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# Sylvia Ritter. AKA: by silvery trait
# variable values
$pretrained_model_name_or_path = "D:\models\v1-5-pruned-mse-vae.ckpt"
$train_dir = "D:\models\blakecreates"
$output_dir = "D:\models\blakecreates\diffusers_fine_tuned_model_bucket"
$training_folder = "dataset\192_cables retake artstyle"
$learning_rate = 1e-6
$dataset_repeats = 192
$train_batch_size = 4
$epoch = 1
$save_every_n_epochs=1
$mixed_precision="fp16"
$num_cpu_threads_per_process=6
$lr_scheduler="constant"
$max_resolution = "576,704"
# You should not have to change values past this point
$data_dir = $train_dir + "\" + $training_folder
# Get smalest image resolution to prevent upscaling to that
$shellApp = New-Object -ComObject 'shell.application'
$folderNamespace = $shellApp.Namespace($data_dir)
$get_smalest_resolution = Get-ChildItem $data_dir -Recurse -File -Include *.png, *.jpg, *.webp |
ForEach-Object {
$image = $folderNamespace.ParseName($_.Name)
if ($folderNamespace.GetDetailsOf($image, 31) -match '(?<width>\d+) x (?<height>\d+)') {
[PsCustomObject]@{
Image = $_.FullName
Width = $Matches.width
Height = $Matches.height
PixelResolution = [int]($Matches.width) * [int]($Matches.height)
# IsPortrait = $([int]$Matches.height -gt [int]$Matches.width)
}
}
} | Sort-Object -Property PixelResolution | Select-Object -first 1
$width, $height = $max_resolution -split ","
if (([int]$width * [int]$height) -gt $get_smalest_resolution.PixelResolution) {
$smalest_resolution = $get_smalest_resolution.Width + "," + $get_smalest_resolution.Height
Write-Output "Smallest resolution of images data found is " $get_smalest_resolution.Image "at $smalest_resolution. Replace image with a larger one, or change resolution to prevent this resolution change."
$max_resolution = $smalest_resolution
}
# stop script on error
$ErrorActionPreference = "Stop"
# activate venv
cd D:\kohya_ss
.\venv\Scripts\activate
# create caption json file
python D:\kohya_ss\diffusers_fine_tuning\merge_captions_to_metadata.py `
--caption_extention ".caption" $train_dir"\"$training_folder $train_dir"\meta_cap.json"
# create images buckets
python D:\kohya_ss\diffusers_fine_tuning\prepare_buckets_latents.py `
$train_dir"\"$training_folder `
$train_dir"\meta_cap.json" `
$train_dir"\meta_lat.json" `
$pretrained_model_name_or_path `
--batch_size 4 --max_resolution $max_resolution --mixed_precision fp16
# Get number of valid images
$image_num = Get-ChildItem "$train_dir\$training_folder" -Recurse -File -Include *.npz | Measure-Object | %{$_.Count}
$repeats = $image_num * $dataset_repeats
# calculate max_train_set
$max_train_set = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process D:\kohya_ss\diffusers_fine_tuning\fine_tune.py `
--pretrained_model_name_or_path=$pretrained_model_name_or_path `
--in_json $train_dir"\meta_lat.json" `
--train_data_dir=$train_dir"\"$training_folder `
--output_dir=$output_dir `
--train_batch_size=$train_batch_size `
--dataset_repeats=$dataset_repeats `
--learning_rate=$learning_rate `
--max_train_steps=$max_train_set `
--use_8bit_adam --xformers `
--mixed_precision=$mixed_precision `
--save_every_n_epochs=$save_every_n_epochs `
--lr_scheduler=$lr_scheduler `
--seed=494481440 `
--train_text_encoder `
--save_precision="fp16"
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