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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. 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.

English translation by darkstorm2150 is here. Thanks to darkstorm2150!

• 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
• 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.

For Lion8bit, you need to upgrade bitsandbytes to 0.38.0 or later. Uninstall bitsandbytes, and for Windows, install the Windows version whl file from here or other sources, like:

pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl

For upgrading, upgrade this repo with pip install ., and upgrade necessary packages manually.

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

• Fixed an issue where an error would occur if the encoding of the prompt file was different from the default. PR #510 Thanks to sdbds!
  • Please save the prompt file in UTF-8.
• プロンプトファイルのエンコーディングがデフォルトと異なる場合にエラーが発生する問題を修正しました。 PR #510 sdbds氏に感謝します。
  • プロンプトファイルはUTF-8で保存してください。

• Added English translation of documents by darkstorm2150. Thank you very much!

• The prompt for sample generation during training can now be specified in .toml or .json. PR #504 Thanks to Linaqruf!

  • For details on prompt description, please see the PR.

• darkstorm2150氏にドキュメント類を英訳していただきました。ありがとうございます！

• 学習中のサンプル生成のプロンプトを.tomlまたは.jsonで指定可能になりました。 PR #504 Linaqruf氏に感謝します。

  • プロンプト記述の詳細は当該PRをご覧ください。

• Added an option --dim_from_weights to train_network.py to automatically determine the dim(rank) from the weight file. PR #491 Thanks to AI-Casanova!

  • It is useful in combination with resize_lora.py. Please see the PR for details.

• Fixed a bug where the noise resolution was incorrect with Multires noise. PR #489 Thanks to sdbds!

  • Please see the PR for details.

• The image generation scripts can now use img2img and highres fix at the same time.

• Fixed a bug where the hint image of ControlNet was incorrectly BGR instead of RGB in the image generation scripts.

• Added a feature to the image generation scripts to use the memory-efficient VAE.

  • If you specify a number with the --vae_slices option, the memory-efficient VAE will be used. The maximum output size will be larger, but it will be slower. Please specify a value of about 16 or 32.
  • The implementation of the VAE is in library/slicing_vae.py.

• train_network.pyにdim(rank)を重みファイルから自動決定するオプション--dim_from_weightsが追加されました。 PR #491 AI-Casanova氏に感謝します。

  • resize_lora.pyと組み合わせると有用です。詳細はPRもご参照ください。

• Multires noiseでノイズ解像度が正しくない不具合が修正されました。 PR #489 sdbds氏に感謝します。

  • 詳細は当該PRをご参照ください。

• 生成スクリプトでimg2imgとhighres fixを同時に使用できるようにしました。

• 生成スクリプトでControlNetのhint画像が誤ってBGRだったのをRGBに修正しました。

• 生成スクリプトで省メモリ化VAEを使えるよう機能追加しました。

  • --vae_slicesオプションに数値を指定すると、省メモリ化VAEを用います。出力可能な最大サイズが大きくなりますが、遅くなります。16または32程度の値を指定してください。
  • VAEの実装はlibrary/slicing_vae.pyにあります。

• The documentation has been moved to the docs folder. If you have links, please change them.

• Removed gradio from requirements.txt.

• DAdaptAdaGrad, DAdaptAdan, and DAdaptSGD are now supported by DAdaptation. PR#455 Thanks to sdbds!

  • DAdaptation needs to be installed. Also, depending on the optimizer, DAdaptation may need to be updated. Please update with pip install --upgrade dadaptation.

• Added support for pre-calculation of LoRA weights in image generation scripts. Specify --network_pre_calc.

  • The prompt option --am is available. Also, it is disabled when Regional LoRA is used.

• Added Adaptive noise scale to each training script. Specify a number with --adaptive_noise_scale to enable it.

  • Experimental option. It may be removed or changed in the future.
  • This is an original implementation that automatically adjusts the value of the noise offset according to the absolute value of the mean of each channel of the latents. It is expected that appropriate noise offsets will be set for bright and dark images, respectively.
  • Specify it together with --noise_offset.
  • The actual value of the noise offset is calculated as noise_offset + abs(mean(latents, dim=(2,3))) * adaptive_noise_scale. Since the latent is close to a normal distribution, it may be a good idea to specify a value of about 1/10 to the same as the noise offset.
  • Negative values can also be specified, in which case the noise offset will be clipped to 0 or more.

• Other minor fixes.

• ドキュメントをdocsフォルダに移動しました。リンク等を張られている場合は変更をお願いいたします。

• requirements.txtからgradioを削除しました。

• DAdaptationで新しくDAdaptAdaGrad、DAdaptAdan、DAdaptSGDがサポートされました。PR#455 sdbds氏に感謝します。

  • dadaptationのインストールが必要です。またオプティマイザによってはdadaptationの更新が必要です。pip install --upgrade dadaptationで更新してください。

• 画像生成スクリプトでLoRAの重みの事前計算をサポートしました。--network_pre_calcを指定してください。

  • プロンプトオプションの--amが利用できます。またRegional LoRA使用時には無効になります。

• 各学習スクリプトにAdaptive noise scaleを追加しました。--adaptive_noise_scaleで数値を指定すると有効になります。

  • 実験的オプションです。将来的に削除、仕様変更される可能性があります。
  • Noise offsetの値を、latentsの各チャネルの平均値の絶対値に応じて自動調整するオプションです。独自の実装で、明るい画像、暗い画像に対してそれぞれ適切なnoise offsetが設定されることが期待されます。
  • --noise_offset と同時に指定してください。
  • 実際のNoise offsetの値は noise_offset + abs(mean(latents, dim=(2,3))) * adaptive_noise_scale で計算されます。 latentは正規分布に近いためnoise_offsetの1/10～同程度の値を指定するとよいかもしれません。
  • 負の値も指定でき、その場合はnoise offsetは0以上にclipされます。

• その他の細かい修正を行いました。

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-C3Lier 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-C3Lier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。