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furry/bailin/bailin_loha/train.sh

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+#!/bin/bash
+# LoRA train script by @Akegarasu
+
+# Train data path | 设置训练用模型、图片
+pretrained_model="/content/lora-scripts/sd-models/Animefull-final-pruned.ckpt" # base model path | 底模路径
+train_data_dir="/content/lora-scripts/train/aki/"              # train dataset path | 训练数据集路径
+reg_data_dir=""                           # directory for regularization images | 正则化数据集路径，默认不使用正则化图像。
+
+# Network settings | 网络设置
+network_module="lycoris.kohya" # 在这里将会设置训练的网络种类，默认为 networks.lora 也就是 LoRA 训练。如果你想训练 LyCORIS（LoCon、LoHa） 等，则修改这个值为 lycoris.kohya
+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。
+network_dim=32                 # network dim | 常用 4~128，不是越大越好
+network_alpha=16               # 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。
+
+# Train related params | 训练相关参数
+resolution="512,768"  # image resolution w,h. 图片分辨率，宽,高。支持非正方形，但必须是 64 倍数。
+batch_size=3          # batch size
+max_train_epoches=15  # max train epoches | 最大训练 epoch
+save_every_n_epochs=1 # save every n epochs | 每 N 个 epoch 保存一次
+
+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
+train_text_encoder_only=0 # train Text Encoder only | 仅训练 文本编码器
+
+noise_offset=0 # noise offset | 在训练中添加噪声偏移来改良生成非常暗或者非常亮的图像，如果启用，推荐参数为0.1
+keep_tokens=1  # keep heading N tokens when shuffling caption tokens | 在随机打乱 tokens 时，保留前 N 个不变。
+
+# Learning rate | 学习率
+lr="1.5e-4"
+unet_lr="1.5e-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 | 仅在 lr_scheduler 为 constant_with_warmup 时需要填写这个值
+lr_restart_cycles=1                 # cosine_with_restarts restart cycles | 余弦退火重启次数，仅在 lr_scheduler 为 cosine_with_restarts 时起效。
+
+# Output settings | 输出设置
+output_name="bailin_loha"           # output model name | 模型保存名称
+save_model_as="safetensors" # model save ext | 模型保存格式 ckpt, pt, safetensors
+
+# 其他设置
+min_bucket_reso=256              # arb min resolution | arb 最小分辨率
+max_bucket_reso=1024             # arb max resolution | arb 最大分辨率
+persistent_data_loader_workers=0 # persistent dataloader workers | 容易爆内存，保留加载训练集的worker，减少每个 epoch 之间的停顿
+clip_skip=2                      # clip skip | 玄学 一般用 2
+
+# 优化器设置
+use_8bit_adam=0 # use 8bit adam optimizer | 使用 8bit adam 优化器节省显存，默认启用。部分 10 系老显卡无法使用，修改为 0 禁用。
+use_lion=1      # use lion optimizer | 使用 Lion 优化器
+
+# LyCORIS 训练设置
+algo="loha"  # LyCORIS network algo | LyCORIS 网络算法 可选 lora、loha。lora即为locon
+conv_dim=8   # conv dim | 类似于 network_dim，推荐为 4
+conv_alpha=4 # conv alpha | 类似于 network_alpha，可以采用与 conv_dim 一致或者更小的值
+
+# ============= DO NOT MODIFY CONTENTS BELOW | 请勿修改下方内容 =====================
+export HF_HOME="huggingface"
+export TF_CPP_MIN_LOG_LEVEL=3
+
+extArgs=()
+
+if [ $train_unet_only == 1 ]; then extArgs+=("--network_train_unet_only"); fi
+
+if [ $train_text_encoder_only == 1 ]; then extArgs+=("--network_train_text_encoder_only"); fi
+
+if [ $network_weights ]; then extArgs+=("--network_weights $network_weights"); fi
+
+if [ $reg_data_dir ]; then extArgs+=("--reg_data_dir $reg_data_dir"); fi
+
+if [ $use_8bit_adam == 1 ]; then extArgs+=("--use_8bit_adam"); fi
+
+if [ $use_lion == 1 ]; then extArgs+=("--use_lion_optimizer"); fi
+
+if [ $persistent_data_loader_workers == 1 ]; then extArgs+=("--persistent_data_loader_workers"); fi
+
+if [ $network_module == "lycoris.kohya" ]; then
+  extArgs+=("--network_args conv_dim=$conv_dim conv_alpha=$conv_alpha algo=$algo")
+fi
+
+if [ $noise_offset ]; then extArgs+=("--noise_offset $noise_offset"); fi
+
+accelerate launch --num_cpu_threads_per_process=8 "./sd-scripts/train_network.py" \
+  --enable_bucket \
+  --pretrained_model_name_or_path=$pretrained_model \
+  --train_data_dir=$train_data_dir \
+  --output_dir="/content/drive/MyDrive/Lora/output/bailin_loha" \
+  --logging_dir="/content/drive/MyDrive/Lora/output/bailin_loha/logs" \
+  --log_prefix=$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 \
+  --clip_skip=$clip_skip \
+  --prior_loss_weight=0.3 \
+  --max_token_length=225 \
+  --caption_extension=".txt" \
+  --save_model_as=$save_model_as \
+  --min_bucket_reso=$min_bucket_reso \
+  --max_bucket_reso=$max_bucket_reso \
+  --keep_tokens=$keep_tokens \
+  --xformers --shuffle_caption ${extArgs[@]}