pax_global_header 0000666 0000000 0000000 00000000064 14564651534 0014527 g ustar 00root root 0000000 0000000 52 comment=3a4fe51e4ceeb638cf6d9e6aea0be3c03d87a384
stable-diffusion-trainer/ 0000775 0000000 0000000 00000000000 14564651534 0015773 5 ustar 00root root 0000000 0000000 stable-diffusion-trainer/LICENSE 0000664 0000000 0000000 00000103333 14564651534 0017003 0 ustar 00root root 0000000 0000000 GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc.
Everyone is permitted to copy and distribute verbatim copies
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END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
Copyright (C)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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You should have received a copy of the GNU Affero General Public License
along with this program. If not, see .
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer
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get its source. For example, if your program is a web application, its
interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
.
stable-diffusion-trainer/readme.md 0000664 0000000 0000000 00000011375 14564651534 0017561 0 ustar 00root root 0000000 0000000 # 这个仓库用于介绍如何使用青龙脚本进行训练
## 使用前必读
- 该项目仅供进行ai调试学习，请勿用于任何违法用途
- 请自行解决数据集授权问题，禁止使用非授权数据集进行训练！
- 任何由于使用非授权数据集进行训练造成的问题，需使用者自行承担全部责任和后果！与仓库、仓库维护者、*Stability* AI、 Kohya S.、烈风、秋葉、青龙圣者、本镜像制作者无关！
- [镜像所用仓库](https://gitee.com/taichuzhishi/lora-scripts)
- [GitHub - kohya-ss/sd-scripts](https://github.com/kohya-ss/sd-scripts)
- [青龙圣者的个人空间](https://space.bilibili.com/219296?spm_id_from=333.337.0.0)
## 镜像介绍
基于青龙圣者训练脚本制作的镜像，可以用于进行sd1.5和sdxl的lora训练或者db训练。
本软件为傻瓜式脚本，关闭傻瓜式功能后会快一两分钟，但是我没有设置关闭的选项
PS：本人不是专业的，也没有debug的能力，做出来的东西有问题很正常，请多多包涵
## 如何使用
### 如何创建实例
fork本仓库，然后再云脑中选择新建训练任务。（fork按钮在右上角，中文是派生）
然后选择英伟达GPU，并在参数设置-镜像中选择下面的镜像
智算集群：
```
192.168.242.22:443/default-workspace/2a72307689ae49758c80c896fffda0a1/image:train_fin
```
### 上传训练集
直接在顶部上传数据集压缩包，格式为zip，暂不支持正则，正常上传完了之后就不需要再训练脚本额外设置路径，命名规则为：1.要么你的压缩包名字为“数字_概念”，例如10_1girl，要么你的压缩包内存在文件名为“数字_概念”的文件夹例如10_1girl
### 上传底膜
模型顶部上传（PS：上传模型默认开源）
### 开启训练任务
在模型中选择底模，在训练集中选择训练集
设置训练程序为train.py
## old version
### 如何创建实例
想要在云脑的创建实例需要创建一个仓库，也可以选择fork本仓库，然后再云脑中选择新建调试任务。（fork按钮在右上角，中文是派生）
然后选择英伟达GPU，并在参数设置-镜像中选择下面的镜像
镜像创建后有四小时的时间，四小时后镜像就会关闭，只保留code目录下的文件(你能在左边栏看到的所有文件)！！！！！！！！！！！！！！
#### 须知
如果你在晚上6点-9点创建实例的时候一直waiting请不要紧张，那是因为使用的人太多了，需要排队，尤其是在周末
如果你实在担心可以点任务名称的蓝字，进入后可以查看镜像的状态
#### 镜像链接
智算集群：
```
192.168.242.22:443/default-workspace/fccb038c23234b9e80105d4ccd152117/image:final_scripts
```
### 如何开始训练
- 打开终端（terminal）输入su，自动激活conda
- 移动训练脚本
```
mv /root/lora-scripts /code
```
- 上传底模和训练集
- 打开训练脚本（SDXL）
```
