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v17.1
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@bmaltais
bmaltais committed Dec 17, 2022
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311 changes: 18 additions & 293 deletions README.md
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Expand Up @@ -77,6 +77,20 @@ pip install --upgrade -r requirements.txt

Once the commands have completed successfully you should be ready to use the new version.

## GUI

There is now support for GUI based training using gradio. You can start the GUI interface by running:

```powershell
python .\dreambooth_gui.py
```

## Quickstart screencast

You can find a screen cast on how to use the GUI at the following location:

https://youtu.be/RlvqEKj03WI

## Folders configuration

Refer to the note to understand how to create the folde structure. In short it should look like:
Expand Down Expand Up @@ -106,305 +120,16 @@ my_asd_dog_dreambooth
`- dog8.png
```

## GUI

There is now support for GUI based training using gradio. You can start the GUI interface by running:

```powershell
python .\dreambooth_gui.py
```

## Support

Drop by the discord server for support: https://discord.com/channels/1041518562487058594/1041518563242020906

## Manual Script Execution

### SD1.5 example

Edit and paste the following in a Powershell terminal:

```powershell
accelerate launch --num_cpu_threads_per_process 6 train_db_fixed.py `
--pretrained_model_name_or_path="D:\models\last.ckpt" `
--train_data_dir="D:\dreambooth\train_bernard\train_man" `
--reg_data_dir="D:\dreambooth\train_bernard\reg_man" `
--output_dir="D:\dreambooth\train_bernard" `
--prior_loss_weight=1.0 `
--resolution=512 `
--train_batch_size=1 `
--learning_rate=1e-6 `
--max_train_steps=2100 `
--use_8bit_adam `
--xformers `
--mixed_precision="fp16" `
--cache_latents `
--gradient_checkpointing `
--save_every_n_epochs=1
```

### SD2.0 512 Base example

```powershell
# variable values
$pretrained_model_name_or_path = "D:\models\512-base-ema.ckpt"
$data_dir = "D:\models\dariusz_zawadzki\kohya_reg\data"
$reg_data_dir = "D:\models\dariusz_zawadzki\kohya_reg\reg"
$logging_dir = "D:\models\dariusz_zawadzki\logs"
$output_dir = "D:\models\dariusz_zawadzki\train_db_fixed_model_reg_v2"
$resolution = "512,512"
$lr_scheduler="polynomial"
$cache_latents = 1 # 1 = true, 0 = false
$image_num = Get-ChildItem $data_dir -Recurse -File -Include *.png, *.jpg, *.webp | Measure-Object | %{$_.Count}
Write-Output "image_num: $image_num"
$dataset_repeats = 200
$learning_rate = 2e-6
$train_batch_size = 4
$epoch = 1
$save_every_n_epochs=1
$mixed_precision="bf16"
$num_cpu_threads_per_process=6
# You should not have to change values past this point
if ($cache_latents -eq 1) {
$cache_latents_value="--cache_latents"
}
else {
$cache_latents_value=""
}
$repeats = $image_num * $dataset_repeats
$mts = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
Write-Output "Repeats: $repeats"
cd D:\kohya_ss
.\venv\Scripts\activate
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed.py `
--v2 `
--pretrained_model_name_or_path=$pretrained_model_name_or_path `
--train_data_dir=$data_dir `
--output_dir=$output_dir `
--resolution=$resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$mts `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
$cache_latents_value `
--save_every_n_epochs=$save_every_n_epochs `
--logging_dir=$logging_dir `
--save_precision="fp16" `
--reg_data_dir=$reg_data_dir `
--seed=494481440 `
--lr_scheduler=$lr_scheduler
# Add the inference yaml file along with the model for proper loading. Need to have the same name as model... Most likelly "last.yaml" in our case.
cp v2_inference\v2-inference.yaml $output_dir"\last.yaml"
```

### SD2.0 768v Base example

```powershell
# variable values
$pretrained_model_name_or_path = "C:\Users\berna\Downloads\768-v-ema.ckpt"
$data_dir = "D:\dreambooth\train_paper_artwork\kohya\data"
$logging_dir = "D:\dreambooth\train_paper_artwork"
$output_dir = "D:\models\paper_artwork\train_db_fixed_model_v2_768v"
$resolution = "768,768"
$lr_scheduler="polynomial"
$cache_latents = 1 # 1 = true, 0 = false
$image_num = Get-ChildItem $data_dir -Recurse -File -Include *.png, *.jpg, *.webp | Measure-Object | %{$_.Count}
Write-Output "image_num: $image_num"
$dataset_repeats = 200
$learning_rate = 2e-6
$train_batch_size = 4
$epoch = 1
$save_every_n_epochs=1
$mixed_precision="bf16"
$num_cpu_threads_per_process=6
# You should not have to change values past this point
if ($cache_latents -eq 1) {
$cache_latents_value="--cache_latents"
}
else {
$cache_latents_value=""
}
$repeats = $image_num * $dataset_repeats
$mts = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
Write-Output "Repeats: $repeats"
cd D:\kohya_ss
.\venv\Scripts\activate
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db_fixed.py `
--v2 `
--v_parameterization `
--pretrained_model_name_or_path=$pretrained_model_name_or_path `
--train_data_dir=$data_dir `
--output_dir=$output_dir `
--resolution=$resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$mts `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
$cache_latents_value `
--save_every_n_epochs=$save_every_n_epochs `
--logging_dir=$logging_dir `
--save_precision="fp16" `
--seed=494481440 `
--lr_scheduler=$lr_scheduler
# Add the inference 768v yaml file along with the model for proper loading. Need to have the same name as model... Most likelly "last.yaml" in our case.
cp v2_inference\v2-inference-v.yaml $output_dir"\last.yaml"
```

## Finetuning

If you would rather use model finetuning rather than the dreambooth method you can use a command similat to the following. The advantage of fine tuning is that you do not need to worry about regularization images... but you need to provide captions for every images. The caption will be used to train the model. You can use auto1111 to preprocess your training images and add either BLIP or danbooru captions to them. You then need to edit those to add the name of the model and correct any wrong description.

