldm.modules.textual_inversion_manager import TextualInversionManager
from ldm.invoke.devices import torch_dtype from ldm.invoke.devices import torch_dtype
from ldm.modules.textual_inversion_manager import TextualInversionManager
class WeightedPromptFragmentsToEmbeddingsConverter(): class WeightedPromptFragmentsToEmbeddingsConverter():
@ -22,8 +23,8 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
return self.tokenizer.model_max_length return self.tokenizer.model_max_length
def get_embeddings_for_weighted_prompt_fragments(self, def get_embeddings_for_weighted_prompt_fragments(self,
text: list[str], text: list[list[str]],
fragment_weights: list[float], fragment_weights: list[list[float]],
should_return_tokens: bool = False, should_return_tokens: bool = False,
device='cpu' device='cpu'
) -> torch.Tensor: ) -> torch.Tensor:
@ -198,12 +199,12 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers] all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers]
per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers] per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers]
# pad out to a self.max_length-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token] # pad out to a self.max_length-entry array: [bos_token, <prompt tokens>, eos_token, pad_token…]
# (typically self.max_length == 77) # (typically self.max_length == 77)
all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id] all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
per_token_weights = [1.0] + per_token_weights + [1.0] per_token_weights = [1.0] + per_token_weights + [1.0]
pad_length = self.max_length - len(all_token_ids) pad_length = self.max_length - len(all_token_ids)
all_token_ids += [self.tokenizer.eos_token_id] * pad_length all_token_ids += [self.tokenizer.pad_token_id] * pad_length
per_token_weights += [1.0] * pad_length per_token_weights += [1.0] * pad_length
all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long, device=device) all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long, device=device)

6
ldm/modules/textual_inversion_manager.py
View File

@ -38,11 +38,15 @@ class TextualInversionManager():
if concept_name in self.hf_concepts_library.concepts_loaded: if concept_name in self.hf_concepts_library.concepts_loaded:
continue continue
trigger = self.hf_concepts_library.concept_to_trigger(concept_name) trigger = self.hf_concepts_library.concept_to_trigger(concept_name)
if self.has_textual_inversion_for_trigger_string(trigger): if self.has_textual_inversion_for_trigger_string(trigger) \
or self.has_textual_inversion_for_trigger_string(concept_name) \
or self.has_textual_inversion_for_trigger_string(f'<{concept_name}>'): # in case a token with literal angle brackets encountered
print(f'>> Loaded local embedding for trigger {concept_name}')
continue continue
bin_file = self.hf_concepts_library.get_concept_model_path(concept_name) bin_file = self.hf_concepts_library.get_concept_model_path(concept_name)
if not bin_file: if not bin_file:
continue continue
print(f'>> Loaded remote embedding for trigger {concept_name}')
self.load_textual_inversion(bin_file) self.load_textual_inversion(bin_file)
self.hf_concepts_library.concepts_loaded[concept_name]=True self.hf_concepts_library.concepts_loaded[concept_name]=True

