first commit
This commit is contained in:
Guocheng Qian
6
scripts/magic123/preprocess_folder.sh
Executable file
6
scripts/magic123/preprocess_folder.sh
Executable file
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topdir=$1
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imagename=$2 # rgba.png or image.png
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for i in $topdir/*; do
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echo preprocessing "$i"/$imagename ...
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python scripts/preprocess_image.py "$i"/$imagename
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done
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75
scripts/magic123/run_2dprior.sh
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75
scripts/magic123/run_2dprior.sh
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#! /bin/bash
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#SBATCH -N 1
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#SBATCH --array=0
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#SBATCH -J magic123
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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=3:00:00
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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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#source ~/.bashrc
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#source activate magic123
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source venv_magic123/bin/activate
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which python
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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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RUN_ID=$2
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RUN_ID2=$3
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DATA_DIR=$4
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IMAGE_NAME=$5
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step1=$6
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step2=$7
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FILENAME=$(basename $DATA_DIR)
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dataset=$(basename $(dirname $DATA_DIR))
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echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
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if (( ${step1} )); then
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CUDA_VISIBLE_DEVICES=$1 python main.py -O \
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--text "A high-resolution DSLR image of <token>" \
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--sd_version 1.5 \
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--image ${DATA_DIR}/${IMAGE_NAME} \
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--learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
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--workspace out/magic123-2d/magic123-2d-${RUN_ID}-coarse/$dataset/magic123_2d_${FILENAME}_${RUN_ID}_coarse \
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--optim adam \
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--iters 5000 \
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--guidance SD \
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--lambda_guidance 1 \
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--guidance_scale 100 \
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--latent_iter_ratio 0 \
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--normal_iter_ratio 0.2 \
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--t_range 0.2 0.6 \
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--bg_radius -1 \
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--save_mesh \
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${@:8}
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fi
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if (( ${step2} )); then
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CUDA_VISIBLE_DEVICES=$1 python main.py -O \
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--text "A high-resolution DSLR image of <token>" \
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--sd_version 1.5 \
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--image ${DATA_DIR}/${IMAGE_NAME} \
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--learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
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--workspace out/magic123-2d/magic123-2d-${RUN_ID}-${RUN_ID2}/$dataset/magic123_2d_${FILENAME}_${RUN_ID}_${RUN_ID2} \
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--dmtet --init_ckpt out/magic123-2d/magic123-2d-${RUN_ID}-coarse/$dataset/magic123_2d_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123_2d_${FILENAME}_${RUN_ID}_coarse.pth \
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--iters 5000 \
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--optim adam \
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--latent_iter_ratio 0 \
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--guidance SD \
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--lambda_guidance 1e-3 \
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--guidance_scale 100 \
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--rm_edge \
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--bg_radius -1 \
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--save_mesh
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fi
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73
scripts/magic123/run_2dprior_notextinv_ironman.sh
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73
scripts/magic123/run_2dprior_notextinv_ironman.sh
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@@ -0,0 +1,73 @@
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#! /bin/bash
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#SBATCH -N 1
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#SBATCH --array=0
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#SBATCH -J magic123
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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=3:00:00
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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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#source ~/.bashrc
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#source activate magic123
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source venv_magic123/bin/activate
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which python
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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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RUN_ID=$2
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RUN_ID2=$3
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DATA_DIR="data/demo/ironman"
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IMAGE_NAME="rgba.png"
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step1=$4
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step2=$5
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FILENAME=$(basename $DATA_DIR)
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dataset=$(basename $(dirname $DATA_DIR))
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echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
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if (( ${step1} )); then
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CUDA_VISIBLE_DEVICES=$1 python main.py -O \
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--text "A high-resolution DSLR image of a full body ironman" \
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--sd_version 1.5 \
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--image ${DATA_DIR}/${IMAGE_NAME} \
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--workspace out/magic123-2d-noinv/magic123-2d-noinv-${RUN_ID}-coarse/$dataset/magic123-2d-noinv_${FILENAME}_${RUN_ID}_coarse \
