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

Stable Diffusion web UI now seems to support LoRA trained by sd-scripts. Thank you for great work!!!

Note: Currently the LoRA models trained by release v0.4.0 does not seem to be supported. If you use Web UI native LoRA support, please use release 0.3.2 for now. The LoRA models for SD 2.x is not supported too in Web UI.

• Release v0.4.0: 22 Jan. 2023
  • Add --network_alpha option to specify alpha value to prevent underflows for stable training. Thanks to CCRcmcpe!
    • Details of the issue are described in kohya-ss/sd-webui-additional-networks#49 .
    • The default value is 1, scale 1 / rank (or dimension). Set same value as network_dim for same behavior to old version.
    • LoRA with a large dimension (rank) seems to require a higher learning rate with alpha=1 (e.g. 1e-3 for 128-dim, still investigating).　
  • For generating images in Web UI, the latest version of the extension sd-webui-additional-networks (v0.3.0 or later) is required for the models trained with this release or later.
  • Add logging for the learning rate for U-Net and Text Encoder independently, and for running average epoch loss. Thanks to mgz-dev!
  • Add more metadata such as dataset/reg image dirs, session ID, output name etc... See kohya-ss#77 for details. Thanks to space-nuko!
    • Now the metadata includes the folder name (the basename of the folder contains image files, not fullpath). If you do not want it, disable metadata storing with --no_metadata option.
  • Add --training_comment option. You can specify an arbitrary string and refer to it by the extension.

Stable Diffusion web UI本体で当リポジトリで学習したLoRAモデルによる画像生成がサポートされたようです。

注：現時点ではversion 0.4.0で学習したモデルはサポートされないようです。Web UI本体の生成機能を使う場合には、version 0.3.2を引き続きご利用ください。またSD2.x用のLoRAモデルもサポートされないようです。

• Release 0.4.0: 2023/1/22
  • アンダーフローを防ぎ安定して学習するための alpha 値を指定する、--network_alpha オプションを追加しました。CCRcmcpe 氏に感謝します。
    • 問題の詳細はこちらをご覧ください： kohya-ss/sd-webui-additional-networks#49
    • デフォルト値は 1 で、LoRAの計算結果を 1 / rank (dimension・次元数) 倍します（つまり小さくなります。これにより同じ効果を出すために必要なLoRAの重みの変化が大きくなるため、アンダーフローが避けられるようになります）。network_dim と同じ値を指定すると旧バージョンと同じ動作になります。
    • alpha=1の場合、次元数（rank）の多いLoRAモジュールでは学習率を高めにしたほうが良いようです（128次元で1e-3など）。
    • このバージョンのスクリプトで学習したモデルをWeb UIで使うためには sd-webui-additional-networks 拡張の最新版（v0.3.0以降）が必要となります。
  • U-Net と Text Encoder のそれぞれの学習率、エポックの平均lossをログに記録するようになりました。mgz-dev 氏に感謝します。
  • 画像ディレクトリ、セッションID、出力名などいくつかの項目がメタデータに追加されました（詳細は kohya-ss#77 を参照）。space-nuko氏に感謝します。
    • メタデータにフォルダ名が含まれるようになりました（画像を含むフォルダの名前のみで、フルパスではありません）。 もし望まない場合には --no_metadata オプションでメタデータの記録を止めてください。
  • --training_comment オプションを追加しました。任意の文字列を指定でき、Web UI拡張から参照できます。

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

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
• image generation
• model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers)

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.

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
• 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.)

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 --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 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