0
scripts/load_models.py Normal file → Executable file
View File

92
scripts/merge_models.py Executable file
View File

@ -0,0 +1,92 @@
#!/usr/bin/env python
import argparse
import os
import sys
import traceback
from pathlib import Path
from omegaconf import OmegaConf
from ldm.invoke.globals import (Globals, global_cache_dir, global_config_file,
global_set_root)
from ldm.invoke.model_manager import ModelManager
parser = argparse.ArgumentParser(description="InvokeAI textual inversion training")
parser.add_argument(
"--root_dir",
"--root-dir",
type=Path,
default=Globals.root,
help="Path to the invokeai runtime directory",
)
parser.add_argument(
"--models",
required=True,
type=str,
nargs="+",
help="Two to three model names to be merged",
)
parser.add_argument(
"--merged_model_name",
"--destination",
dest="merged_model_name",
type=str,
help="Name of the output model. If not specified, will be the concatenation of the input model names.",
)
parser.add_argument(
"--alpha",
type=float,
default=0.5,
help="The interpolation parameter, ranging from 0 to 1. It affects the ratio in which the checkpoints are merged. Higher values give more weight to the 2d and 3d models",
)
parser.add_argument(
"--interpolation",
dest="interp",
type=str,
choices=["weighted_sum", "sigmoid", "inv_sigmoid", "add_difference"],
default="weighted_sum",
help='Interpolation method to use. If three models are present, only "add_difference" will work.',
)
parser.add_argument(
"--force",
action="store_true",
help="Try to merge models even if they are incompatible with each other",
)
parser.add_argument(
"--clobber",
"--overwrite",
dest='clobber',
action="store_true",
help="Overwrite the merged model if --merged_model_name already exists",
)
args = parser.parse_args()
global_set_root(args.root_dir)
assert args.alpha >= 0 and args.alpha <= 1.0, "alpha must be between 0 and 1"
assert len(args.models) >= 1 and len(args.models) <= 3, "provide 2 or 3 models to merge"
if not args.merged_model_name:
args.merged_model_name = "+".join(args.models)
print(
f'>> No --merged_model_name provided. Defaulting to "{args.merged_model_name}"'
)
model_manager = ModelManager(OmegaConf.load(global_config_file()))
assert (args.clobber or args.merged_model_name not in model_manager.model_names()), f'A model named "{args.merged_model_name}" already exists. Use --clobber to overwrite.'
# It seems that the merge pipeline is not honoring cache_dir, so we set the
# HF_HOME environment variable here *before* we load diffusers.
cache_dir = str(global_cache_dir("diffusers"))
os.environ["HF_HOME"] = cache_dir
from ldm.invoke.merge_diffusers import merge_diffusion_models
try:
merge_diffusion_models(**vars(args))
print(f'>> Models merged into new model: "{args.merged_model_name}".')
except Exception as e:
print(f"** An error occurred while merging the pipelines: {str(e)}")
print("** DETAILS:")
print(traceback.format_exc())
sys.exit(-1)

87
scripts/merge_fe.py → scripts/merge_models_fe.py Normal file → Executable file
View File

