add ablation study cfgs

.gitignore

@@ -8,7 +8,8 @@ shap_e_model_cache/*
 slurm_logs/
 debug/
 notinclude/
-scripts/snap/yamls
+scripts/snap
+scripts/paper 
 
 # */validataion
 *csv

requirements.txt

@@ -26,7 +26,7 @@ dearpygui
 # for stable-diffusion
 huggingface_hub
 diffusers >= 0.9.0
-accelerate # required by textural inversion 
+accelerate # required by textual inversion 
 transformers
 
 # for dmtet

scripts/magic123/run_2dprior.sh

@@ -28,9 +28,9 @@ echo "number of gpus:" $NUM_GPU_AVAILABLE
 RUN_ID=$2
 RUN_ID2=$3
 DATA_DIR=$4
-IMAGE_NAME=$5
-step1=$6
-step2=$7
+IMAGE_NAME=rgba.png
+step1=$5
+step2=$6
 FILENAME=$(basename $DATA_DIR)
 dataset=$(basename $(dirname $DATA_DIR))
 echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
@@ -52,7 +52,7 @@ if (( ${step1} )); then
         --t_range 0.2 0.6 \
         --bg_radius -1 \
         --save_mesh \
-        ${@:8}
+        ${@:7}
 fi
 
 if (( ${step2} )); then

scripts/magic123/run_3dprior.sh

@@ -28,9 +28,9 @@ echo "number of gpus:" $NUM_GPU_AVAILABLE
 RUN_ID=$2
 RUN_ID2=$3
 DATA_DIR=$4
-IMAGE_NAME=$5
-step1=$6
-step2=$7
+IMAGE_NAME=rgba.png
+step1=$5
+step2=$6
 FILENAME=$(basename $DATA_DIR)
 dataset=$(basename $(dirname $DATA_DIR))
 echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
@@ -51,7 +51,7 @@ if (( ${step1} )); then
         --t_range 0.2 0.6 \
         --bg_radius -1 \
         --save_mesh \
-        ${@:8}
+        ${@:7}
 fi
 
 if (( ${step2} )); then

scripts/magic123/run_both_priors.sh

@@ -28,9 +28,9 @@ echo "number of gpus:" $NUM_GPU_AVAILABLE
 RUN_ID=$2 # jobname for the first stage
 RUN_ID2=$3 # jobname for the second stage
 DATA_DIR=$4 # path to the directory containing the images, e.g. data/nerf4/chair
-IMAGE_NAME=$5 # name of the image file, e.g. rgba.png
-step1=$6 # whether to use the first stage
-step2=$7 # whether to use the second stage
+IMAGE_NAME=rgba.png # name of the image file, e.g. rgba.png
+step1=$5 # whether to use the first stage
+step2=$6 # whether to use the second stage
 
 FILENAME=$(basename $DATA_DIR)
 dataset=$(basename $(dirname $DATA_DIR))
@@ -53,7 +53,7 @@ if (( ${step1} )); then
         --t_range 0.2 0.6 \
         --bg_radius -1 \
         --save_mesh \
-        ${@:8}
+        ${@:7}
 fi
 