cd /code/lora-scripts && bash train_sdxllora.sh
```
### 关于数据里及底模
底模和训练集可以提前上传，创建调试任务的时候添加然后从对应的文件夹调出来就行
ps：上传模型需要在本地计算md5码，如果你的CPU过于拉跨，可能会卡住一段时间
底模建议放到/code/lora-scripts/sd-models
```
cd /code/lora-scripts/sd-models
```
训练集建议放到/code/lora-scripts/train/aki
```
cd /code/lora-scripts/train/aki
```
下载数据集以及底模可以使用阿里云盘命令行版（具体使用方法在下面）
### 关于训练脚本
由于使用的仓库的训练脚本各种时期的都有，建议在train_sdxllora.sh中修改参数
### 关于训练界面(GUI)
未测试，想使用的可以试一下这个命令
```
bash /code/lora-scripts/run_gui.sh
```
平台不直接对外提供端口，想用webui可以看一下这个：https://openi.pcl.ac.cn/OpenIOSSG/Online-Inference_Example
### 关于训练日志
由于平台不直接对外提供端口，建议使用wandb，使用方法见：[【一边炼丹一边跑图】玩转wandb，最新优化器算法以及训练脚本更新](https://www.bilibili.com/video/BV1As4y1R7cu/?share_source=copy_web&vd_source=51018c0040396d1c3f6bd3270a4341e8)
tensorboard想要使用也需要开放端口，具体方法同训练界面(GUI)
### 关于阿里云命令行客户端使用方法
打开阿里云盘
```
/code/lora-scripts/aliyunpan/aliyunpan
```
具体用法可见，一般用到的只有登录命令的upload命令
[如何使用](https://gitcode.net/weixin_47065084/aliyunpan#%E5%A6%82%E4%BD%95%E4%BD%BF%E7%94%A8)
## 关于训练任务
不需要再用调试任务，一键搞定
handleDatasetModelStorageSchedule failed
## 更新
现在可以直接在页面编辑脚本了，可以节省时间 stable-diffusion-trainer/train.py 0000664 0000000 0000000 00000027233 14564651534 0017471 0 ustar 00root root 0000000 0000000 import subprocess
import time
import threading
import os
import shutil
import re
def check_model_files():
# 检索/tmp/pretrainmodel目录下的所有文件。
files = os.listdir("/tmp/pretrainmodel")
# 检查是否存在文件大于1g
for file in files:
file_path = os.path.join("/tmp/pretrainmodel", file)
file_size = os.path.getsize(file_path)
if file_size > 1024**3: # 1g = 1024**3 bytes
# 检查其后缀是否为safetensors
file_ext = os.path.splitext(file_path)[1]
if file_ext == ".safetensors":
# 复制其文件到原目录下并且命名为model.safetensors
new_file_path = os.path.join("/tmp/pretrainmodel", "model.safetensors")
shutil.copy(file_path, new_file_path)
print(f"已将 {file_path} 复制到 {new_file_path}")
return # 结束函数
else:
# 提示：你的模型文件不为safetensors格式，请手动设置 train_sdxl.sh 中的pretrained_model为你的模型
print(f"你的模型文件 {file_path} 不为safetensors格式，请手动设置 train_sdxl.sh 中的pretrained_model为你的模型")
return # 结束函数
# 若没有，则无需结束程序并且提示：未找到模型文件
print("未找到模型文件")
def check_gpu_usage():
# 使用nvidia-smi命令获取GPU占用率
gpu_usage = subprocess.check_output(
"nvidia-smi --query-gpu=utilization.gpu --format=csv,noheader,nounits",
shell=True
).decode('utf-8').strip()
return int(gpu_usage)
def monitor_gpu_usage(process, interval=600,wait_time=1200):
time.sleep(wait_time)
while True:
if process.poll() is not None:
# 子进程已结束
break
gpu_usage = check_gpu_usage()
print(f'GPU占用率: {gpu_usage}%')
if gpu_usage < 40:
process.kill()
print('GPU占用率低于40%，子进程已被终止。')
break
time.sleep(interval) # 等待指定的时间间隔
def run_command(command):
#process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
