python train_db.py --pretrained_model_name_or_path=/data1/lujiale/stable-diffusion-webui/models/Stable-diffusion/chilloutmix_NiPrunedFp32Fix.safetensors --dataset_config=./finetune/dreamboothconfig.toml --output_dir=./dataset/dreambooth_out --output_name=dreamboothmodel --save_model_as=safetensors --prior_loss_weight=1.0 --learning_rate=1e-6 --max_train_steps=1600 --use_8bit_adam --mixed_precision=fp16 --xformers --cache_latents --gradient_checkpointing 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。

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.

Training guide - common : data preparation, options etc...
- Dataset config
DreamBooth training guide
Step by Step fine-tuning guide:
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!

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

4 Apr. 2023, 2023/4/4:
- There may be bugs because I changed a lot. If you cannot revert the script to the previous version when a problem occurs, please wait for the update for a while.
- The learning rate and dim (rank) of each block may not work with other modules (LyCORIS, etc.) because the module needs to be changed.
- Fix some bugs and add some features.
  - Fix an issue that .json format dataset config files cannot be read. issue #351 Thanks to rockerBOO!
  - Raise an error when an invalid --lr_warmup_steps option is specified (when warmup is not valid for the specified scheduler). PR #364 Thanks to shirayu!
  - Add min_snr_gamma to metadata in train_network.py. PR #373 Thanks to rockerBOO!
  - Fix the data type handling in fine_tune.py. This may fix an error that occurs in some environments when using xformers, npz format cache, and mixed_precision.
- Add options to train_network.py to specify block weights for learning rates. PR #355 Thanks to u-haru for the great contribution!
  - Specify the weights of 25 blocks for the full model.
    - No LoRA corresponds to the first block, but 25 blocks are specified for compatibility with 'LoRA block weight' etc. Also, if you do not expand to conv2d3x3, some blocks do not have LoRA, but please specify 25 values for the argument for consistency.
  - Specify the following arguments with --network_args.
  - down_lr_weight : Specify the learning rate weight of the down blocks of U-Net. The following can be specified.
    - The weight for each block: Specify 12 numbers such as "down_lr_weight=0,0,0,0,0,0,1,1,1,1,1,1".
    - Specify from preset: Specify such as "down_lr_weight=sine" (the weights by sine curve). sine, cosine, linear, reverse_linear, zeros can be specified. Also, if you add +number such as "down_lr_weight=cosine+.25", the specified number is added (such as 0.25~1.25).
  - mid_lr_weight : Specify the learning rate weight of the mid block of U-Net. Specify one number such as "down_lr_weight=0.5".
  - up_lr_weight : Specify the learning rate weight of the up blocks of U-Net. The same as down_lr_weight.
  - If you omit the some arguments, the 1.0 is used. Also, if you set the weight to 0, the LoRA modules of that block are not created.
  - block_lr_zero_threshold : If the weight is not more than this value, the LoRA module is not created. The default is 0.
- Add options to train_network.py to specify block dims (ranks) for variable rank.
  - Specify 25 values for the full model of 25 blocks. Some blocks do not have LoRA, but specify 25 values always.
  - Specify the following arguments with --network_args.
  - block_dims : Specify the dim (rank) of each block. Specify 25 numbers such as "block_dims=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2".
  - block_alphas : Specify the alpha of each block. Specify 25 numbers as with block_dims. If omitted, the value of network_alpha is used.
  - conv_block_dims : Expand LoRA to Conv2d 3x3 and specify the dim (rank) of each block.
  - conv_block_alphas : Specify the alpha of each block when expanding LoRA to Conv2d 3x3. If omitted, the value of conv_alpha is used.
- 大きく変更したため不具合があるかもしれません。問題が起きた時にスクリプトを前のバージョンに戻せない場合は、しばらく更新を控えてください。
- 階層別学習率、階層別dim(rank)についてはモジュール側の変更が必要なため、当リポジトリ内のnetworkモジュール以外（LyCORISなど）では現在は動作しないと思われます。
- いくつかのバグ修正、機能追加を行いました。
  - .json形式のdataset設定ファイルを読み込めない不具合を修正しました。 issue #351 rockerBOO 氏に感謝します。
  - 無効な--lr_warmup_steps オプション（指定したスケジューラでwarmupが無効な場合）を指定している場合にエラーを出すようにしました。 PR #364 shirayu 氏に感謝します。
  - train_network.py で min_snr_gamma をメタデータに追加しました。 PR #373 rockerBOO 氏に感謝します。
  - fine_tune.py でデータ型の取り扱いが誤っていたのを修正しました。一部の環境でxformersを使い、npz形式のキャッシュ、mixed_precisionで学習した時にエラーとなる不具合が解消されるかもしれません。
- 階層別学習率を train_network.py で指定できるようになりました。PR #355 u-haru 氏の多大な貢献に感謝します。
  - フルモデルの25個のブロックの重みを指定できます。
    - 最初のブロックに該当するLoRAは存在しませんが、階層別LoRA適用等との互換性のために25個としています。またconv2d3x3に拡張しない場合も一部のブロックにはLoRAが存在しませんが、記述を統一するため常に25個の値を指定してください。 ---network_args で以下の引数を指定してください。
  - down_lr_weight : U-Netのdown blocksの学習率の重みを指定します。以下が指定可能です。
    - ブロックごとの重み : "down_lr_weight=0,0,0,0,0,0,1,1,1,1,1,1" のように12個の数値を指定します。
    - プリセットからの指定 : "down_lr_weight=sine" のように指定します（サインカーブで重みを指定します）。sine, cosine, linear, reverse_linear, zeros が指定可能です。また "down_lr_weight=cosine+.25" のように +数値 を追加すると、指定した数値を加算します（0.25~1.25になります）。
  - mid_lr_weight : U-Netのmid blockの学習率の重みを指定します。"down_lr_weight=0.5" のように数値を一つだけ指定します。
  - up_lr_weight : U-Netのup blocksの学習率の重みを指定します。down_lr_weightと同様です。
  - 指定を省略した部分は1.0として扱われます。また重みを0にするとそのブロックのLoRAモジュールは作成されません。
  - block_lr_zero_threshold : 重みがこの値以下の場合、LoRAモジュールを作成しません。デフォルトは0です。
- 階層別dim (rank)を train_network.py で指定できるようになりました。
  - フルモデルの25個のブロックのdim (rank)を指定できます。階層別学習率と同様に一部のブロックにはLoRAが存在しない場合がありますが、常に25個の値を指定してください。
  - --network_args で以下の引数を指定してください。
  - block_dims : 各ブロックのdim (rank)を指定します。"block_dims=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2" のように25個の数値を指定します。
  - block_alphas : 各ブロックのalphaを指定します。block_dimsと同様に25個の数値を指定します。省略時はnetwork_alphaの値が使用されます。
  - conv_block_dims : LoRAをConv2d 3x3に拡張し、各ブロックのdim (rank)を指定します。
  - conv_block_alphas : LoRAをConv2d 3x3に拡張したときの各ブロックのalphaを指定します。省略時はconv_alphaの値が使用されます。
- 階層別学習率コマンドライン指定例 / Examples of block learning rate command line specification:
  
