65 lines
2.0 KiB
PowerShell
65 lines
2.0 KiB
PowerShell
# This powershell script will create a model using the fine tuning dreambooth method. It will require landscape,
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# portrait and square images.
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#
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# Adjust the script to your own needs
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# variable values
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$pretrained_model_name_or_path = "D:\models\512-base-ema.ckpt"
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$data_dir = "D:\models\dariusz_zawadzki\kohya_reg\data"
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$reg_data_dir = "D:\models\dariusz_zawadzki\kohya_reg\reg"
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$logging_dir = "D:\models\dariusz_zawadzki\logs"
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$output_dir = "D:\models\dariusz_zawadzki\train_db_model_reg_v2"
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$resolution = "512,512"
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$lr_scheduler="polynomial"
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$cache_latents = 1 # 1 = true, 0 = false
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$image_num = Get-ChildItem $data_dir -Recurse -File -Include *.png, *.jpg, *.webp | Measure-Object | %{$_.Count}
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Write-Output "image_num: $image_num"
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$dataset_repeats = 200
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$learning_rate = 2e-6
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$train_batch_size = 4
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$epoch = 1
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$save_every_n_epochs=1
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$mixed_precision="bf16"
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$num_cpu_threads_per_process=6
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# You should not have to change values past this point
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if ($cache_latents -eq 1) {
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$cache_latents_value="--cache_latents"
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}
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else {
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$cache_latents_value=""
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}
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$repeats = $image_num * $dataset_repeats
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$mts = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
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Write-Output "Repeats: $repeats"
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cd D:\kohya_ss
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.\venv\Scripts\activate
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accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
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--v2 `
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--pretrained_model_name_or_path=$pretrained_model_name_or_path `
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--train_data_dir=$data_dir `
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--output_dir=$output_dir `
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--resolution=$resolution `
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--train_batch_size=$train_batch_size `
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--learning_rate=$learning_rate `
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--max_train_steps=$mts `
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--use_8bit_adam `
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--xformers `
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--mixed_precision=$mixed_precision `
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$cache_latents_value `
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--save_every_n_epochs=$save_every_n_epochs `
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--logging_dir=$logging_dir `
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--save_precision="fp16" `
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--reg_data_dir=$reg_data_dir `
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--seed=494481440 `
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--lr_scheduler=$lr_scheduler
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# 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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