90 lines
3.3 KiB
PowerShell
90 lines
3.3 KiB
PowerShell
# Sylvia Ritter. AKA: by silvery trait
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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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$training_folder = "all-images-v3"
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$learning_rate = 5e-6
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$dataset_repeats = 40
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$train_batch_size = 6
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$epoch = 4
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$save_every_n_epochs=1
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$mixed_precision="bf16"
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$num_cpu_threads_per_process=6
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$max_resolution = "768,576"
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# You should not have to change values past this point
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# stop script on error
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$ErrorActionPreference = "Stop"
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# activate venv
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.\venv\Scripts\activate
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# create caption json file
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python D:\kohya_ss\finetune\merge_captions_to_metadata.py `
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--caption_extention ".txt" $train_dir"\"$training_folder $train_dir"\meta_cap.json"
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# create images buckets
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python D:\kohya_ss\finetune\prepare_buckets_latents.py `
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$train_dir"\"$training_folder `
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$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 $max_resolution --mixed_precision fp16
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# Get number of valid images
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$image_num = Get-ChildItem "$train_dir\$training_folder" -Recurse -File -Include *.npz | Measure-Object | %{$_.Count}
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$repeats = $image_num * $dataset_repeats
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# calculate max_train_set
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$max_train_set = [Math]::Ceiling($repeats / $train_batch_size * $epoch)
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accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process D:\kohya_ss\finetune\fine_tune.py `
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--pretrained_model_name_or_path=$pretrained_model_name_or_path `
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--in_json $train_dir"\meta_lat.json" `
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--train_data_dir=$train_dir"\"$training_folder `
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--output_dir=$train_dir"\fine_tuned2" `
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--train_batch_size=$train_batch_size `
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--dataset_repeats=$dataset_repeats `
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--learning_rate=$learning_rate `
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--max_train_steps=$max_train_set `
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--use_8bit_adam --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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--train_text_encoder `
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--save_precision="fp16"
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accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process D:\kohya_ss\finetune\fine_tune.py `
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--pretrained_model_name_or_path=$train_dir"\fine_tuned\last.ckpt" `
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--in_json $train_dir"\meta_lat.json" `
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--train_data_dir=$train_dir"\"$training_folder `
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--output_dir=$train_dir"\fine_tuned2" `
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--train_batch_size=$train_batch_size `
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--dataset_repeats=$([Math]::Ceiling($dataset_repeats / 2)) `
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--learning_rate=$learning_rate `
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--max_train_steps=$([Math]::Ceiling($max_train_set / 2)) `
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--use_8bit_adam --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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--save_precision="fp16"
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# Hypernetwork
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accelerate launch --num_cpu_threads_per_process $num_cpu_threads_per_process D:\kohya_ss\finetune\fine_tune.py `
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--pretrained_model_name_or_path=$pretrained_model_name_or_path `
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--in_json $train_dir"\meta_lat.json" `
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--train_data_dir=$train_dir"\"$training_folder `
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--output_dir=$train_dir"\fine_tuned" `
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--train_batch_size=$train_batch_size `
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--dataset_repeats=$dataset_repeats `
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--learning_rate=$learning_rate `
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--max_train_steps=$max_train_set `
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--use_8bit_adam --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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--save_precision="fp16" `
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--hypernetwork_module="hypernetwork_nai" |