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# This powershell script will create a model using the fine tuning dreambooth method. It will require landscape,
# portrait and square images.
#
# Adjust the script to your own needs
# 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_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 $num_cpu_threads_per_process train_db.py `
--v2 `
--pretrained_model_name_or_path=$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.
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