153 lines
6.1 KiB
PowerShell
153 lines
6.1 KiB
PowerShell
# variables related to the pretrained model
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$pretrained_model_name_or_path = "D:\models\test\samdoesart2\model\last"
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$v2 = 1 # set to 1 for true or 0 for false
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$v_model = 0 # set to 1 for true or 0 for false
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# variables related to the training dataset and output directory
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$train_dir = "D:\models\test\samdoesart2"
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$image_folder = "D:\dataset\samdoesart2\raw"
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$output_dir = "D:\models\test\samdoesart2\model_e2\"
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$max_resolution = "512,512"
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# variables related to the training process
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$learning_rate = 1e-6
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$lr_scheduler = "constant" # Default is constant
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$lr_warmup = 0 # % of steps to warmup for 0 - 100. Default is 0.
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$dataset_repeats = 40
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$train_batch_size = 8
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$epoch = 1
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$save_every_n_epochs = 1
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$mixed_precision = "bf16"
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$save_precision = "fp16" # use fp16 for better compatibility with auto1111 and other repo
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$seed = "494481440"
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$num_cpu_threads_per_process = 6
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$train_text_encoder = 0 # set to 1 to train text encoder otherwise set to 0
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# variables related to the resulting diffuser model. If input is ckpt or tensors then it is not applicable
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$convert_to_safetensors = 1 # set to 1 to convert resulting diffuser to ckpt
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$convert_to_ckpt = 1 # set to 1 to convert resulting diffuser to ckpt
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# other variables
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$kohya_finetune_repo_path = "D:\kohya_ss"
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### You should not need to change things below
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# Set variables to useful values using ternary operator
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$v_model = ($v_model -eq 0) ? $null : "--v_parameterization"
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$v2 = ($v2 -eq 0) ? $null : "--v2"
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$train_text_encoder = ($train_text_encoder -eq 0) ? $null : "--train_text_encoder"
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# stop script on error
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$ErrorActionPreference = "Stop"
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# define a list of substrings to search for
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$substrings_v2 = "stable-diffusion-2-1-base", "stable-diffusion-2-base"
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# check if $v2 and $v_model are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
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if ($v2 -eq $null -and $v_model -eq $null -and ($substrings_v2 | Where-Object { $pretrained_model_name_or_path -match $_ }).Count -gt 0) {
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Write-Host("SD v2 model detected. Setting --v2 parameter")
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$v2 = "--v2"
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$v_model = $null
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}
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# define a list of substrings to search for v-objective
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$substrings_v_model = "stable-diffusion-2-1", "stable-diffusion-2"
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# check if $v2 and $v_model are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_model list
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elseif ($v2 -eq $null -and $v_model -eq $null -and ($substrings_v_model | Where-Object { $pretrained_model_name_or_path -match $_ }).Count -gt 0) {
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Write-Host("SD v2 v_model detected. Setting --v2 parameter and --v_parameterization")
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$v2 = "--v2"
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$v_model = "--v_parameterization"
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}
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# activate venv
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cd $kohya_finetune_repo_path
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.\venv\Scripts\activate
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# create caption json file
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if (!(Test-Path -Path $train_dir)) {
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New-Item -Path $train_dir -ItemType "directory"
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}
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python $kohya_finetune_repo_path\script\merge_captions_to_metadata.py `
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--caption_extention ".txt" $image_folder $train_dir"\meta_cap.json"
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# create images buckets
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python $kohya_finetune_repo_path\script\prepare_buckets_latents.py `
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$image_folder `
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$train_dir"\meta_cap.json" `
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$train_dir"\meta_lat.json" `
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$pretrained_model_name_or_path `
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--batch_size 4 --max_resolution $max_resolution --mixed_precision $mixed_precision
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# Get number of valid images
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$image_num = Get-ChildItem "$image_folder" -Recurse -File -Include *.npz | Measure-Object | % { $_.Count }
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$repeats = $image_num * $dataset_repeats
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Write-Host("Repeats = $repeats")
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# calculate max_train_set
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$max_train_set = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
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Write-Host("max_train_set = $max_train_set")
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$lr_warmup_steps = [Math]::Round($lr_warmup * $max_train_set / 100)
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Write-Host("lr_warmup_steps = $lr_warmup_steps")
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Write-Host("$v2 $v_model")
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accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process $kohya_finetune_repo_path\script\fine_tune.py `
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$v2 `
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$v_model `
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--pretrained_model_name_or_path=$pretrained_model_name_or_path `
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--in_json $train_dir\meta_lat.json `
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--train_data_dir="$image_folder" `
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--output_dir=$output_dir `
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--train_batch_size=$train_batch_size `
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--dataset_repeats=$dataset_repeats `
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--learning_rate=$learning_rate `
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--lr_scheduler=$lr_scheduler `
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--lr_warmup_steps=$lr_warmup_steps `
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--max_train_steps=$max_train_set `
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--use_8bit_adam `
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--xformers `
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--mixed_precision=$mixed_precision `
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--save_every_n_epochs=$save_every_n_epochs `
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--seed=$seed `
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$train_text_encoder `
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--save_precision=$save_precision
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# check if $output_dir\last is a directory... therefore it is a diffuser model
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if (Test-Path "$output_dir\last" -PathType Container) {
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if ($convert_to_ckpt) {
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Write-Host("Converting diffuser model $output_dir\last to $output_dir\last.ckpt")
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python "$kohya_finetune_repo_path\tools\convert_diffusers20_original_sd.py" `
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$output_dir\last `
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$output_dir\last.ckpt `
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--$save_precision
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}
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if ($convert_to_safetensors) {
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Write-Host("Converting diffuser model $output_dir\last to $output_dir\last.safetensors")
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python "$kohya_finetune_repo_path\tools\convert_diffusers20_original_sd.py" `
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$output_dir\last `
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$output_dir\last.safetensors `
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--$save_precision
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}
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}
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# define a list of substrings to search for inference file
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$substrings_sd_model = ".ckpt", ".safetensors"
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$matching_extension = foreach ($ext in $substrings_sd_model) {
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Get-ChildItem $output_dir -File | Where-Object { $_.Extension -contains $ext }
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}
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if ($matching_extension.Count -gt 0) {
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# copy the file named "v2-inference.yaml" from the "v2_inference" folder to $output_dir as last.yaml
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if ( $v2 -ne $null -and $v_model -ne $null) {
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Write-Host("Saving v2-inference-v.yaml as $output_dir\last.yaml")
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Copy-Item -Path "$kohya_finetune_repo_path\v2_inference\v2-inference-v.yaml" -Destination "$output_dir\last.yaml"
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}
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elseif ( $v2 -ne $null ) {
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Write-Host("Saving v2-inference.yaml as $output_dir\last.yaml")
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Copy-Item -Path "$kohya_finetune_repo_path\v2_inference\v2-inference.yaml" -Destination "$output_dir\last.yaml"
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}
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} |