```
accelerate launch --num_cpu_threads_per_process 6 train_db_fixed-ber.py `
--pretrained_model_name_or_path="D:\models\alexandrine_teissier_and_bernard_maltais-400-kohya-sd15-v1.ckpt" `
--train_data_dir="D:\dreambooth\source\alet_et_bernard\landscape-pp" `
--output_dir="D:\dreambooth\train_alex_and_bernard" `
--resolution="640,448" `
--train_batch_size=1 `
--learning_rate=1e-6 `
--max_train_steps=550 `
--use_8bit_adam `
--xformers `
--mixed_precision="fp16" `
--cache_latents `
--save_every_n_epochs=1 `
--fine_tuning `
--enable_bucket `
--dataset_repeats=200 `
--seed=23 `
---save_precision="fp16"
```

Refer to this url for more details about finetuning: https://note.com/kohya_ss/n/n1269f1e1a54e

## Options list

```txt
usage: train_db_fixed.py [-h] [--v2] [--v_parameterization] [--pretrained_model_name_or_path PRETRAINED_MODEL_NAME_OR_PATH]
[--fine_tuning] [--shuffle_caption] [--caption_extention CAPTION_EXTENTION]
[--caption_extension CAPTION_EXTENSION] [--train_data_dir TRAIN_DATA_DIR]
[--reg_data_dir REG_DATA_DIR] [--dataset_repeats DATASET_REPEATS] [--output_dir OUTPUT_DIR]
[--use_safetensors] [--save_every_n_epochs SAVE_EVERY_N_EPOCHS] [--save_state] [--resume RESUME]
[--prior_loss_weight PRIOR_LOSS_WEIGHT] [--no_token_padding]
[--stop_text_encoder_training STOP_TEXT_ENCODER_TRAINING] [--color_aug] [--flip_aug]
[--face_crop_aug_range FACE_CROP_AUG_RANGE] [--random_crop] [--debug_dataset]
[--resolution RESOLUTION] [--train_batch_size TRAIN_BATCH_SIZE] [--use_8bit_adam] [--mem_eff_attn]
[--xformers] [--vae VAE] [--cache_latents] [--enable_bucket] [--min_bucket_reso MIN_BUCKET_RESO]
[--max_bucket_reso MAX_BUCKET_RESO] [--learning_rate LEARNING_RATE]
[--max_train_steps MAX_TRAIN_STEPS] [--seed SEED] [--gradient_checkpointing]
[--mixed_precision {no,fp16,bf16}] [--full_fp16] [--save_precision {None,float,fp16,bf16}]
[--clip_skip CLIP_SKIP] [--logging_dir LOGGING_DIR] [--log_prefix LOG_PREFIX]
[--lr_scheduler LR_SCHEDULER] [--lr_warmup_steps LR_WARMUP_STEPS]
options:
-h, --help show this help message and exit
--v2 load Stable Diffusion v2.0 model / Stable Diffusion 2.0のモデルを読み込む
--v_parameterization enable v-parameterization training / v-parameterization学習を有効にする
--pretrained_model_name_or_path PRETRAINED_MODEL_NAME_OR_PATH
pretrained model to train, directory to Diffusers model or StableDiffusion checkpoint /
学習元モデル、Diffusers形式モデルのディレクトリまたはStableDiffusionのckptファイル
--fine_tuning fine tune the model instead of DreamBooth / DreamBoothではなくfine tuningする
--shuffle_caption shuffle comma-separated caption / コンマで区切られたcaptionの各要素をshuffleする
--caption_extention CAPTION_EXTENTION
extension of caption files (backward compatiblity) / 読み込むcaptionファイルの拡張子（スペルミスを残してあります）
--caption_extension CAPTION_EXTENSION
extension of caption files / 読み込むcaptionファイルの拡張子
--train_data_dir TRAIN_DATA_DIR
directory for train images / 学習画像データのディレクトリ
--reg_data_dir REG_DATA_DIR
directory for regularization images / 正則化画像データのディレクトリ
--dataset_repeats DATASET_REPEATS
repeat dataset in fine tuning / fine tuning時にデータセットを繰り返す回数
--output_dir OUTPUT_DIR
directory to output trained model / 学習後のモデル出力先ディレクトリ
--use_safetensors use safetensors format to save / checkpoint、モデルをsafetensors形式で保存する
--save_every_n_epochs SAVE_EVERY_N_EPOCHS
save checkpoint every N epochs / 学習中のモデルを指定エポックごとに保存する
--save_state save training state additionally (including optimizer states etc.) /
optimizerなど学習状態も含めたstateを追加で保存する
--resume RESUME saved state to resume training / 学習再開するモデルのstate
--prior_loss_weight PRIOR_LOSS_WEIGHT
loss weight for regularization images / 正則化画像のlossの重み
