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, and CUI based.

• DreamBooth training guide
• Step by Step fine-tuning guide: Including BLIP captioning and tagging by DeepDanbooru or WD14 tagger
• training LoRA
• training Textual Inversion
• note.com Image generation
• note.com Model conversion

Windows Required Dependencies

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

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!!!

License

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

Memory Efficient Attention Pytorch: MIT

bitsandbytes: MIT

BLIP: BSD-3-Clause

Change History

• 23 Feb. 2023, 2023/2/23:

  • Fix instability training issue in train_network.py.
    • fp16 training is probably not affected by this issue.
    • Training with float for SD2.x models will work now. Also training with bf16 might be improved.
    • This issue seems to have occurred in PR#190.
  • Add some metadata to LoRA model. Thanks to space-nuko!
  • Raise an error if optimizer options conflict (e.g. --optimizer_type and --use_8bit_adam.)
  • Support ControlNet in gen_img_diffusers.py (no documentation yet.)
  • train_network.py で学習が不安定になる不具合を修正しました。
    • fp16 精度での学習には恐らくこの問題は影響しません。
    • float 精度での SD2.x モデルの学習が正しく動作するようになりました。また bf16 精度の学習も改善する可能性があります。
    • この問題は PR#190 から起きていたようです。
  • いくつかのメタデータを LoRA モデルに追加しました。 space-nuko 氏に感謝します。
  • オプティマイザ関係のオプションが矛盾していた場合、エラーとするように修正しました（例: --optimizer_type と --use_8bit_adam）。
  • gen_img_diffusers.py で ControlNet をサポートしました（ドキュメントはのちほど追加します）。

• 22 Feb. 2023, 2023/2/22:

  • Refactor optmizer options. Thanks to mgz-dev!
    • Add --optimizer_type option for each training script. Please see help. Japanese documentation is here.
    • --use_8bit_adam and --use_lion_optimizer options also work, but override above option.
  • Add SGDNesterov and its 8bit.
  • Add D-Adaptation optimizer. Thanks to BootsofLagrangian and all!
    • Please install D-Adaptation optimizer with pip install dadaptation (it is not in requirements.txt currently.)
    • Please see https://github.com/kohya-ss/sd-scripts/issues/181 for details.
  • Add AdaFactor optimizer. Thanks to Toshiaki!
  • Extra lr scheduler settings (num_cycles etc.) are working in training scripts other than train_network.py.
  • Add --max_grad_norm option for each training script for gradient clipping. 0.0 disables clipping.
  • Symbolic link can be loaded in each training script. Thanks to TkskKurumi!
  • オプティマイザ関連のオプションを見直しました。mgz-dev氏に感謝します。
    • --optimizer_type を各学習スクリプトに追加しました。ドキュメントはこちら。
    • --use_8bit_adam と --use_lion_optimizer のオプションは依然として動作しますがoptimizer_typeを上書きしますのでご注意ください。
  • SGDNesterov オプティマイザおよびその8bit版を追加しました。
  • D-Adaptation オプティマイザを追加しました。BootsofLagrangian 氏および諸氏に感謝します。
    • pip install dadaptation コマンドで別途インストールが必要です（現時点ではrequirements.txtに含まれておりません）。
    • こちらのissueもあわせてご覧ください。 https://github.com/kohya-ss/sd-scripts/issues/181
  • AdaFactor オプティマイザを追加しました。Toshiaki氏に感謝します。
  • 追加のスケジューラ設定（num_cycles等）が train_network.py 以外の学習スクリプトでも使えるようになりました。
  • 勾配クリップ時の最大normを指定する --max_grad_norm オプションを追加しました。0.0を指定するとクリップしなくなります。
  • 各学習スクリプトでシンボリックリンクが読み込めるようになりました。TkskKurumi氏に感謝します。

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