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#!/bin/bash
#SBATCH --job-name=taiyi-sd-dreambooth # create a short name for your job
#SBATCH --nodes=1 # node count
#SBATCH --ntasks-per-node=2 # number of tasks to run per node
#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
#SBATCH --gres=gpu:2 # number of gpus per node
#SBATCH -o %x-%j.log # output and error log file names (%x for job id)
#SBATCH -x dgx050
# pwd=Fengshenbang-LM/fengshen/examples/pretrain_erlangshen
ROOT_DIR=../../workspace
# export CUDA_VISIBLE_DEVICES='2'
export TORCH_EXTENSIONS_DIR=${ROOT_DIR}/torch_extendsions
MODEL_NAME=taiyi-sd-dreambooth-prior
MODEL_ROOT_DIR=$ROOT_DIR/${MODEL_NAME}
if [ ! -d ${MODEL_ROOT_DIR} ];then
mkdir ${MODEL_ROOT_DIR}
fi
NNODES=1
GPUS_PER_NODE=2
MICRO_BATCH_SIZE=2
# 如果你不用Deepspeed的话 下面的一段话都可以删掉 Begin
CONFIG_JSON="$MODEL_ROOT_DIR/${MODEL_NAME}.ds_config.json"
ZERO_STAGE=1
# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size()
cat <<EOT > $CONFIG_JSON
{
"zero_optimization": {
"stage": ${ZERO_STAGE}
},
"fp16": {
"enabled": true
},
"train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE
}
EOT
export PL_DEEPSPEED_CONFIG_PATH=$CONFIG_JSON
### End
INSTANCE_PROMPT="[小黄鸭]"
OUTPUT_DIR="saved_model_duck2"
INSTANCE_DIR="train_images_duck"
CLASS_PROMPT="小黄鸭"
CLASS_DIR="class_images_duck"
DATA_ARGS="\
--dataloader_workers 2 \
--train_batchsize $MICRO_BATCH_SIZE \
--val_batchsize $MICRO_BATCH_SIZE \
--test_batchsize $MICRO_BATCH_SIZE \
--instance_data_dir=$INSTANCE_DIR \
--instance_prompt=$INSTANCE_PROMPT \
--class_prompt=$CLASS_PROMPT \
--class_data_dir=$CLASS_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--num_class_images=200 \
--resolution=512 \
--sample_batch_size=1 \
"
MODEL_ARGS="\
--model_path $MODEL_ROOT_DIR/pretrain/Taiyi-Stable-Diffusion-1B-Chinese-v0.1/ \
--train_text_encoder \
--learning_rate 1e-6 \
--scheduler_type constant \
"
MODEL_CHECKPOINT_ARGS="\
--every_n_epochs 100 \
--save_ckpt_path ${MODEL_ROOT_DIR}/ckpt \
--load_ckpt_path ${MODEL_ROOT_DIR}/ckpt/last.ckpt \
"
TRAINER_ARGS="\
--max_epochs 200 \
--gpus $GPUS_PER_NODE \
--num_nodes $NNODES \
--strategy deepspeed_stage_${ZERO_STAGE} \
--log_every_n_steps 100 \
--precision 16 \
--default_root_dir ${MODEL_ROOT_DIR} \
--replace_sampler_ddp False \
--num_sanity_val_steps 0 \
--limit_val_batches 0 \
"
# num_sanity_val_steps， limit_val_batches 通过这俩参数把validation关了
export options=" \
$DATA_ARGS \
$MODEL_ARGS \
$MODEL_CHECKPOINT_ARGS \
$TRAINER_ARGS \
"
# run local
# python train.py $options
# run on slurm
srun python train.py $options |