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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
# Sylvia Ritter
# variable values
$pretrained_model_name_or_path = "D:\models\v1-5-pruned-mse-vae.ckpt"
$train_dir = "D:\dreambooth\train_sylvia_ritter\raw_data"
$landscape_image_num = 4
$portrait_image_num = 25
$square_image_num = 2
$learning_rate = 1e-6
$dataset_repeats = 120
$train_batch_size = 4
$epoch = 1
$save_every_n_epochs=1
$mixed_precision="fp16"
$num_cpu_threads_per_process=6
$landscape_folder_name = "landscape-pp"
$landscape_resolution = "832,512"
$portrait_folder_name = "portrait-pp"
$portrait_resolution = "448,896"
$square_folder_name = "square-pp"
$square_resolution = "512,512"
# You should not have to change values past this point
$landscape_data_dir = $train_dir + "\" + $landscape_folder_name
$portrait_data_dir = $train_dir + "\" + $portrait_folder_name
$square_data_dir = $train_dir + "\" + $square_folder_name
$landscape_output_dir = $train_dir + "\model-l"
$portrait_output_dir = $train_dir + "\model-lp"
$square_output_dir = $train_dir + "\model-lps"
$landscape_repeats = $landscape_image_num * $dataset_repeats
$portrait_repeats = $portrait_image_num * $dataset_repeats
$square_repeats = $square_image_num * $dataset_repeats
$landscape_mts = [Math]::Ceiling($landscape_repeats / $train_batch_size * $epoch)
$portrait_mts = [Math]::Ceiling($portrait_repeats / $train_batch_size * $epoch)
$square_mts = [Math]::Ceiling($square_repeats / $train_batch_size * $epoch)
# Write-Output $landscape_repeats
.\venv\Scripts\activate
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
--pretrained_model_name_or_path=$pretrained_model_name_or_path `
--train_data_dir=$landscape_data_dir `
--output_dir=$landscape_output_dir `
--resolution=$landscape_resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$landscape_mts `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$dataset_repeats `
--save_half
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
--pretrained_model_name_or_path=$landscape_output_dir"\last.ckpt" `
--train_data_dir=$portrait_data_dir `
--output_dir=$portrait_output_dir `
--resolution=$portrait_resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$portrait_mts `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$dataset_repeats `
--save_half
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
--pretrained_model_name_or_path=$portrait_output_dir"\last.ckpt" `
--train_data_dir=$square_data_dir `
--output_dir=$square_output_dir `
--resolution=$square_resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$square_mts `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$dataset_repeats `
--save_half
# 2nd pass at half the dataset repeat value
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
--pretrained_model_name_or_path=$square_output_dir"\last.ckpt" `
--train_data_dir=$landscape_data_dir `
--output_dir=$landscape_output_dir"2" `
--resolution=$landscape_resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$([Math]::Ceiling($landscape_mts/2)) `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$([Math]::Ceiling($dataset_repeats/2)) `
--save_half
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
--pretrained_model_name_or_path=$landscape_output_dir"2\last.ckpt" `
--train_data_dir=$portrait_data_dir `
--output_dir=$portrait_output_dir"2" `
--resolution=$portrait_resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$([Math]::Ceiling($portrait_mts/2)) `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$([Math]::Ceiling($dataset_repeats/2)) `
--save_half
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
--pretrained_model_name_or_path=$portrait_output_dir"2\last.ckpt" `
--train_data_dir=$square_data_dir `
--output_dir=$square_output_dir"2" `
--resolution=$square_resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$([Math]::Ceiling($square_mts/2)) `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$([Math]::Ceiling($dataset_repeats/2)) `
--save_half
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