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This repository contains training, generation and utility scripts for Stable Diffusion.

Change History is moved to the bottom of the page. 更新履歴はページ末尾に移しました。

日本語版README

For easier use (GUI and PowerShell scripts etc...), please visit the repository maintained by bmaltais. Thanks to @bmaltais!

This repository contains the scripts for:

• DreamBooth training, including U-Net and Text Encoder
• Fine-tuning (native training), including U-Net and Text Encoder
• LoRA training
• Texutl Inversion training
• Image generation
• Model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers)

Stable Diffusion web UI now seems to support LoRA trained by sd-scripts. (SD 1.x based only) Thank you for great work!!!

These files do not contain requirements for PyTorch. Because the versions of them depend on your environment. Please install PyTorch at first (see installation guide below.)

The scripts are tested with PyTorch 1.12.1 and 1.13.0, Diffusers 0.10.2.

Most of the documents are written in Japanese.

• Training guide - common : data preparation, options etc...
  • Chinese version
• Dataset config
• DreamBooth training guide
• Step by Step fine-tuning guide:
• training LoRA
• training Textual Inversion
• note.com Image generation
• note.com Model conversion

Python 3.10.6 and Git:

• Python 3.10.6: https://www.python.org/ftp/python/3.10.6/python-3.10.6-amd64.exe
• git: https://git-scm.com/download/win

Give unrestricted script access to powershell so venv can work:

• Open an administrator powershell window
• Type Set-ExecutionPolicy Unrestricted and answer A
• Close admin powershell window

Open a regular Powershell terminal and type the following inside:

git clone https://github.com/kohya-ss/sd-scripts.git
cd sd-scripts

python -m venv venv
.\venv\Scripts\activate

pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
pip install --upgrade -r requirements.txt
pip install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl

cp .\bitsandbytes_windows\*.dll .\venv\Lib\site-packages\bitsandbytes\
cp .\bitsandbytes_windows\cextension.py .\venv\Lib\site-packages\bitsandbytes\cextension.py
cp .\bitsandbytes_windows\main.py .\venv\Lib\site-packages\bitsandbytes\cuda_setup\main.py

accelerate config

update: python -m venv venv is seemed to be safer than python -m venv --system-site-packages venv (some user have packages in global python).

Answers to accelerate config:

- This machine
- No distributed training
- NO
- NO
- NO
- all
- fp16

note: Some user reports ValueError: fp16 mixed precision requires a GPU is occurred in training. In this case, answer 0 for the 6th question: What GPU(s) (by id) should be used for training on this machine as a comma-separated list? [all]:

(Single GPU with id 0 will be used.)

Other versions of PyTorch and xformers seem to have problems with training. If there is no other reason, please install the specified version.

When a new release comes out you can upgrade your repo with the following command:

cd sd-scripts
git pull
.\venv\Scripts\activate
pip install --use-pep517 --upgrade -r requirements.txt

Once the commands have completed successfully you should be ready to use the new version.

The implementation for LoRA is based on cloneofsimo's repo. Thank you for great work!

The LoRA expansion to Conv2d 3x3 was initially released by cloneofsimo and its effectiveness was demonstrated at LoCon by KohakuBlueleaf. Thank you so much KohakuBlueleaf!

The majority of scripts is licensed under ASL 2.0 (including codes from Diffusers, cloneofsimo's and LoCon), however portions of the project are available under separate license terms:

Memory Efficient Attention Pytorch: MIT

bitsandbytes: MIT

BLIP: BSD-3-Clause

• Added Chinese translation of DreamBooth guide and LoRA guide. PR #459 Thanks to tomj2ee!
• Added documentation for image generation script gen_img_diffusers.py (Japanese version only).
• **語版のDreamBoothガイドとLoRAガイドが追加されました。 PR #459 tomj2ee氏に感謝します。
• 画像生成スクリプト gen_img_diffusers.pyの簡単なドキュメントを追加しました（日本語版のみ）。

• Added Chinese translation of training guide. PR #445 Thanks to tomj2ee!
• tag_images_by_wd14_tagger.py can now get arguments from outside. PR #453 Thanks to mio2333!
• 学習に関するドキュメントの**語版が追加されました。 PR #445 tomj2ee氏に感謝します。
• tag_images_by_wd14_tagger.pyの引数を外部から取得できるようになりました。 PR #453 mio2333氏に感謝します。

• Please do not update for a while if you cannot revert the repository to the previous version when something goes wrong, because the model saving part has been changed.

• Added --save_every_n_steps option to each training script. The model is saved every specified steps.

  • --save_last_n_steps option can be used to save only the specified number of models (old models will be deleted).
  • If you specify the --save_state option, the state will also be saved at the same time. You can specify the number of steps to keep the state with the --save_last_n_steps_state option (the same value as --save_last_n_steps is used if omitted).
  • You can use the epoch-based model saving and state saving options together.
  • Not tested in multi-GPU environment. Please report any bugs.

