File size: 2,824 Bytes
fe6327d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
# 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"
$data_dir = "D:\test\squat"
$train_dir = "D:\test\"
$resolution = "512,512"
$image_num = Get-ChildItem $data_dir -Recurse -File -Include *.png | Measure-Object | %{$_.Count}
Write-Output "image_num: $image_num"
$learning_rate = 1e-6
$dataset_repeats = 40
$train_batch_size = 8
$epoch = 1
$save_every_n_epochs=1
$mixed_precision="fp16"
$num_cpu_threads_per_process=6
# You should not have to change values past this point
$output_dir = $train_dir + "\model"
$repeats = $image_num * $dataset_repeats
$mts = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
Write-Output "Repeats: $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=$data_dir `
--output_dir=$output_dir `
--resolution=$resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$mts `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$dataset_repeats `
--save_precision="fp16"
# 2nd pass at half the dataset repeat value
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
--pretrained_model_name_or_path=$output_dir"\last.ckpt" `
--train_data_dir=$data_dir `
--output_dir=$output_dir"2" `
--resolution=$resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$([Math]::Ceiling($mts/2)) `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$([Math]::Ceiling($dataset_repeats/2)) `
--save_precision="fp16"
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process train_db.py `
--pretrained_model_name_or_path=$output_dir"\last.ckpt" `
--train_data_dir=$data_dir `
--output_dir=$output_dir"2" `
--resolution=$resolution `
--train_batch_size=$train_batch_size `
--learning_rate=$learning_rate `
--max_train_steps=$mts `
--use_8bit_adam `
--xformers `
--mixed_precision=$mixed_precision `
--cache_latents `
--save_every_n_epochs=$save_every_n_epochs `
--fine_tuning `
--dataset_repeats=$dataset_repeats `
--save_precision="fp16"
|