process = subprocess.Popen(command, shell=True, executable='/bin/bash', stdout=subprocess.PIPE, stderr=subprocess.STDOUT,pipesize=1048576)
# 创建并启动一个线程来监控GPU占用率
monitor_thread = threading.Thread(target=monitor_gpu_usage, args=(process,))
monitor_thread.daemon = True
monitor_thread.start()
# 使用communicate()方法读取输出
#stdout, stderr = process.communicate()
#print(f'Command: {command}')
#print(stdout.decode('utf-8'))
#print(stderr.decode('utf-8'))
while True:
# 从标准输出中读取一行
line = process.stdout.readline()
#print("steps: 25%|██▌ | 320/1280 [04:45<14:17, 1.12it/s, avr_loss=0.116]steps: 25%|██▌ | 320/1280 [04:45<14:17, 1.12it/s, avr_loss=0.116]epoch 5/20generating sample images at step / サンプル画像生成 ステップ: 320prompt: (masterpiece, best quality:1.2), 1girl, solo,negative_prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry,height: 1024width: 1024sample_steps: 24scale: 7.0sample_sampler: euler_aseed: 13370%| | 0/24 [00:00 2:
return True
else:
return False
# 定义一个函数，用于递归查询目录下的所有文件夹，寻找符合条件的文件夹，并将其移动到指定的目标目录
def find_and_move_folders(source_path, target_path):
# 获取源目录下的所有文件和子文件夹
files = os.listdir(source_path)
# 定义一个变量，用来记录是否查找到符合条件的文件夹
found = False
for file in files:
# 如果是子文件夹，而不是文件
if os.path.isdir(os.path.join(source_path, file)):
# 检测文件夹名是否符合条件
if check_folder_name(file) and check_folder_files(os.path.join(source_path, file)):
# 如果符合条件，将文件夹移动到目标目录
try:
shutil.move(os.path.join(source_path, file), target_path)
# 打印移动的信息
print(f"Moved {file} from {source_path} to {target_path}")
# 将变量设为True，表示查找到符合条件的文件夹
found = True
# 跳过当前循环，继续查找其他文件夹
continue
except shutil.Error as err:
# 打印错误信息
print(err)
# 跳过当前循环，继续查找其他文件夹
found = True
continue
else:
# 如果不符合条件，继续递归查询该文件夹下的子文件夹
# 接收递归返回的值，如果为True，表示查找到符合条件的文件夹
if find_and_move_folders(os.path.join(source_path, file), target_path):
# 将变量设为True，表示查找到符合条件的文件夹
found = True
# 返回变量，表示是否查找到符合条件的文件夹
return found
# 定义一个函数，用于检测文件名是否符合XXX_YYY.zip或者XXX_YYY.tar.gz的格式，其中XXX为任一数字，YY为任一字符串
def check_file_name(file_name):
# 使用正则表达式匹配文件名
pattern = r"^\d+_\w+\.(zip|tar\.gz)$"
match = re.match(pattern, file_name)
# 如果匹配成功，返回True，否则返回False
if match:
return True
else:
return False
# 定义一个函数，用于检测目录下的所有.zip或者.tar.gz文件，如果符合条件，创建相同名字的文件夹，如果不符合条件，创建一个文件叫10_1girl
def find_and_create_folders(source_path):
# 获取源目录下的所有文件和子文件夹
files = os.listdir(source_path)
for file in files:
# 如果是文件，而不是子文件夹
if os.path.isfile(os.path.join(source_path, file)):
# 检测文件名是否符合条件
if check_file_name(file):
# 如果符合条件，创建相同名字的文件夹
folder_name = file.split(".")[0]
os.mkdir(os.path.join(source_path, folder_name))
# 打印创建的信息
print(f"Created folder {folder_name} for file {file}")
return True
else:
# 如果不符合条件，创建一个文件叫10_1girl
with open(os.path.join(source_path, "10_1girl"), "w") as f:
f.write("This is a file created by Bing.")
