add ablation study cfgs

scripts/textual_inversion/textual_inversion.sh

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

scripts/textual_inversion/textual_inversion_list.sh

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+
+
+examples=(
+	'data/nerf4/chair'
+	'data/nerf4/drums'
+	'data/nerf4/ficus'
+	'data/nerf4/mic'
+)
+
+for i in "${examples[@]}"; do
+	filename=$(basename "$i")
+	bash scripts/texural_inversion/textual_inversion.sh 0 runwayml/stable-diffusion-v1-5 "$i"/rgba.png out/texural_inversion/${filename} _nerf_${filename}_ ${filename} --max_train_steps 3000
+done