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# LoRA train script by @Akegarasu
# Train data path | ����ѵ����ģ�͡�ͼƬ�͡�ͼƬ
$pretrained_model = "./sd-models/model.ckpt" # base model path | ������
$is_v2_model = 0 # SD2.0 model | SD2.0ģ��� 2.ģ������� clip_skĬ����Ч����Ч
$parameterization = 0 # parameterization | ���������������Ҫ����������ͬ��ʹ��2ʵ���Թ���ͬ��ʹ�� ʵ���Թ���
$train_data_dir = "./train/aki" # train dataset path | ѵ�����ݼ�·���·��
$reg_data_dir = "" # directory for regularization images | �������ݼ�·����Ĭ�ϲ�ʹ������ͼ�������ͼ��
# Network settings | ������������
$network_module = "networks.lora" # �����ォ������ѵ�����������࣬Ĭ��Ϊ������࣬Ĭ��Ϊ netҲ����ks.lorѵ�����������ѵ��oRA ѵ�������������ѵ����L�ȣ��������ֵΪoCon��LoHa�� �ȣ��������ֵΪ lycoris.kohya
$network_weights = "" # pretrained weights for LoRA network | ����Ҫ�����е����е� ģ���ϼ���ѵ��������д��ѵ���ģ��·����д LoRA ģ��·����
$network_dim = 32 # network dim | ������ 4~1������Խ��Խ���Խ��Խ��
$network_alpha = 32 # network alpha | ��������� network_d��ͬ��ֵ���߲��ý�С��ֵ�����ý�С��ֵ���� ��һ��w��ֹ���硣Ĭ��ֵΪһ���ʹ�ý�С����硣Ĭ��ֵ��Ҫ����ѧϰ�ʡ��С�� alpha ��Ҫ����ѧϰ�ʡ�
# Train related params | ѵ����ز������
$resolution = "512,512" # image resolution w,h. ͼƬ�ֱ��ʣ�����ߡ�֧�ַ������Σ�����������Σ������������ 64 ������
$batch_size = 1 # batch size
$max_train_epoches = 10 # max train epoches | ���ѵ���� epoch
$save_every_n_epochs = 2 # save every n epochs | ÿ N ��� epoch����һ���һ��
$train_unet_only = 0 # train U-Net only | ��ѵ���� U-N���������������Ч����������Դ�ʹ�á����Դ���Կ����Դ�ʹ�á�6G�Դ���Կ���
$train_text_encoder_only = 0 # train Text Encoder only | ��ѵ����ı�������������
$stop_text_encoder_training = 0 # stop text encoder training | �ڵ����ʱֹͣѵ���ı��������������
$noise_offset = 0 # noise offset | ��ѵ�����������ƫ�����������ɷdz������߷dz�����ͼ��������ã��Ƽ�����Ϊ����ͼ��������ã��Ƽ�����Ϊ 0.1
$keep_tokens = 0 # keep heading N tokens when shuffling caption tokens | ������������� tokʱ������ǰ��������䡣 N �����䡣
$min_snr_gamma = 0 # minimum signal-to-noise ratio (SNR) value for gamma-ray | ٤�������¼�����С����ȣ��С���ֵ�ȣĬ��ΪR��ֵ Ĭ��Ϊ 0
# Learning rate | ѧϰ���
$lr = "1e-4"
$unet_lr = "1e-4"
$text_encoder_lr = "1e-5"
$lr_scheduler = "cosine_with_restarts" # "linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"
$lr_warmup_steps = 0 # warmup steps | ѧϰ��Ԥ�Ȳ���������lr_scheduΪer Ϊ const��nt �� adafaʱ��ֵ��Ҫ��Ϊ�����Ҫ��Ϊ0��
$lr_restart_cycles = 1 # cosine_with_restarts restart cycles | �����˻����������������������� lr_sΪheduler Ϊ cosine_with_ʱ��Ч��arts ʱ��Ч��
# Output settings | ����������
$output_name = "aki" # output model name | ģ�ͱ�����������
$save_model_as = "safetensors" # model save ext | ģ�ͱ����ʽ�ʽ ckpt, pt, safetensors
# Resume training state | �ָ�ѵ����������
$save_state = 0 # save training state | ����ѵ��״̬������������������� <output_name>-??????-state��ʾ????? ���ʾ epoch ��
$resume = "" # resume from state | ��ij��״̬�ļ����лָ�ѵ��л������Ϸ�����ͬʱʹ������ڹ淶�ļ�����ʹ�� �������ȫ�ֲ������ᱣ��e��ʹ�ָ�ʱ����Ҳ��ȫ�ֲ��ʼ�����ᱣ�� ��ʹ�ָ�ʱ�����ľ���ʵ�ֲ�������һ��� network_weights �ľ���ʵ�ֲ�������һ��
# ������������
$min_bucket_reso = 256 # arb min resolution | arb ��С�ֱ������
$max_bucket_reso = 1024 # arb max resolution | arb ���ֱ�����
$persistent_data_loader_workers = 0 # persistent dataloader workers | ���ױ��ڴ棬�������ѵ�������ѵ����������ÿ��ker����֮���ͣ�� epoch ֮���ͣ��
$clip_skip = 2 # clip skip | ��ѧѧһ������� 2
$multi_gpu = 0 # multi gpu | ���Կ�ѵ��ѵ�ò����������Կ���������ʹ���� >= 2 ʹ��
$lowram = 0 # lowram mode | ���ڴ�ģʽģ��ģʽ�»Ὣ�»Ὣ U-n�ı������������ת�Ƶ�VAE ת�Դ��� ���ø�ģʽ���ܻ���Դ���һ��Ӱ��ʽ���ܻ���Դ���һ��Ӱ��
# ������������
$optimizer_type = "AdamW8bit" # Optimizer type | �Ż��������Ĭ��Ϊ Ĭ��Ϊ Adam����ѡ��t����ѡ��AdamW AdamW8bit Lion SGDNesterov SGDNesterov8bit DAdaptation AdaFactor
# LyCORIS ѵ���������
$algo = "lora" # LyCORIS network algo | LyCORIS �����㷨���ѡ��ѡ l��ra����oha���lok����ia3���dylo��Ϊ��lora��Ϊlocon