# 打印创建的信息
print(f"Created file 10_1girl for file {file}")
return False
# 定义一个函数，用于检测目录下的所有文件夹，将拥有超过3个文件的文件夹的所有内容复制到指定的目标文件夹
def find_and_copy_folders(source_path, target_path):
# 获取源目录下的所有文件和子文件夹
files = os.listdir(source_path)
for file in files:
# 如果是子文件夹，而不是文件
if os.path.isdir(os.path.join(source_path, file)):
# 检测文件夹中是否有超过3个文件
if check_folder_files(os.path.join(source_path, file)):
# 如果有，将文件夹中的所有内容复制到目标文件夹
shutil.copytree(os.path.join(source_path, file), os.path.join(target_path, file))
# 打印复制的信息
print(f"Copied {file} from {source_path} to {target_path}")
# 定义一个主函数，用于执行程序的逻辑
def main():
# 定义源目录和目标目录
print(" 无yi'yisteps: 25%|██▌ | 320/1280 [04:45<14:17, 1.12it/s, avr_loss=0.116]steps: 25%|██▌ | 320/1280 [04:45<14:17, 1.12it/s, avr_loss=0.116]epoch 5/20generating sample images at step / サンプル画像生成 ステップ: 320prompt: (masterpiece, best quality:1.2), 1girl, solo,negative_prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts,signature, watermark, username, blurry,height: 1024width: 1024sample_steps: 24scale: 7.0sample_sampler: euler_aseed: 13370%| | 0/24 [00:00= 2 使用
# 优化器设置
#use_8bit_adam=1 # 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=0 # use lion optimizer | 使用 Lion 优化器
optimizer_type="Lion8bit" # "adaFactor","AdamW8bit","Lion","DAdaptation", 推荐新优化器Lion。推荐学习率unetlr=lr=6e-5,tenclr=7e-6
# 新增优化器"Lion8bit"(速度更快，内存消耗更少)、"DAdaptAdaGrad"、"DAdaptAdan"(北大最新算法，效果待测)、"DAdaptSGD"
# 新增优化器 Sophia(2倍速1.7倍显存)、Prodigy天才优化器，可自适应Dylora
d0="4e-7"
# lycoris 训练设置
enable_lycoris_train=0 # enable lycoris train | 启用 LoCon 训练 启用后 network_dim 和 network_alpha 应当选择较小的值，比如 2~16
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
algo="loha" # algo参数，制定训练lycoris模型种类，包括lora(locon)、loha、IA3以及lokr、dylora 。5个可选
dropout="0" #lycoris专用dropout
# dylora 训练设置
enable_dylora_train=0 # enable dylora train | 启用 LoCon 训练 启用后 network_dim 和 network_alpha 应当选择较小的值，比如 2~16
unit=4 #block size
# SDXL
min_timestep="0" #最小时序，默认值0
max_timestep="1000" #最大时序，默认值1000
bucket_reso_steps="32" #default 64,SDXL can use 32
cache_text_encoder_outputs=0 #开启缓存文本编码器，开启后减少显存使用。但是无法和shuffle共用
cache_text_encoder_outputs_to_disk=0 #开启缓存文本编码器到磁盘，开启后减少显存使用。但是无法和shuffle共用
#checkpoint train
no_token_padding=0 #不进行分词器填充
diffuser_xformers=0
train_text_encoder=1
# ============= DO NOT MODIFY CONTENTS BELOW | 请勿修改下方内容 =====================
export HF_HOME="huggingface"
export TF_CPP_MIN_LOG_LEVEL=3
network_module="networks.lora"
extArgs=()
launchArgs=()
train_script="train_network"
if [[ $multi_gpu == 1 ]]; then launchArgs+=("--multi_gpu"); fi
if [ $no_half_vae == 1 ]; then
extArgs+=("--no_half_vae")
mixed_precision="fp32"
full_bf16=0
full_fp16=0
fp8_base=0
fi
if [ $fp8_base -ne 0 ]; then
extArgs+=("--fp8_base")
full_fp16=0
full_bf16=0
fi
if [[ $model == *db ]]; then