 if (( ${step2} )); then

scripts/magic123/run_both_priors_angle60.sh

@@ -0,0 +1,82 @@
+#! /bin/bash
+#SBATCH -N 1
+#SBATCH --array=0
+#SBATCH -J magic123
+#SBATCH -o slurm_logs/%x.%3a.%A.out
+#SBATCH -e slurm_logs/%x.%3a.%A.err
+#SBATCH --time=3:00:00
+#SBATCH --gres=gpu:v100:1
+#SBATCH --cpus-per-gpu=6
+#SBATCH --mem=30G
+##SBATCH --gpus=1
+
+module load gcc/7.5.0
+
+
+#source ~/.bashrc
+#source activate magic123
+source venv_magic123/bin/activate
+which python 
+
+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
+
+RUN_ID=$2-p60 # jobname for the first stage
+RUN_ID2=$3-p60 # jobname for the second stage
+DATA_DIR=$4 # path to the directory containing the images, e.g. data/nerf4/chair
+IMAGE_NAME=rgba.png # name of the image file, e.g. rgba.png
+step1=$5 # whether to use the first stage
+step2=$6 # whether to use the second stage
+
+FILENAME=$(basename $DATA_DIR)
+dataset=$(basename $(dirname $DATA_DIR))
+echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
+
+if (( ${step1} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse \
+        --optim adam \
+        --iters 5000 \
+        --guidance SD zero123 \
+        --lambda_guidance 1.0 40 \
+        --guidance_scale 100 5 \
+        --latent_iter_ratio 0 \
+        --normal_iter_ratio 0.2 \
+        --t_range 0.2 0.6 \
+        --bg_radius -1 \
+        --radius_range 1.0 1.5 \
+        --fovy_range 40 70 \
+        --default_polar 60 \
+        --save_mesh \
+        ${@:7}
+fi
+
+if (( ${step2} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-${RUN_ID2}/$dataset/magic123_${FILENAME}_${RUN_ID}_${RUN_ID2} \
+        --dmtet --init_ckpt out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123_${FILENAME}_${RUN_ID}_coarse.pth \
+        --iters 5000 \
+        --optim adam \
+        --latent_iter_ratio 0 \
+        --guidance SD zero123 \
+        --lambda_guidance 1e-3 0.01 \
+        --guidance_scale 100 5 \
+        --rm_edge \
+        --bg_radius -1 \
+        --radius_range 1.0 1.5 \
+        --fovy_range 40 70 \
+        --default_polar 60 \
+        --save_mesh 
+fi

scripts/magic123/run_both_priors_camera.sh

@@ -0,0 +1,80 @@
+#! /bin/bash
+#SBATCH -N 1
+#SBATCH --array=0
+#SBATCH -J magic123
+#SBATCH -o slurm_logs/%x.%3a.%A.out
+#SBATCH -e slurm_logs/%x.%3a.%A.err
+#SBATCH --time=3:00:00
+#SBATCH --gres=gpu:v100:1
+#SBATCH --cpus-per-gpu=6
+#SBATCH --mem=30G
+##SBATCH --gpus=1
+
+module load gcc/7.5.0
+
+
+#source ~/.bashrc
+#source activate magic123
+source venv_magic123/bin/activate
+which python 
+
+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
+
+RUN_ID=$2-camera # jobname for the first stage
+RUN_ID2=$3-camera # jobname for the second stage
+DATA_DIR=$4 # path to the directory containing the images, e.g. data/nerf4/chair
+IMAGE_NAME=rgba.png # name of the image file, e.g. rgba.png
+step1=$5 # whether to use the first stage
+step2=$6 # whether to use the second stage
+
+FILENAME=$(basename $DATA_DIR)
+dataset=$(basename $(dirname $DATA_DIR))
+echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
+
+if (( ${step1} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse \
+        --optim adam \
+        --iters 5000 \
+        --guidance SD zero123 \
+        --lambda_guidance 1.0 40 \
+        --guidance_scale 100 5 \
+        --latent_iter_ratio 0 \
+        --normal_iter_ratio 0.2 \
+        --t_range 0.2 0.6 \
+        --bg_radius -1 \
+        --radius_range 1.0 1.5 \
+        --fovy_range 40 70 \
+        --save_mesh \
+        ${@:7}
+fi
+
+if (( ${step2} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-${RUN_ID2}/$dataset/magic123_${FILENAME}_${RUN_ID}_${RUN_ID2} \
+        --dmtet --init_ckpt out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123_${FILENAME}_${RUN_ID}_coarse.pth \
+        --iters 5000 \
+        --optim adam \
+        --latent_iter_ratio 0 \
+        --guidance SD zero123 \
+        --lambda_guidance 1e-3 0.01 \
+        --guidance_scale 100 5 \
+        --rm_edge \
+        --bg_radius -1 \
+        --radius_range 1.0 1.5 \
+        --fovy_range 40 70 \
+        --save_mesh 
+fi