@ -3,11 +3,10 @@
import npyscreen import npyscreen
import os import os
import sys import sys
import re
import shutil
import traceback import traceback
import argparse import argparse
from ldm.invoke.globals import Globals, global_set_root from ldm.invoke.globals import Globals, global_set_root, global_cache_dir, global_config_file
from ldm.invoke.model_manager import ModelManager
from omegaconf import OmegaConf from omegaconf import OmegaConf
from pathlib import Path from pathlib import Path
from typing import List from typing import List
@ -30,6 +29,14 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
'inv_sigmoid', 'inv_sigmoid',
'add_difference'] 'add_difference']
def __init__(self, parentApp, name):
self.parentApp = parentApp
super().__init__(parentApp, name)
@property
def model_manager(self):
return self.parentApp.model_manager
def afterEditing(self): def afterEditing(self):
self.parentApp.setNextForm(None) self.parentApp.setNextForm(None)
@ -83,6 +90,11 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
lowest=0, lowest=0,
value=0.5, value=0.5,
) )
self.force = self.add_widget_intelligent(
npyscreen.Checkbox,
name='Force merge of incompatible models',
value=False,
)
self.merged_model_name = self.add_widget_intelligent( self.merged_model_name = self.add_widget_intelligent(
npyscreen.TitleText, npyscreen.TitleText,
name='Name for merged model', name='Name for merged model',
@ -105,20 +117,51 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
self.merge_method.value=0 self.merge_method.value=0
def on_ok(self): def on_ok(self):
if self.validate_field_values(): if self.validate_field_values() and self.check_for_overwrite():
self.parentApp.setNextForm(None) self.parentApp.setNextForm(None)
self.editing = False self.editing = False
self.parentApp.merge_arguments = self.marshall_arguments()
npyscreen.notify('Starting the merge...')
import ldm.invoke.merge_diffusers # this keeps the message up while diffusers loads
else: else:
self.editing = True self.editing = True
def ok_cancel(self): def on_cancel(self):
sys.exit(0) sys.exit(0)
def marshall_arguments(self)->dict:
model_names = self.model_names
models = [
model_names[self.model1.value[0]],
model_names[self.model2.value[0]],
]
if self.model3.value[0] > 0:
models.append(model_names[self.model3.value[0]-1])
args = dict(
models=models,
alpha = self.alpha.value,
interp = self.interpolations[self.merge_method.value[0]],
force = self.force.value,
merged_model_name = self.merged_model_name.value,
)
return args
def check_for_overwrite(self)->bool:
model_out = self.merged_model_name.value
if model_out not in self.model_names:
return True
else:
return npyscreen.notify_yes_no(f'The chosen merged model destination, {model_out}, is already in use. Overwrite?')
def validate_field_values(self)->bool: def validate_field_values(self)->bool:
bad_fields = [] bad_fields = []
selected_models = set((self.model1.value[0],self.model2.value[0],self.model3.value[0])) model_names = self.model_names
if len(selected_models) < 3: selected_models = set((model_names[self.model1.value[0]],model_names[self.model2.value[0]]))
bad_fields.append('Please select two or three DIFFERENT models to compare') if self.model3.value[0] > 0:
selected_models.add(model_names[self.model3.value[0]-1])
if len(selected_models) < 2:
bad_fields.append(f'Please select two or three DIFFERENT models to compare. You selected {selected_models}')
if len(bad_fields) > 0: if len(bad_fields) > 0:
message = 'The following problems were detected and must be corrected:' message = 'The following problems were detected and must be corrected:'
for problem in bad_fields: for problem in bad_fields:
@ -129,13 +172,15 @@ class mergeModelsForm(npyscreen.FormMultiPageAction):
return True return True
def get_model_names(self)->List[str]: def get_model_names(self)->List[str]:
conf = OmegaConf.load(os.path.join(Globals.root,'configs/models.yaml')) model_names = [name for name in self.model_manager.model_names() if self.model_manager.model_info(name).get('format') == 'diffusers']
model_names = [name for name in conf.keys() if conf[name].get('format',None)=='diffusers'] print(model_names)
return sorted(model_names) return sorted(model_names)
class MyApplication(npyscreen.NPSAppManaged): class Mergeapp(npyscreen.NPSAppManaged):
def __init__(self): def __init__(self):
super().__init__() super().__init__()
conf = OmegaConf.load(global_config_file())
self.model_manager = ModelManager(conf,'cpu','float16') # precision doesn't really matter here
def onStart(self): def onStart(self):
npyscreen.setTheme(npyscreen.Themes.DefaultTheme) npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
@ -152,5 +197,21 @@ if __name__ == '__main__':
args = parser.parse_args() args = parser.parse_args()
global_set_root(args.root_dir) global_set_root(args.root_dir)
myapplication = MyApplication() cache_dir = str(global_cache_dir('diffusers')) # because not clear the merge pipeline is honoring cache_dir
myapplication.run() os.environ['HF_HOME'] = cache_dir
mergeapp = Mergeapp()
mergeapp.run()
args = mergeapp.merge_arguments
args.update(cache_dir = cache_dir)
from ldm.invoke.merge_diffusers import merge_diffusion_models
try:
merge_diffusion_models(**args)
print(f'>> Models merged into new model: "{args["merged_model_name"]}".')
except Exception as e:
print(f'** An error occurred while merging the pipelines: {str(e)}')
print('** DETAILS:')
print(traceback.format_exc())
sys.exit(-1)

0
scripts/merge_embeddings.py → scripts/orig_scripts/merge_embeddings.py Normal file → Executable file
View File

5
setup.py
View File

@ -92,8 +92,9 @@ setup(
'Topic :: Scientific/Engineering :: Image Processing', 'Topic :: Scientific/Engineering :: Image Processing',
], ],
scripts = ['scripts/invoke.py','scripts/configure_invokeai.py', 'scripts/sd-metadata.py', scripts = ['scripts/invoke.py','scripts/configure_invokeai.py', 'scripts/sd-metadata.py',
'scripts/preload_models.py', 'scripts/images2prompt.py','scripts/merge_embeddings.py', 'scripts/preload_models.py', 'scripts/images2prompt.py',
'scripts/textual_inversion_fe.py','scripts/textual_inversion.py' 'scripts/textual_inversion_fe.py','scripts/textual_inversion.py',
'scripts/merge_models_fe.py', 'scripts/merge_models.py',
], ],
data_files=FRONTEND_FILES, data_files=FRONTEND_FILES,
) )