SDXL is now supported. The sdxl branch has been merged into the main branch. If you update the repository, please follow the upgrade instructions. Also, the version of accelerate has been updated, so please run accelerate config again. The documentation for SDXL training is here.

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はこちら

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
• Textual Inversion training
• Image generation
• Model conversion (supports 1.x and 2.x, Stable Diffision ckpt/safetensors and Diffusers)

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 2.0.1. 1.12.1 is not tested but should work.

Links to usage documentation

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

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==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118
pip install --upgrade -r requirements.txt
pip install xformers==0.0.20

accelerate config

Note: Now bitsandbytes is optional. Please install any version of bitsandbytes as needed. Installation instructions are in the following section.

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.)

Optional: Use bitsandbytes (8bit optimizer)

For 8bit optimizer, you need to install bitsandbytes. For Linux, please install bitsandbytes as usual (0.41.1 or later is recommended.)

For Windows, there are several versions of bitsandbytes:

• bitsandbytes 0.35.0: Stable version. AdamW8bit is available. full_bf16 is not available.
• bitsandbytes 0.41.1: Lion8bit, PagedAdamW8bit and PagedLion8bit are available. full_bf16 is available.

Note: bitsandbytesabove 0.35.0 till 0.41.0 seems to have an issue: bitsandbytes-foundation/bitsandbytes#659

Follow the instructions below to install bitsandbytes for Windows.

bitsandbytes 0.35.0 for Windows

Open a regular Powershell terminal and type the following inside:

cd sd-scripts
.\venv\Scripts\activate
pip install bitsandbytes==0.35.0

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

This will install bitsandbytes 0.35.0 and copy the necessary files to the bitsandbytes directory.

bitsandbytes 0.41.1 for Windows

Install the Windows version whl file from here or other sources, like:

python -m pip install bitsandbytes==0.41.1 --prefer-binary --extra-index-url=https://jllllll.github.io/bitsandbytes-windows-webui

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

SDXL training

The documentation in this section will be moved to a separate document later.

Training scripts for SDXL

• sdxl_train.py is a script for SDXL fine-tuning. The usage is almost the same as fine_tune.py, but it also supports DreamBooth dataset.

  • --full_bf16 option is added. Thanks to KohakuBlueleaf!
    • This option enables the full bfloat16 training (includes gradients). This option is useful to reduce the GPU memory usage.
    • The full bfloat16 training might be unstable. Please use it at your own risk.
  • The different learning rates for each U-Net block are now supported in sdxl_train.py. Specify with --block_lr option. Specify 23 values separated by commas like --block_lr 1e-3,1e-3 ... 1e-3.
    • 23 values correspond to 0: time/label embed, 1-9: input blocks 0-8, 10-12: mid blocks 0-2, 13-21: output blocks 0-8, 22: out.

• prepare_buckets_latents.py now supports SDXL fine-tuning.

• sdxl_train_network.py is a script for LoRA training for SDXL. The usage is almost the same as train_network.py.

• Both scripts has following additional options:

  • --cache_text_encoder_outputs and --cache_text_encoder_outputs_to_disk: Cache the outputs of the text encoders. This option is useful to reduce the GPU memory usage. This option cannot be used with options for shuffling or dropping the captions.
  • --no_half_vae: Disable the half-precision (mixed-precision) VAE. VAE for SDXL seems to produce NaNs in some cases. This option is useful to avoid the NaNs.

• --weighted_captions option is not supported yet for both scripts.

• sdxl_train_textual_inversion.py is a script for Textual Inversion training for SDXL. The usage is almost the same as train_textual_inversion.py.

  • --cache_text_encoder_outputs is not supported.
  • There are two options for captions:
    1. Training with captions. All captions must include the token string. The token string is replaced with multiple tokens.
    2. Use --use_object_template or --use_style_template option. The captions are generated from the template. The existing captions are ignored.
  • See below for the format of the embeddings.

• --min_timestep and --max_timestep options are added to each training script. These options can be used to train U-Net with different timesteps. The default values are 0 and 1000.

Utility scripts for SDXL

• tools/cache_latents.py is added. This script can be used to cache the latents to disk in advance.

  • The options are almost the same as `sdxl_train.py'. See the help message for the usage.
  • Please launch the script as follows: accelerate launch --num_cpu_threads_per_process 1 tools/cache_latents.py ...
  • This script should work with multi-GPU, but it is not tested in my environment.

• tools/cache_text_encoder_outputs.py is added. This script can be used to cache the text encoder outputs to disk in advance.

  • The options are almost the same as cache_latents.py and sdxl_train.py. See the help message for the usage.

• sdxl_gen_img.py is added. This script can be used to generate images with SDXL, including LoRA, Textual Inversion and ControlNet-LLLite. See the help message for the usage.

