52 lines
1.4 KiB
Bash
52 lines
1.4 KiB
Bash
#! /bin/bash
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#SBATCH -N 1
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#SBATCH --array=0
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#SBATCH -J dreamfusion
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#SBATCH -o slurm_logs/%x.%3a.%A.out
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#SBATCH -e slurm_logs/%x.%3a.%A.err
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#SBATCH --time=9:00:00
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#SBATCH --gres=gpu:v100:1
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#SBATCH --cpus-per-gpu=6
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#SBATCH --mem=30G
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##SBATCH --gpus=1
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module load gcc/7.5.0
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echo "===> Anaconda env loaded"
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#source ~/.bashrc
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#source activate magic123
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source venv_magic123/bin/activate
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nvidia-smi
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nvcc --version
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hostname
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NUM_GPU_AVAILABLE=`nvidia-smi --query-gpu=name --format=csv,noheader | wc -l`
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echo "number of gpus:" $NUM_GPU_AVAILABLE
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MODEL_NAME=$2 # "path-to-pretrained-model" runwayml/stable-diffusion-v1-5
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DATA_DIR=$3 # "path-to-dir-containing-your-image"
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OUTPUT_DIR=$4 # "path-to-desired-output-dir"
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placeholder_token=$5 # _ironman_
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init_token=$6 # ironman
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# run texturaal inversion
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CUDA_VISIBLE_DEVICES=$1 python textual-inversion/textual_inversion.py \
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--pretrained_model_name_or_path=$MODEL_NAME \
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--train_data_dir=$DATA_DIR \
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--learnable_property="object" \
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--placeholder_token=$placeholder_token \
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--initializer_token=$init_token \
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--resolution=512 \
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--train_batch_size=16 \
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--gradient_accumulation_steps=1 \
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--max_train_steps=3000 \
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--lr_scheduler="constant" \
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--lr_warmup_steps=0 \
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--output_dir=$OUTPUT_DIR \
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--use_augmentations \
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${@:7}
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# test textual inversion
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CUDA_VISIBLE_DEVICES=$1 python guidance/sd_utils.py --text "A high-resolution DSLR image of <token>" --learned_embeds_path $OUTPUT_DIR --workspace $OUTPUT_DIR |