scripts/magic123/run_both_priors_nodepth.sh

@@ -0,0 +1,87 @@
+#! /bin/bash
+#SBATCH -N 1
+#SBATCH --array=0
+#SBATCH -J magic123
+#SBATCH -o slurm_logs/%x.%3a.%A.out
+#SBATCH -e slurm_logs/%x.%3a.%A.err
+#SBATCH --time=3:00:00
+#SBATCH --gres=gpu:v100:1
+#SBATCH --cpus-per-gpu=6
+#SBATCH --mem=30G
+##SBATCH --gpus=1
+
+module load gcc/7.5.0
+
+
+#source ~/.bashrc
+#source activate magic123
+source venv_magic123/bin/activate
+which python 
+
+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
+
+RUN_ID=$2-nodepth # jobname for the first stage
+RUN_ID2=$3-nodepth # jobname for the second stage
+DATA_DIR=$4 # path to the directory containing the images, e.g. data/nerf4/chair
+IMAGE_NAME=rgba.png # name of the image file, e.g. rgba.png
+step1=$5 # whether to use the first stage
+step2=$6 # whether to use the second stage
+
+FILENAME=$(basename $DATA_DIR)
+dataset=$(basename $(dirname $DATA_DIR))
+echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
+
+if (( ${step1} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse \
+        --optim adam \
+        --iters 5000 \
+        --guidance SD zero123 \
+        --lambda_guidance 1.0 40 \
+        --guidance_scale 100 5 \
+        --latent_iter_ratio 0 \
+        --normal_iter_ratio 0.2 \
+        --t_range 0.2 0.6 \
+        --bg_radius -1 \
+        --save_mesh \
+        --lambda_entropy 1.0e-3 \
+        --lambda_orient 1.0e-2 \
+        --lambda_normal_smooth 0.5 \
+        --lambda_normal_smooth2d 0.5 \
+        --lambda_depth 0 \
+        ${@:7}
+fi
+
+if (( ${step2} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-${RUN_ID2}/$dataset/magic123_${FILENAME}_${RUN_ID}_${RUN_ID2} \
+        --dmtet --init_ckpt out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123_${FILENAME}_${RUN_ID}_coarse.pth \
+        --iters 5000 \
+        --optim adam \
+        --latent_iter_ratio 0 \
+        --guidance SD zero123 \
+        --lambda_guidance 1e-3 0.01 \
+        --guidance_scale 100 5 \
+        --rm_edge \
+        --bg_radius -1 \
+        --save_mesh \
+        --lambda_entropy 1.0e-3 \
+        --lambda_orient 1.0e-2 \
+        --lambda_normal_smooth 0.5 \
+        --lambda_normal_smooth2d 0.5 \
+        --dataset_size_test 8 \
+        --lambda_depth 0 
+fi

scripts/magic123/run_both_priors_noinv.sh

@@ -0,0 +1,74 @@
+#! /bin/bash
+#SBATCH -N 1
+#SBATCH --array=0
+#SBATCH -J magic123
+#SBATCH -o slurm_logs/%x.%3a.%A.out
+#SBATCH -e slurm_logs/%x.%3a.%A.err
+#SBATCH --time=3:00:00
+#SBATCH --gres=gpu:v100:1
+#SBATCH --cpus-per-gpu=6
+#SBATCH --mem=30G
+##SBATCH --gpus=1
+
+module load gcc/7.5.0
+
+
+#source ~/.bashrc
+#source activate magic123
+source venv_magic123/bin/activate
+which python 
+
+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
+
+RUN_ID=$2-noinv # jobname for the first stage
+RUN_ID2=$3-noinv # jobname for the second stage
+DATA_DIR=$4 # path to the directory containing the images, e.g. data/nerf4/chair
+IMAGE_NAME=rgba.png # name of the image file, e.g. rgba.png
+step1=$5 # whether to use the first stage
+step2=$6 # whether to use the second stage
+
+FILENAME=$(basename $DATA_DIR)
+dataset=$(basename $(dirname $DATA_DIR))
+echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
+
+if (( ${step1} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --workspace out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse \
+        --optim adam \
+        --iters 5000 \
+        --guidance SD zero123 \
+        --lambda_guidance 1.0 40 \
+        --guidance_scale 100 5 \
+        --latent_iter_ratio 0 \
+        --normal_iter_ratio 0.2 \
+        --t_range 0.2 0.6 \
+        --bg_radius -1 \
+        --save_mesh \
+        ${@:7}
+fi
+
+if (( ${step2} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --workspace out/magic123-${RUN_ID}-${RUN_ID2}/$dataset/magic123_${FILENAME}_${RUN_ID}_${RUN_ID2} \
+        --dmtet --init_ckpt out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123_${FILENAME}_${RUN_ID}_coarse.pth \
+        --iters 5000 \
+        --optim adam \
+        --latent_iter_ratio 0 \
+        --guidance SD zero123 \
+        --lambda_guidance 1e-3 0.01 \
+        --guidance_scale 100 5 \
+        --rm_edge \
+        --bg_radius -1 \
+        --save_mesh 
+fi