Tips for SDXL training

• The default resolution of SDXL is 1024x1024.
• The fine-tuning can be done with 24GB GPU memory with the batch size of 1. For 24GB GPU, the following options are recommended for the fine-tuning with 24GB GPU memory:
  • Train U-Net only.
  • Use gradient checkpointing.
  • Use --cache_text_encoder_outputs option and caching latents.
  • Use Adafactor optimizer. RMSprop 8bit or Adagrad 8bit may work. AdamW 8bit doesn't seem to work.
• The LoRA training can be done with 8GB GPU memory (10GB recommended). For reducing the GPU memory usage, the following options are recommended:
  • Train U-Net only.
  • Use gradient checkpointing.
  • Use --cache_text_encoder_outputs option and caching latents.
  • Use one of 8bit optimizers or Adafactor optimizer.
  • Use lower dim (4 to 8 for 8GB GPU).
• --network_train_unet_only option is highly recommended for SDXL LoRA. Because SDXL has two text encoders, the result of the training will be unexpected.
• PyTorch 2 seems to use slightly less GPU memory than PyTorch 1.
• --bucket_reso_steps can be set to 32 instead of the default value 64. Smaller values than 32 will not work for SDXL training.

Example of the optimizer settings for Adafactor with the fixed learning rate:

optimizer_type = "adafactor"
optimizer_args = [ "scale_parameter=False", "relative_step=False", "warmup_init=False" ]
lr_scheduler = "constant_with_warmup"
lr_warmup_steps = 100
learning_rate = 4e-7 # SDXL original learning rate

Format of Textual Inversion embeddings for SDXL

from safetensors.torch import save_file

state_dict = {"clip_g": embs_for_text_encoder_1280, "clip_l": embs_for_text_encoder_768}
save_file(state_dict, file)

ControlNet-LLLite

ControlNet-LLLite, a novel method for ControlNet with SDXL, is added. See documentation for details.

Change History

Nov 5, 2023 / 2023/11/5

• sdxl_train.py now supports different learning rates for each Text Encoder.

  • Example:
    • --learning_rate 1e-6: train U-Net only
    • --train_text_encoder --learning_rate 1e-6: train U-Net and two Text Encoders with the same learning rate (same as the previous version)
    • --train_text_encoder --learning_rate 1e-6 --learning_rate_te1 1e-6 --learning_rate_te2 1e-6: train U-Net and two Text Encoders with the different learning rates
    • --train_text_encoder --learning_rate 0 --learning_rate_te1 1e-6 --learning_rate_te2 1e-6: train two Text Encoders only
    • --train_text_encoder --learning_rate 1e-6 --learning_rate_te1 1e-6 --learning_rate_te2 0: train U-Net and one Text Encoder only
    • --train_text_encoder --learning_rate 0 --learning_rate_te1 0 --learning_rate_te2 1e-6: train one Text Encoder only

• train_db.py and fine_tune.py now support different learning rates for Text Encoder. Specify with --learning_rate_te option.

  • To train Text Encoder with fine_tune.py, specify --train_text_encoder option too. train_db.py trains Text Encoder by default.

• Fixed the bug that Text Encoder is not trained when block lr is specified in sdxl_train.py.

• Debiased Estimation loss is added to each training script. Thanks to sdbds!

  • Specify --debiased_estimation_loss option to enable it. See PR #889 for details.

• Training of Text Encoder is improved in train_network.py and sdxl_train_network.py. Thanks to KohakuBlueleaf! PR #895

• The moving average of the loss is now displayed in the progress bar in each training script. Thanks to shirayu! PR #899

• PagedAdamW32bit optimizer is supported. Specify --optimizer_type=PagedAdamW32bit. Thanks to xzuyn! PR #900

• Other bug fixes and improvements.

• sdxl_train.py で、二つのText Encoderそれぞれに独立した学習率が指定できるようになりました。サンプルは上の英語版を参照してください。

• train_db.py および fine_tune.py で Text Encoder に別の学習率を指定できるようになりました。--learning_rate_te オプションで指定してください。

  • fine_tune.py で Text Encoder を学習するには --train_text_encoder オプションをあわせて指定してください。train_db.py はデフォルトで学習します。

• sdxl_train.py で block lr を指定すると Text Encoder が学習されない不具合を修正しました。

• Debiased Estimation loss が各学習スクリプトに追加されました。sdbsd 氏に感謝します。

  • --debiased_estimation_loss を指定すると有効になります。詳細は PR #889 を参照してください。

• train_network.py と sdxl_train_network.py でText Encoderの学習が改善されました。KohakuBlueleaf 氏に感謝します。 PR #895

• 各学習スクリプトで移動平均のlossがプログレスバーに表示されるようになりました。shirayu 氏に感謝します。 PR #899

• PagedAdamW32bit オプティマイザがサポートされました。--optimizer_type=PagedAdamW32bit と指定してください。xzuyn 氏に感謝します。 PR #900

• その他のバグ修正と改善。

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

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-C3Lier 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-C3Lierを使いWeb UIで生成するには拡張を使用してください。

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。