• --cache_latents_to_disk option automatically enables --cache_latents option when specified. #438

• Fixed a bug in gen_img_diffusers.py where latents upscaler would fail with a batch size of 2 or more.

• モデル保存部分を変更していますので、何か不具合が起きた時にリポジトリを前のバージョンに戻せない場合には、しばらく更新を控えてください。

• 各学習スクリプトに--save_every_n_stepsオプションを追加しました。指定ステップごとにモデルを保存します。

  • --save_last_n_stepsオプションに数値を指定すると、そのステップ数のモデルのみを保存します（古いモデルは削除されます）。
  • --save_stateオプションを指定するとstateも同時に保存します。--save_last_n_steps_stateオプションでstateを残すステップ数を指定できます（省略時は--save_last_n_stepsと同じ値が使われます）。
  • エポックごとのモデル保存、state保存のオプションと共存できます。
  • マルチGPU環境でのテストを行っていないため、不具合等あればご報告ください。

• --cache_latents_to_diskオプションが指定されたとき、--cache_latentsオプションが自動的に有効になるようにしました。 #438

• gen_img_diffusers.pyでlatents upscalerがバッチサイズ2以上でエラーとなる不具合を修正しました。

Please read Releases for recent updates. 最近の更新情報は Release をご覧ください。

The LoRA supported by train_network.py has been named to avoid confusion. The documentation has been updated. The following are the names of LoRA types in this repository.

1. LoRA-LierLa : (LoRA for Li n e a r La yers)

   LoRA for Linear layers and Conv2d layers with 1x1 kernel

2. LoRA-C3Lier : (LoRA for C olutional layers with 3 x3 Kernel and Li n e a r layers)

   In addition to 1., LoRA for Conv2d layers with 3x3 kernel

LoRA-LierLa is the default LoRA type for train_network.py (without conv_dim network arg). LoRA-LierLa can be used with our extension for AUTOMATIC1111's Web UI, or with the built-in LoRA feature of the Web UI.

To use LoRA-C3Liar with Web UI, please use our extension.

train_network.py がサポートするLoRAについて、混乱を避けるため名前を付けました。ドキュメントは更新済みです。以下は当リポジトリ内の独自の名称です。

1. LoRA-LierLa : (LoRA for Li n e a r La yers、リエラと読みます)

   Linear 層およびカーネルサイズ 1x1 の Conv2d 層に適用されるLoRA

2. LoRA-C3Lier : (LoRA for C olutional layers with 3 x3 Kernel and Li n e a r layers、セリアと読みます)

   1.に加え、カーネルサイズ 3x3 の Conv2d 層に適用されるLoRA

LoRA-LierLa はWeb UI向け拡張、またはAUTOMATIC1111氏のWeb UIのLoRA機能で使用することができます。

LoRA-C3Liarを使いWeb UIで生成するには拡張を使用してください。

A prompt file might look like this, for example

# prompt 1
masterpiece, best quality, (1girl), in white shirts, upper body, looking at viewer, simple background --n low quality, worst quality, bad anatomy,bad composition, poor, low effort --w 768 --h 768 --d 1 --l 7.5 --s 28

# prompt 2
masterpiece, best quality, 1boy, in business suit, standing at street, looking back --n (low quality, worst quality), bad anatomy,bad composition, poor, low effort --w 576 --h 832 --d 2 --l 5.5 --s 40

Lines beginning with # are comments. You can specify options for the generated image with options like --n after the prompt. The following can be used.

• --n Negative prompt up to the next option.
• --w Specifies the width of the generated image.
• --h Specifies the height of the generated image.
• --d Specifies the seed of the generated image.
• --l Specifies the CFG scale of the generated image.
• --s Specifies the number of steps in the generation.

The prompt weighting such as ( ) and [ ] are working.

プロンプトファイルは例えば以下のようになります。

# prompt 1
masterpiece, best quality, (1girl), in white shirts, upper body, looking at viewer, simple background --n low quality, worst quality, bad anatomy,bad composition, poor, low effort --w 768 --h 768 --d 1 --l 7.5 --s 28

# prompt 2
masterpiece, best quality, 1boy, in business suit, standing at street, looking back --n (low quality, worst quality), bad anatomy,bad composition, poor, low effort --w 576 --h 832 --d 2 --l 5.5 --s 40

# で始まる行はコメントになります。--n のように「ハイフン二個＋英小文字」の形でオプションを指定できます。以下が使用可能できます。

• --n Negative prompt up to the next option.
• --w Specifies the width of the generated image.
• --h Specifies the height of the generated image.
• --d Specifies the seed of the generated image.
• --l Specifies the CFG scale of the generated image.
• --s Specifies the number of steps in the generation.

( ) や [ ] などの重みづけも動作します。