if [[ $model == "db" ]]; then
train_script="train_db"
if [[ $no_token_padding -ne 0 ]]; then extArgs+=("--no_token_padding"); fi
else
train_script="train"
if [[ $diffuser_xformers -ne 0 ]]; then extArgs+=("--diffuser_xformers"); fi
if [[ $train_text_encoder -ne 0 ]]; then extArgs+=("--train_text_encoder"); fi
fi
network_module=""
network_dim=""
network_alpha=""
network_weights=""
enable_block_weights=0
enable_block_dim=0
enable_lycoris_train=0
enable_dylora_train=0
unet_lr=""
text_encoder_lr=""
train_unet_only=0
train_text_encoder_only=0
training_comment=""
prior_loss_weight="1"
network_dropout="0"
fi
if [[ $model == sdxl* ]]; then
train_script="sdxl_$train_script"
if [ $cache_text_encoder_outputs -ne 0 ]; then
extArgs+=("--cache_text_encoder_outputs")
enable_bucket=0
shuffle_caption=0
if [ $cache_text_encoder_outputs_to_disk -ne 0 ]; then
extArgs+=("--cache_text_encoder_outputs_to_disk")
fi
fi
if [ $full_bf16 -ne 0 ]; then
extArgs+=("--full_bf16")
mixed_precision="bf16"
full_fp16=0
fp8_base=0
fi
if [ $bucket_reso_steps != "64" ]; then
extArgs+=("--bucket_reso_steps=$bucket_reso_steps")
fi
if [ $min_timestep != "0" ]; then
extArgs+=("--min_timestep=$min_timestep")
fi
if [ $max_timestep != "1000" ]; then
extArgs+=("--max_timestep=$max_timestep")
fi
fi
if [ $network_module ]; then extArgs+=("--network_module=$network_module"); fi
if [ $network_dim ]; then
extArgs+=("--network_dim=$network_dim")
if [ $network_alpha ]; then
extArgs+=("--network_alpha=$network_alpha")
fi
fi
if [ $unet_lr ]; then extArgs+=("--unet_lr=$unet_lr"); fi
if [ $text_encoder_lr ]; then extArgs+=("--text_encoder_lr=$text_encoder_lr"); fi
if [ $prior_loss_weight != "1" ]; then extArgs+=("--prior_loss_weight=$prior_loss_weight"); fi
if [ $training_comment ]; then extArgs+=("--training_comment=$training_comment"); fi
if [ $save_state -ne 0 ]; then extArgs+=("--save_state"); fi
if [ $resume ]; then extArgs+=("--resume=$resume"); fi
if [ $reg_data_dir ]; then extArgs+=("--reg_data_dir=$reg_data_dir"); fi
if [ $train_unet_only -ne 0 ]; then
extArgs+=("--network_train_unet_only")
elif [ $train_text_encoder_only -ne 0 ]; 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 [ $shuffle_caption -ne 0 ]; then extArgs+=("--shuffle_caption"); fi
if [ $persistent_data_loader_workers -ne 0 ]; then extArgs+=("--persistent_data_loader_workers"); fi
if [ $weighted_captions -ne 0 ]; then extArgs+=("--weighted_captions"); fi
if [ $caption_dropout_rate != "0" ]; then extArgs+=("--caption_dropout_rate=$caption_dropout_rate"); fi
if [ $vae ]; then extArgs+=("--vae=$vae"); fi
if [ $cache_latents -ne 0 ]; then
extArgs+=("--cache_latents")
if [ $cache_latents_to_disk -ne 0 ]; then
extArgs+=("--cache_latents_to_disk")
fi
fi