  --network_args "down_lr_weight=0.5,0.5,0.5,0.5,1.0,1.0,1.0,1.0,1.5,1.5,1.5,1.5" "mid_lr_weight=2.0" "up_lr_weight=1.5,1.5,1.5,1.5,1.0,1.0,1.0,1.0,0.5,0.5,0.5,0.5"
  
  --network_args "block_lr_zero_threshold=0.1" "down_lr_weight=sine+.5" "mid_lr_weight=1.5" "up_lr_weight=cosine+.5"
- 階層別学習率tomlファイル指定例 / Examples of block learning rate toml file specification
  
  network_args = [ "down_lr_weight=0.5,0.5,0.5,0.5,1.0,1.0,1.0,1.0,1.5,1.5,1.5,1.5", "mid_lr_weight=2.0", "up_lr_weight=1.5,1.5,1.5,1.5,1.0,1.0,1.0,1.0,0.5,0.5,0.5,0.5",]
  
  network_args = [ "block_lr_zero_threshold=0.1", "down_lr_weight=sine+.5", "mid_lr_weight=1.5", "up_lr_weight=cosine+.5", ]
- 階層別dim (rank)コマンドライン指定例 / Examples of block dim (rank) command line specification:
  
  --network_args "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2"
  
  --network_args "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2" "conv_block_dims=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2"
  
  --network_args "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2" "block_alphas=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2"
- 階層別dim (rank)tomlファイル指定例 / Examples of block dim (rank) toml file specification
  
  network_args = [ "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2",]
  
  network_args = [ "block_dims=2,4,4,4,8,8,8,8,12,12,12,12,16,12,12,12,12,8,8,8,8,4,4,4,2", "block_alphas=2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2",]

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。

lujiale621 / trainsystem