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