if [ $full_fp16 -ne 0 ]; then
extArgs+=("--full_fp16")
mixed_precision="fp16"
fi
if [ $mixed_precision ]; then
extArgs+=("--mixed_precision=$mixed_precision")
fi
if [[ $network_dropout != "0" ]]; then
enable_lycoris=0
extArgs+=("--network_dropout=$network_dropout")
extArgs+=("--scale_weight_norms=$scale_weight_norms")
if [[ $enable_dylora != "0" && $model != db* ]]; then
extArgs+=("--network_args rank_dropout=$rank_dropout module_dropout=$module_dropout")
fi
fi
if [ $enable_lycoris_train == 1 ]; then
network_module="lycoris.kohya"
extArgs+=("--network_args conv_dim=$conv_dim conv_alpha=$conv_alpha algo=$algo dropout=$dropout")
elif [ $enable_dylora_train == 1 ]; then
network_module="networks.dylora"
extArgs+=("--network_args unit=$unit")
if [[ $module_dropout != "0" ]]; then
extArgs+=("module_dropout=$module_dropout")
fi
elif [ $enable_block_weights == 1 ]; then
extArgs+=("--network_args down_lr_weight=$down_lr_weight mid_lr_weight=$mid_lr_weight up_lr_weight=$up_lr_weight block_lr_zero_threshold=$block_lr_zero_threshold")
if [ $enable_block_dim == 1 ]; then
extArgs+=("block_dims=$block_dims block_alphas=$block_alphas")
if [ $conv_block_dims ]; then
extArgs+=("conv_block_dims=$conv_block_dims conv_block_alphas=$conv_block_alphas")
fi
fi
fi
if [[ $optimizer_type == "Lion" ]]; then
extArgs+=("--optimizer_type=$optimizer_type" "--optimizer_args weight_decay=0.01 betas=.95,.98")
elif [[ $optimizer_type == "DAdaptation" ]] || [[ $optimizer_type == "DAdaptAdam" ]]; then
extArgs+=("--optimizer_type=$optimizer_type" "--optimizer_args weight_decay=0.01 decouple=True use_bias_correction=True")
lr="1"
unet_lr="1"
text_encoder_lr="1"
elif [[ $optimizer_type == "DAdaptAdan" ]] || [[ $optimizer_type == "DAdaptSGD" ]] || [[ $optimizer_type == "DAdaptAdaGrad" ]]; then
extArgs+=("--optimizer_type=$optimizer_type" "--optimizer_args weight_decay=0.01 betas=.965,.95,.98")
lr="1"
unet_lr="1"
text_encoder_lr="1"
elif [[ $optimizer_type == "adafactor" ]]; then
extArgs+=("--optimizer_type=$optimizer_type" "--optimizer_args scale_parameter=False warmup_init=False relative_step=False")
elif [[ $optimizer_type == "Prodigy" ]]; then
extArgs+=("--optimizer_type=$optimizer_type" "--optimizer_args weight_decay=0.01 decouple=True use_bias_correction=True d_coef=1.0 d0=$d0 safeguard_warmup=True")
lr="1"
unet_lr="1"
text_encoder_lr="1"
elif [[ $optimizer_type == *AdamW8bit ]]; then
extArgs+=("--optimizer_type=$optimizer_type" "--optimizer_args is_paged=True")
elif [[ $optimizer_type == *Lion8bit ]]; then
extArgs+=("--optimizer_type=$optimizer_type" "--optimizer_args weight_decay=0.01 betas=.95,.98 is_paged=True")
fi
if [[ $noise_offset != "0" ]]; then
extArgs+=("--noise_offset=$noise_offset")