scripts/magic123/run_both_priors_nonorm.sh

@@ -0,0 +1,87 @@
+#! /bin/bash
+#SBATCH -N 1
+#SBATCH --array=0
+#SBATCH -J magic123
+#SBATCH -o slurm_logs/%x.%3a.%A.out
+#SBATCH -e slurm_logs/%x.%3a.%A.err
+#SBATCH --time=3:00:00
+#SBATCH --gres=gpu:v100:1
+#SBATCH --cpus-per-gpu=6
+#SBATCH --mem=30G
+##SBATCH --gpus=1
+
+module load gcc/7.5.0
+
+
+#source ~/.bashrc
+#source activate magic123
+source venv_magic123/bin/activate
+which python 
+
+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
+
+RUN_ID=$2-nonorm # jobname for the first stage
+RUN_ID2=$3-nonorm # jobname for the second stage
+DATA_DIR=$4 # path to the directory containing the images, e.g. data/nerf4/chair
+IMAGE_NAME=rgba.png # name of the image file, e.g. rgba.png
+step1=$5 # whether to use the first stage
+step2=$6 # whether to use the second stage
+
+FILENAME=$(basename $DATA_DIR)
+dataset=$(basename $(dirname $DATA_DIR))
+echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
+
+if (( ${step1} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse \
+        --optim adam \
+        --iters 5000 \
+        --guidance SD zero123 \
+        --lambda_guidance 1.0 40 \
+        --guidance_scale 100 5 \
+        --latent_iter_ratio 0 \
+        --normal_iter_ratio 0.2 \
+        --t_range 0.2 0.6 \
+        --bg_radius -1 \
+        --save_mesh \
+        --lambda_entropy 1.0e-3 \
+        --lambda_orient 1.0e-2 \
+        --lambda_normal_smooth 0 \
+        --lambda_normal_smooth2d 0 \
+        ${@:7}
+fi
+
+if (( ${step2} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-${RUN_ID2}/$dataset/magic123_${FILENAME}_${RUN_ID}_${RUN_ID2} \
+        --dmtet --init_ckpt out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123_${FILENAME}_${RUN_ID}_coarse.pth \
+        --iters 5000 \
+        --optim adam \
+        --latent_iter_ratio 0 \
+        --guidance SD zero123 \
+        --lambda_guidance 1e-3 0.01 \
+        --guidance_scale 100 5 \
+        --rm_edge \
+        --bg_radius -1 \
+        --save_mesh \
+        --lambda_entropy 1.0e-3 \
+        --lambda_orient 1.0e-2 \
+        --lambda_normal_smooth 0 \
+        --lambda_normal_smooth2d 0 \
+        --lambda_mesh_normal 1.0e-10 \
+        --dataset_size_test 8 \
+        --lambda_mesh_lap 0
+fi

scripts/magic123/run_both_priors_noreg.sh

@@ -0,0 +1,82 @@
+#! /bin/bash
+#SBATCH -N 1
+#SBATCH --array=0
+#SBATCH -J magic123
+#SBATCH -o slurm_logs/%x.%3a.%A.out
+#SBATCH -e slurm_logs/%x.%3a.%A.err
+#SBATCH --time=3:00:00
+#SBATCH --gres=gpu:v100:1
+#SBATCH --cpus-per-gpu=6
+#SBATCH --mem=30G
+##SBATCH --gpus=1
+
+module load gcc/7.5.0
+
+
+#source ~/.bashrc
+#source activate magic123
+source venv_magic123/bin/activate
+which python 
+
+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
+
+RUN_ID=$2-noreg # jobname for the first stage
+RUN_ID2=$3-noreg # jobname for the second stage
+DATA_DIR=$4 # path to the directory containing the images, e.g. data/nerf4/chair
+IMAGE_NAME=rgba.png # name of the image file, e.g. rgba.png
+step1=$5 # whether to use the first stage
+step2=$6 # whether to use the second stage
+
+FILENAME=$(basename $DATA_DIR)
+dataset=$(basename $(dirname $DATA_DIR))
+echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
+
+if (( ${step1} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse \
+        --optim adam \
+        --iters 5000 \
+        --guidance SD zero123 \
+        --lambda_guidance 1.0 40 \
+        --guidance_scale 100 5 \
+        --latent_iter_ratio 0 \
+        --normal_iter_ratio 0.2 \
+        --t_range 0.2 0.6 \
+        --bg_radius -1 \
+        --save_mesh \
+        --lambda_entropy 0 \
+        --lambda_orient 1.0e-10 \
+        ${@:7}
+fi
+
+if (( ${step2} )); then
+    CUDA_VISIBLE_DEVICES=$1 python main.py -O \
+        --text "A high-resolution DSLR image of <token>" \
+        --sd_version 1.5 \
+        --image ${DATA_DIR}/${IMAGE_NAME} \
+        --learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
+        --workspace out/magic123-${RUN_ID}-${RUN_ID2}/$dataset/magic123_${FILENAME}_${RUN_ID}_${RUN_ID2} \
+        --dmtet --init_ckpt out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123_${FILENAME}_${RUN_ID}_coarse.pth \
+        --iters 5000 \
+        --optim adam \
+        --latent_iter_ratio 0 \
+        --guidance SD zero123 \
+        --lambda_guidance 1e-3 0.01 \
+        --guidance_scale 100 5 \
+        --rm_edge \
+        --bg_radius -1 \
+        --save_mesh \
+        --lambda_entropy 0 \
+        --lambda_orient 1.0e-10 \
+        --lambda_normal_smooth 0 \
+        --lambda_normal_smooth2d 0
+fi