--no_token_padding disable token padding (same as Diffuser's DreamBooth) /
トークンのpaddingを無効にする（Diffusers版DreamBoothと同じ動作）
--stop_text_encoder_training STOP_TEXT_ENCODER_TRAINING
steps to stop text encoder training / Text Encoderの学習を止めるステップ数
--color_aug enable weak color augmentation / 学習時に色合いのaugmentationを有効にする
--flip_aug enable horizontal flip augmentation / 学習時に左右反転のaugmentationを有効にする
--face_crop_aug_range FACE_CROP_AUG_RANGE
enable face-centered crop augmentation and its range (e.g. 2.0,4.0) /
学習時に顔を中心とした切り出しaugmentationを有効にするときは倍率を指定する（例：2.0,4.0）
--random_crop enable random crop (for style training in face-centered crop augmentation) /
ランダムな切り出しを有効にする（顔を中心としたaugmentationを行うときに画風の学習用に指定する）
--debug_dataset show images for debugging (do not train) / デバッグ用に学習データを画面表示する（学習は行わない）
--resolution RESOLUTION
resolution in training ('size' or 'width,height') / 学習時の画像解像度（'サイズ'指定、または'幅,高さ' 指定）
--train_batch_size TRAIN_BATCH_SIZE
batch size for training (1 means one train or reg data, not train/reg pair) /
学習時のバッチサイズ（1でtrain/regをそれぞれ1件ずつ学習）
--use_8bit_adam use 8bit Adam optimizer (requires bitsandbytes) / 8bit Adamオプティマイザを使う（bitsandbytesのインス トールが必要）
--mem_eff_attn use memory efficient attention for CrossAttention / CrossAttentionに省メモリ版attentionを使う
--xformers use xformers for CrossAttention / CrossAttentionにxformersを使う
--vae VAE path to checkpoint of vae to replace / VAEを入れ替える場合、VAEのcheckpointファイルまたはディレクトリ
--cache_latents cache latents to reduce memory (augmentations must be disabled) /
メモリ削減のためにlatentをcacheする（augmentationは使用不可）
--enable_bucket enable buckets for multi aspect ratio training / 複数解像度学習のためのbucketを有効にする
--min_bucket_reso MIN_BUCKET_RESO
minimum resolution for buckets / bucketの最小解像度
--max_bucket_reso MAX_BUCKET_RESO
maximum resolution for buckets / bucketの最小解像度
--learning_rate LEARNING_RATE
learning rate / 学習率
--max_train_steps MAX_TRAIN_STEPS
training steps / 学習ステップ数
--seed SEED random seed for training / 学習時の乱数のseed
--gradient_checkpointing
enable gradient checkpointing / grandient checkpointingを有効にする
--mixed_precision {no,fp16,bf16}
use mixed precision / 混合精度を使う場合、その精度
--full_fp16 fp16 training including gradients / 勾配も含めてfp16で学習する
--save_precision {None,float,fp16,bf16}
precision in saving (available in StableDiffusion checkpoint) /
保存時に精度を変更して保存する（StableDiffusion形式での保存時のみ有効）
--clip_skip CLIP_SKIP
use output of nth layer from back of text encoder (n>=1) / text encoderの後ろからn番目の層の出力を用いる（nは1以上）
--logging_dir LOGGING_DIR
enable logging and output TensorBoard log to this directory /
ログ出力を有効にしてこのディレクトリにTensorBoard用のログを出力する
--log_prefix LOG_PREFIX
add prefix for each log directory / ログディレクトリ名の先頭に追加する文字列
--lr_scheduler LR_SCHEDULER
scheduler to use for learning rate / 学習率のスケジューラ: linear, cosine, cosine_with_restarts, polynomial,
constant (default), constant_with_warmup
--lr_warmup_steps LR_WARMUP_STEPS
Number of steps for the warmup in the lr scheduler (default is 0) /
学習率のスケジューラをウォームアップするステップ数（デフォルト0）
```

## Change history

* 12/17 (v17.1) update:
- Adding GUI for kohya_ss called dreambooth_gui.py
- removing support for `--finetuning` as there is now a dedicated python repo for that. `--fine-tuning` is still there behind the scene until kohya_ss remove it in a future code release.
- removing cli examples as I will now focus on the GUI for training. People who prefer cli based training can still do that.
* 12/13 (v17) update:
- Added support for learning to fp16 gradient (experimental function). SD1.x can be trained with 8GB of VRAM. Specify full_fp16 options.
* 12/06 (v16) update:
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