88 lines
3.5 KiB
PowerShell
88 lines
3.5 KiB
PowerShell
# LoRA train script by @Akegarasu
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# Train data path | 设置训练用模型、图片
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$pretrained_model = "./sd-models/model.ckpt" # base model path | 底模路径
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$train_data_dir = "./train/aki" # train dataset path | 训练数据集路径
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# Train related params | 训练相关参数
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$resolution = "512,512" # image resolution w,h. 图片分辨率，宽,高。支持非正方形，但必须是 64 倍数。
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$batch_size = 1 # batch size
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$max_train_epoches = 10 # max train epoches | 最大训练 epoch
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$save_every_n_epochs = 2 # save every n epochs | 每 N 个 epoch 保存一次
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$network_dim = 32 # network dim | 常用 4~128，不是越大越好
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$network_alpha = 32 # network alpha | 常用与 network_dim 相同的值或者采用较小的值，如 network_dim的一半 防止下溢。默认值为 1，使用较小的 alpha 需要提升学习率。
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$clip_skip = 2 # clip skip | 玄学 一般用 2
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$train_unet_only = 0 # train U-Net only | 仅训练 U-Net，开启这个会牺牲效果大幅减少显存使用。6G显存可以开启
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$train_text_encoder_only = 0 # train Text Encoder only | 仅训练 文本编码器
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# Learning rate | 学习率
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$lr = "1e-4"
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$unet_lr = "1e-4"
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$text_encoder_lr = "1e-5"
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$lr_scheduler = "cosine_with_restarts" # "linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"
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$lr_warmup_steps = 0 # warmup steps | 仅在 lr_scheduler 为 constant_with_warmup 时需要填写这个值
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# Output settings | 输出设置
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$output_name = "aki" # output model name | 模型保存名称
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$save_model_as = "safetensors" # model save ext | 模型保存格式 ckpt, pt, safetensors
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# 其他设置
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$network_weights = "" # pretrained weights for LoRA network | 若需要从已有的 LoRA 模型上继续训练，请填写 LoRA 模型路径。
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$min_bucket_reso = 256 # arb min resolution | arb 最小分辨率
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$max_bucket_reso = 1024 # arb max resolution | arb 最大分辨率
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# ============= DO NOT MODIFY CONTENTS BELOW | 请勿修改下方内容 =====================
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# Activate python venv
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.\venv\Scripts\activate
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$Env:HF_HOME = "huggingface"
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$ext_args = [System.Collections.ArrayList]::new()
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if ($train_unet_only) {
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[void]$ext_args.Add("--network_train_unet_only")
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}
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if ($train_text_encoder_only) {
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[void]$ext_args.Add("--network_train_text_encoder_only")
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}
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if ($network_weights) {
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[void]$ext_args.Add("--network_weights=" + $network_weights)
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}
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# run train
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accelerate launch --num_cpu_threads_per_process=8 "./sd-scripts/train_network.py" `
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--enable_bucket `
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--pretrained_model_name_or_path=$pretrained_model `
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--train_data_dir=$train_data_dir `
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--output_dir="./output" `
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--logging_dir="./logs" `
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--resolution=$resolution `
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--network_module=networks.lora `
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--max_train_epochs=$max_train_epoches `
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--learning_rate=$lr `
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--unet_lr=$unet_lr `
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--text_encoder_lr=$text_encoder_lr `
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--lr_scheduler=$lr_scheduler `
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--lr_warmup_steps=$lr_warmup_steps `
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--network_dim=$network_dim `
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--network_alpha=$network_alpha `
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--output_name=$output_name `
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--train_batch_size=$batch_size `
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--save_every_n_epochs=$save_every_n_epochs `
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--mixed_precision="fp16" `
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--save_precision="fp16" `
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--seed="1337" `
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--cache_latents `
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--clip_skip=$clip_skip `
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--prior_loss_weight=1 `
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--max_token_length=225 `
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--caption_extension=".txt" `
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--save_model_as=$save_model_as `
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--min_bucket_reso=$min_bucket_reso `
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--max_bucket_reso=$max_bucket_reso `
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--xformers --shuffle_caption --use_8bit_adam $ext_args
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Write-Output "Train finished"
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Read-Host | Out-Null ; |