if [[ $adaptive_noise_scale != "0" ]]; then extArgs+=("--adaptive_noise_scale=$adaptive_noise_scale"); fi
elif [[ $multires_noise_iterations != "0" ]]; then
extArgs+=("--multires_noise_iterations=$multires_noise_iterations")
extArgs+=("--multires_noise_discount=$multires_noise_discount")
fi
if [[ $max_grad_norm != "0" ]]; then extArgs+=("--max_grad_norm=$max_grad_norm"); fi
if [[ $vae_batch_size -ne 0 ]]; then extArgs+=("--vae_batch_size=$vae_batch_size"); fi
if [[ $min_snr_gamma -ne 0 ]]; then
extArgs+=("--min_snr_gamma=$min_snr_gamma")
elif [[ $debiased_estimation_loss -ne 0 ]]; then
extArgs+=("--debiased_estimation_loss")
fi
if [[ $forced_weight_normalization -ne 0 ]]; then extArgs+=("--forced_weight_normalization"); fi
if [[ $ip_noise_gamma != "0" ]]; then extArgs+=("--ip_noise_gamma=$ip_noise_gamma"); fi
if [[ $gradient_checkpointing -ne 0 ]]; then extArgs+=("--gradient_checkpointing"); fi
if [[ $gradient_accumulation_steps -ne 0 ]]; then extArgs+=("--gradient_accumulation_steps=$gradient_accumulation_steps"); fi
if [[ $is_v2_model == 1 ]]; then
extArgs+=("--v2")
extArgs+=("--v_parameterization")
extArgs+=("--scale_v_pred_loss_like_noise_pred")
extArgs+=("--zero_terminal_snr")
else
extArgs+=("--clip_skip=$clip_skip")
fi
if [ $wandb_api_key ]; then
extArgs+=("--wandb_api_key=$wandb_api_key")
extArgs+=("--log_with=wandb")
extArgs+=("--log_tracker_name=$log_tracker_name")
fi
if [ $enable_sample == 1 ]; then
extArgs+=("--sample_every_n_epochs=$sample_every_n_epochs")
extArgs+=("--sample_prompts=$sample_prompts")
extArgs+=("--sample_sampler=$sample_sampler")
fi
if [[ $enable_bucket -ne 0 ]]; then
extArgs+=("--enable_bucket")
if [[ $min_bucket_reso != "0" ]]; then
extArgs+=("--min_bucket_reso=$min_bucket_reso")
fi
if [[ $max_bucket_reso != "0" ]]; then
extArgs+=("--max_bucket_reso=$max_bucket_reso")
fi
fi
if [[ $torch_compile -ne 0 ]]; then
extArgs+=("--torch_compile")
extArgs+=("--sdpa")
if [ $dynamo_backend ]; then
extArgs+=("--dynamo_backend=$dynamo_backend")
fi
else
extArgs+=("--xformers")
fi
python -m accelerate.commands.launch ${launchArgs[@]} --num_cpu_threads_per_process=8 "./sd-scripts/$train_script.py" \
--pretrained_model_name_or_path=$pretrained_model \
--train_data_dir=$train_data_dir \
--output_dir="/tmp/output" \
--logging_dir="./logs" \
--resolution=$resolution \
--max_train_epochs=$max_train_epoches \
--learning_rate=$lr \
--lr_scheduler=$lr_scheduler \
--lr_warmup_steps=$lr_warmup_steps \
--lr_scheduler_num_cycles=$lr_restart_cycles \
--output_name=$output_name \
--train_batch_size=$batch_size \
--save_every_n_epochs=$save_every_n_epochs \
--save_precision=$save_precision \
--seed=$seed \
--max_token_length=225 \
--caption_extension=".txt" \
--save_model_as=$save_model_as \
--keep_tokens=$keep_tokens \
${extArgs[@]}