scripts/magic123/run_folder_both_priors.sh

@@ -1,13 +1,13 @@
-device=$1
-runid=$2 # jobname for the first stage
-runid2=$3 # jobname for the second stage
-topdir=$4   # path to the directory containing the images, e.g. data/nerf4
-imagename=$5
+script_name=$1
+device=$2
+runid=$3 # jobname for the first stage
+runid2=$4 # jobname for the second stage
+topdir=$5   # path to the directory containing the images, e.g. data/nerf4
 step1=$6
 step2=$7
 
 for i in $topdir/*; do
     echo "$i"
     [ -d "$i" ] && echo "$i exists."
-    bash scripts/magic123/run_both_priors.sh $device $runid "$i" $imagename $step1 $step2 ${@:8}
+    bash ${script_name} $device $runid "$i" $step1 $step2 ${@:8}
 done

scripts/magic123/run_list_both_priors.sh

@@ -1,18 +1,18 @@
-device=$1
-runid=$2 # jobname for the first stage
-runid2=$3 # jobname for the second stage
-imagename=$4
+script_name=$1
+device=$2
+runid=$3 # jobname for the first stage
+runid2=$4 # jobname for the second stage
 step1=$5
 step2=$6
 
 examples=(
+    'data/nerf4/chair'
+    'data/realfusion15/colorful_teapot/'
     'data/realfusion15/teddy_bear/'
     'data/realfusion15/mental_dragon_statue/'
-    'data/realfusion15/colorful_teapot/'
     'data/realfusion15/fish_real_nemo/'
     'data/realfusion15/two_cherries' 
     'data/realfusion15/watercolor_horse/'
-    'data/nerf4/chair'
     'data/nerf4/drums'
     'data/nerf4/ficus'
     'data/nerf4/mic'
@@ -21,5 +21,7 @@ examples=(
 for i in "${examples[@]}"; do
     echo "$i"
     [ -d "$i" ] && echo "$i exists."
-    bash scripts/magic123/run_both_priors.sh $device $runid "$i" $imagename $step1 $step2 ${@:7}
+    bash ${script_name} $device $runid $runid2 "$i" $step1 $step2 ${@:7}
 done
+
+# usage: bash scripts/magic123/run_list_both_priors.sh scripts/magic123/run_both_priors.sh 0 coarse fine 1 1

scripts/textual_inversion/textual_inversion.sh

@@ -48,5 +48,5 @@ CUDA_VISIBLE_DEVICES=$1 python textual-inversion/textual_inversion.py \
   --use_augmentations \
   ${@:7}
 
-# test textural inversion
+# 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

@@ -0,0 +1,13 @@
+
+
+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

scripts/texural_inversion/textural_inversion_list.sh

@@ -1,13 +0,0 @@
-
-
-examples=(
-	'data/nerf4/chair'
-	'data/nerf4/drums'
-	'data/nerf4/ficus'
-	'data/nerf4/mic'
-)
-
-for i in "${examples[@]}"; do
-	filename=$(basename "$i")
-	bash scripts/texural_inversion/textural_inversion.sh 0 runwayml/stable-diffusion-v1-5 "$i"/rgba.png out/texural_inversion/${filename} _nerf_${filename}_ ${filename} --max_train_steps 3000
-done