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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!!!

About requirements.txt

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.

Links to how-to-use documents

All documents are in Japanese currently.

Windows Required Dependencies

Python 3.10.6 and Git:

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

Windows Installation

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.)

about PyTorch and xformers

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

Upgrade

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.

Credits

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!

License

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

Change History

22 Apr. 2023, 2023/4/22:

  • Added support for logging to wandb. Please refer to PR #428. Thank you p1atdev!

    • wandb installation is required. Please install it with pip install wandb. Login to wandb with wandb login command, or set --wandb_api_key option for automatic login.
    • Please let me know if you find any bugs as the test is not complete.

  • You can automatically login to wandb by setting the --wandb_api_key option. Please be careful with the handling of API Key. PR #435 Thank you Linaqruf!

  • Improved the behavior of --debug_dataset on non-Windows environments. PR #429 Thank you tsukimiya!

  • Fixed --face_crop_aug option not working in Fine tuning method.

  • Prepared code to use any upscaler in gen_img_diffusers.py.

  • wandbへのロギングをサポートしました。詳細は PR #428をご覧ください。p1atdev氏に感謝します。

    • wandb のインストールが別途必要です。pip install wandb でインストールしてください。また wandb login でログインしてください（学習スクリプト内でログインする場合は --wandb_api_key オプションを設定してください）。
    • テスト未了のため不具合等ありましたらご連絡ください。

  • wandbへのロギング時に --wandb_api_key オプションを設定することで自動ログインできます。API Keyの扱いにご注意ください。 PR #435 Linaqruf氏に感謝します。

  • Windows以外の環境での--debug_dataset の動作を改善しました。PR #429 tsukimiya氏に感謝します。

  • --face_crop_augオプションがFine tuning方式で動作しなかったのを修正しました。

  • gen_img_diffusers.pyに任意のupscalerを利用するためのコード準備を行いました。

19 Apr. 2023, 2023/4/19:

  • Fixed lora_interrogator.py not working. Please refer to PR #392 for details. Thank you A2va and heyalexchoi!
  • Fixed the handling of tags containing _ in tag_images_by_wd14_tagger.py.
  • lora_interrogator.pyが動作しなくなっていたのを修正しました。詳細は PR #392 をご参照ください。A2va氏およびheyalexchoi氏に感謝します。
  • tag_images_by_wd14_tagger.pyで_を含むタグの取り扱いを修正しました。

Naming of LoRA

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.

LoRAの名称について

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。

17 Apr. 2023, 2023/4/17:

  • Added the --recursive option to each script in the finetune folder to process folders recursively. Please refer to PR #400 for details. Thanks to Linaqruf!
  • finetuneフォルダ内の各スクリプトに再起的にフォルダを処理するオプション--recursiveを追加しました。詳細は PR #400 を参照してください。Linaqruf 氏に感謝します。

14 Apr. 2023, 2023/4/14:

  • Fixed a bug that caused an error when loading DyLoRA with the --network_weight option in train_network.py.
  • train_network.pyで、DyLoRAを--network_weightオプションで読み込むとエラーになる不具合を修正しました。

13 Apr. 2023, 2023/4/13:

  • Added support for DyLoRA in train_network.py. Please refer to here for details (currently only in Japanese).

  • Added support for caching latents to disk in each training script. Please specify both --cache_latents and --cache_latents_to_disk options.

    • The files are saved in the same folder as the images with the extension .npz. If you specify the --flip_aug option, the files with _flip.npz will also be saved.
    • Multi-GPU training has not been tested.
    • This feature is not tested with all combinations of datasets and training scripts, so there may be bugs.

  • Added workaround for an error that occurs when training with fp16 or bf16 in fine_tune.py.

  • train_network.pyでDyLoRAをサポートしました。詳細はこちらをご覧ください。

  • 各学習スクリプトでlatentのディスクへのキャッシュをサポートしました。--cache_latentsオプションに 加えて、--cache_latents_to_diskオプションを指定してください。

    • 画像と同じフォルダに、拡張子 .npz で保存されます。--flip_augオプションを指定した場合、_flip.npzが付いたファイルにも保存されます。
    • マルチGPUでの学習は未テストです。
    • すべてのDataset、学習スクリプトの組み合わせでテストしたわけではないため、不具合があるかもしれません。

  • fine_tune.pyで、fp16およびbf16の学習時にエラーが出る不具合に対して対策を行いました。

Sample image generation during training

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.

サンプル画像生成

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。

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