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logs/textual_inversion/1690152827.559054/hparams.yml

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+adam_beta1: 0.9
+adam_beta2: 0.999
+adam_epsilon: 1.0e-08
+adam_weight_decay: 0.01
+allow_tf32: false
+center_crop: false
+checkpointing_steps: 500
+checkpoints_total_limit: null
+dataloader_num_workers: 0
+enable_xformers_memory_efficient_attention: false
+gradient_accumulation_steps: 4
+gradient_checkpointing: false
+hub_model_id: null
+hub_token: null
+initializer_token: room
+learnable_property: style
+learning_rate: 0.002
+local_rank: 0
+logging_dir: logs
+lr_num_cycles: 1
+lr_scheduler: constant
+lr_warmup_steps: 0
+max_train_steps: 3000
+mixed_precision: 'no'
+num_train_epochs: 7
+num_validation_images: 4
+num_vectors: 1
+output_dir: textual_inversion_scnnt_710
+placeholder_token: <scnnt_710>
+pretrained_model_name_or_path: runwayml/stable-diffusion-v1-5
+push_to_hub: true
+repeats: 100
+report_to: tensorboard
+resolution: 512
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+train_data_dir: scnnt_710
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run_inversion.sh

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+#!/bin/bash
+#SBATCH --partition=orion --qos=normal
+# #SBATCH --time=96:00:00  --> this is a comment, you can choose to not specify a nodelist, it will randomly assign to a GPU
+#SBATCH --nodes=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=12G
+#SBATCH --account=orion
+
+# only use the following on partition with GPUs
+
+# only use the following if yo####SBATCH --mail-user=youremailaddress
+####SBATCH --mail-type=ALLu want email notification
+
+
+# list out some useful information (optional)
+echo "SLURM_JOBID="$SLURM_JOBID
+echo "SLURM_JOB_NODELIST"=$SLURM_JOB_NODELIST
+echo "SLURM_NNODES"=$SLURM_NNODES
+echo "SLURMTMPDIR="$SLURMTMPDIR
+echo "working directory = "$SLURM_SUBMIT_DIR
+
+# sample process (list hostnames of the nodes you've requested)
+# NPROCS=`srun --nodes=${SLURM_NNODES} bash -c 'hostname' |wc -l`
+# echo NPROCS=$NPROCS
+
+# can try the following to list out which GPU you have access to
+#srun /usr/local/cuda/samples/1_Utilities/deviceQuery/deviceQuery
+source ~/.bashrc
+export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-$cuda_version/lib64
+export PATH=${PATH}:/usr/local/cuda-$cuda_version/bin
+export CUDA_HOME=/usr/local/cuda-$cuda_version
+cd $path
+conda activate $conda
+accelerate launch textual_inversion.py \
+  --pretrained_model_name_or_path=runwayml/stable-diffusion-v1-5 \
+  --train_data_dir=scnnt_710  \
+  --learnable_property="style" \
+  --placeholder_token="<scnnt_710>" --initializer_token="room" \
+  --resolution=512 \
+  --train_batch_size=1 \
+  --gradient_accumulation_steps=4 \
+  --max_train_steps=3000 \
+  --learning_rate=5.0e-04 --scale_lr \
+  --lr_scheduler="constant" \
+  --lr_warmup_steps=0 \
+  --push_to_hub \
+  --output_dir="textual_inversion_scnnt_710"

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