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furry/juchuan/juchuan_locon/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
+is_v2_model=0                             # SD2.0 model | SD2.0模型 2.0模型下 clip_skip 默认无效
+parameterization=0                        # parameterization | 参数化 本参数需要和 V2 参数同步使用 实验性功能
+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="768,768"  # 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
+stop_text_encoder_training=0 # stop text encoder training | 在第N步时停止训练文本编码器
+
+noise_offset="0.05"  # 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。
+min_snr_gamma=0 # minimum signal-to-noise ratio (SNR) value for gamma-ray | 伽马射线事件的最小信噪比（SNR）值  默认为 0
+
+# 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", "adafactor"
+lr_warmup_steps=0                   # warmup steps | 学习率预热步数，lr_scheduler 为 constant 或 adafactor 时该值需要设为0。
+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="juchuan"           # 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
+
+# Resume training state | 恢复训练设置
+save_state=0 # save state | 保存训练状态 名称类似于 <output_name>-??????-state ?????? 表示 epoch 数
+resume=""    # resume from state | 从某个状态文件夹中恢复训练 需配合上方参数同时使用 由于规范文件限制 epoch 数和全局步数不会保存 即使恢复时它们也从 1 开始 与 network_weights 的具体实现操作并不一致
+
+# 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
+optimizer_type="AdamW8bit" # Optimizer type | 优化器类型 默认为 AdamW8bit，可选：AdamW AdamW8bit Lion SGDNesterov SGDNesterov8bit DAdaptation AdaFactor
+
+# LyCORIS 训练设置
+algo="lora"  # LyCORIS network algo | LyCORIS 网络算法 可选 lora、loha、lokr、ia3、dylora。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
+dropout="0"  # dropout | dropout 概率, 0 为不使用 dropout, 越大则 dropout 越多，推荐 0~0.5， LoHa/LoKr/(IA)^3暂时不支持
+
+# 远程记录设置
+use_wandb=0 # use_wandb | 启用wandb远程记录功能
+wandb_api_key="" # wandb_api_key | API,通过https://wandb.ai/authorize获取
+log_tracker_name="" # log_tracker_name | wandb项目名称,留空则为"network_train"
+
+# ============= 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=()
+launchArgs=()
+if [[ $multi_gpu == 1 ]]; then launchArgs+=("--multi_gpu"); fi
+
+if [[ $is_v2_model == 1 ]]; then
+  extArgs+=("--v2");
+else
+  extArgs+=("--clip_skip $clip_skip");
+fi
+
+if [[ $parameterization == 1 ]]; then extArgs+=("--v_parameterization"); fi
+
+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 [[ $optimizer_type ]]; then extArgs+=("--optimizer_type $optimizer_type"); fi
+
+if [[ $optimizer_type == "DAdaptation" ]]; then extArgs+=("--optimizer_args decouple=True"); fi
+
+if [[ $save_state == 1 ]]; then extArgs+=("--save_state"); fi
+
+if [[ $resume ]]; then extArgs+=("--resume $resume"); 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 dropout=$dropout")
+fi
+
+if [[ $stop_text_encoder_training -ne 0 ]]; then extArgs+=("--stop_text_encoder_training $stop_text_encoder_training"); fi
+
+if [[ $noise_offset != "0" ]]; then extArgs+=("--noise_offset $noise_offset"); fi
+
+if [[ $min_snr_gamma -ne 0 ]]; then extArgs+=("--min_snr_gamma $min_snr_gamma"); fi
+
+if [[ $use_wandb == 1 ]]; then 
+  extArgs+=("--log_with=all")
+else
+  extArgs+=("--log_with=tensorboard")
+fi
+
+if [[ $wandb_api_key ]]; then extArgs+=("--wandb_api_key $wandb_api_key"); fi
+
+if [[ $log_tracker_name ]]; then extArgs+=("--log_tracker_name $log_tracker_name"); fi
+
+accelerate launch ${launchArgs[@]} --num_cpu_threads_per_process=8 "./sd-scripts/train_network.py" \
+  --enable_bucket \
+  --pretrained_model_name_or_path=$pretrained_model \
+  --train_data_dir=$train_data_dir \
+  --output_dir="/content/drive/MyDrive/Lora/output/juchuan" \
+  --logging_dir="/content/drive/MyDrive/Lora/output/juchuan/logs" \
+  --log_prefix=$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=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[@]}