$conv_dim = 4 # conv dim | ��������� network_���Ƽ�Ϊ��Ƽ�Ϊ 4
$conv_alpha = 4 # conv alpha | ��������� network_al�����Բ�������Բ����� cһ�»��߸�С��ֵһ�»��߸�С��ֵ
$dropout = "0" # dropout | dropout ������, Ϊ��ʹ���ʹ�� dropoԽ���� Խ���� drԽ�࣬�Ƽ� Խ�࣬�Ƽ�� 0~0.5�� LoHa/LoK��ʱ��֧��)^3��ʱ��֧��
# Զ�̼�¼�������
$use_wandb = 0 # enable wandb logging | ������wanԶ�̼�¼����¼����
$wandb_api_key = "" # wandb api key | API,ͨ���https://wandb.ai/authoriz��ȡ�ȡ
$log_tracker_name = "" # wandb log tracker name | wandb��Ŀ�,��,�����Ϊ����Ϊ"network_train"
# ============= DO NOT MODIFY CONTENTS BELOW | �������·�����·����� =====================
# Activate python venv
.\venv\Scripts\activate
$Env:HF_HOME = "huggingface"
$Env:XFORMERS_FORCE_DISABLE_TRITON = "1"
$ext_args = [System.Collections.ArrayList]::new()
$launch_args = [System.Collections.ArrayList]::new()
if ($multi_gpu) {
[void]$launch_args.Add("--multi_gpu")
}
if ($lowram) {
[void]$ext_args.Add("--lowram")
}
if ($is_v2_model) {
[void]$ext_args.Add("--v2")
}
else {
[void]$ext_args.Add("--clip_skip=$clip_skip")
}
if ($parameterization) {
[void]$ext_args.Add("--v_parameterization")
}
if ($train_unet_only) {
[void]$ext_args.Add("--network_train_unet_only")
}
if ($train_text_encoder_only) {
[void]$ext_args.Add("--network_train_text_encoder_only")
}
if ($network_weights) {
[void]$ext_args.Add("--network_weights=" + $network_weights)
}
if ($reg_data_dir) {
[void]$ext_args.Add("--reg_data_dir=" + $reg_data_dir)
}
if ($optimizer_type) {
[void]$ext_args.Add("--optimizer_type=" + $optimizer_type)
}
if ($optimizer_type -eq "DAdaptation") {
[void]$ext_args.Add("--optimizer_args")
[void]$ext_args.Add("decouple=True")
}
if ($network_module -eq "lycoris.kohya") {
[void]$ext_args.Add("--network_args")
[void]$ext_args.Add("conv_dim=$conv_dim")
[void]$ext_args.Add("conv_alpha=$conv_alpha")
[void]$ext_args.Add("algo=$algo")
[void]$ext_args.Add("dropout=$dropout")
}
if ($noise_offset -ne 0) {
[void]$ext_args.Add("--noise_offset=$noise_offset")
}
if ($stop_text_encoder_training -ne 0) {
[void]$ext_args.Add("--stop_text_encoder_training=$stop_text_encoder_training")
}
if ($save_state -eq 1) {
[void]$ext_args.Add("--save_state")
}
if ($resume) {
[void]$ext_args.Add("--resume=" + $resume)
}
if ($min_snr_gamma -ne 0) {
[void]$ext_args.Add("--min_snr_gamma=$min_snr_gamma")
}
if ($persistent_data_loader_workers) {
[void]$ext_args.Add("--persistent_data_loader_workers")
}
if ($use_wandb -eq 1) {
[void]$ext_args.Add("--log_with=all")
if ($wandb_api_key) {
[void]$ext_args.Add("--wandb_api_key=" + $wandb_api_key)
}
if ($log_tracker_name) {
[void]$ext_args.Add("--log_tracker_name=" + $log_tracker_name)
}
}
else {
[void]$ext_args.Add("--log_with=tensorboard")
}
# run train
accelerate launch $launch_args --num_cpu_threads_per_process=8 "./sd-scripts/train_network.py" `
--enable_bucket `
--pretrained_model_name_or_path=$pretrained_model `
--train_data_dir=$train_data_dir `
--output_dir="./output" `
--logging_dir="./logs" `
--log_prefix=$output_name `
--resolution=$resolution `
--network_module=$network_module `
--max_train_epochs=$max_train_epoches `
--learning_rate=$lr `
--unet_lr=$unet_lr `
--text_encoder_lr=$text_encoder_lr `
--lr_scheduler=$lr_scheduler `
--lr_warmup_steps=$lr_warmup_steps `
--lr_scheduler_num_cycles=$lr_restart_cycles `
--network_dim=$network_dim `
--network_alpha=$network_alpha `
--output_name=$output_name `
--train_batch_size=$batch_size `
--save_every_n_epochs=$save_every_n_epochs `
--mixed_precision="fp16" `
--save_precision="fp16" `
--seed="1337" `
--cache_latents `
--prior_loss_weight=1 `
--max_token_length=225 `
--caption_extension=".txt" `
--save_model_as=$save_model_as `
--min_bucket_reso=$min_bucket_reso `
--max_bucket_reso=$max_bucket_reso `
--keep_tokens=$keep_tokens `
--xformers --shuffle_caption $ext_args
Write-Output "Train finished"
Read-Host | Out-Null ;