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--optim adam \
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--iters 5000 \
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--guidance SD \
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--lambda_guidance 1 \
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--guidance_scale 100 \
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--latent_iter_ratio 0 \
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--normal_iter_ratio 0.2 \
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--t_range 0.2 0.6 \
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--bg_radius -1 \
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--save_mesh \
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${@:6}
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fi
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if (( ${step2} )); then
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CUDA_VISIBLE_DEVICES=$1 python main.py -O \
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--text "A high-resolution DSLR image of a full body ironman" \
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--sd_version 1.5 \
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--image ${DATA_DIR}/${IMAGE_NAME} \
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--workspace out/magic123-2d-noinv/magic123-2d-noinv-${RUN_ID}-${RUN_ID2}/$dataset/magic123-2d-noinv_${FILENAME}_${RUN_ID}_${RUN_ID2} \
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--dmtet --init_ckpt out/magic123-2d-noinv/magic123-2d-noinv-${RUN_ID}-coarse/$dataset/magic123-2d-noinv_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123-2d-noinv_${FILENAME}_${RUN_ID}_coarse.pth \
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--iters 5000 \
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--optim adam \
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--latent_iter_ratio 0 \
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--guidance SD \
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--lambda_guidance 1e-3 \
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--guidance_scale 100 \
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--rm_edge \
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--bg_radius -1 \
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--save_mesh
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fi
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73
scripts/magic123/run_3dprior.sh
Executable file
73
scripts/magic123/run_3dprior.sh
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@@ -0,0 +1,73 @@
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#! /bin/bash
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#SBATCH -N 1
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#SBATCH --array=0
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#SBATCH -J magic123
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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=3:00:00
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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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#source ~/.bashrc
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#source activate magic123
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source venv_magic123/bin/activate
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which python
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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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RUN_ID=$2
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RUN_ID2=$3
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DATA_DIR=$4
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IMAGE_NAME=$5
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step1=$6
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step2=$7
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FILENAME=$(basename $DATA_DIR)
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dataset=$(basename $(dirname $DATA_DIR))
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echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
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if (( ${step1} )); then
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CUDA_VISIBLE_DEVICES=$1 python main.py -O \
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--text "A high-resolution DSLR image" \
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--sd_version 1.5 \
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--image ${DATA_DIR}/${IMAGE_NAME} \
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--workspace out/magic123-3d/magic123-3d-${RUN_ID}/$dataset/magic123-3d_${FILENAME}_${RUN_ID}_5k \
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--optim adam \
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--iters 5000 \
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--guidance zero123 \
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--lambda_guidance 40 \
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--guidance_scale 5 \
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--latent_iter_ratio 0 \
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--normal_iter_ratio 0.2 \
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--t_range 0.2 0.6 \
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--bg_radius -1 \
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--save_mesh \
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${@:8}
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fi
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if (( ${step2} )); then
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CUDA_VISIBLE_DEVICES=$1 python main.py -O \
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--text "A high-resolution DSLR image" \
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--sd_version 1.5 \
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--image ${DATA_DIR}/${IMAGE_NAME} \
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--workspace out/magic123-3d/magic123-3d-${RUN_ID}-${RUN_ID2}/$dataset/magic123-3d_${FILENAME}_${RUN_ID}_${RUN_ID2} \
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--dmtet --init_ckpt out/magic123-3d/magic123-3d-${RUN_ID}/$dataset/magic123-3d_${FILENAME}_${RUN_ID}_5k/checkpoints/magic123-3d_${FILENAME}_${RUN_ID}_5k.pth \
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--iters 5000 \
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--optim adam \
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--latent_iter_ratio 0 \
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--guidance zero123 \
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--lambda_guidance 0.01 \
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--guidance_scale 5 \
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--bg_radius -1 \
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--rm_edge \
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--save_mesh
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fi
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76
scripts/magic123/run_both_priors.sh
Executable file
76
scripts/magic123/run_both_priors.sh
Executable file
@@ -0,0 +1,76 @@
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#! /bin/bash
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#SBATCH -N 1
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#SBATCH --array=0
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#SBATCH -J magic123
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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=3:00:00
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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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#source ~/.bashrc
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#source activate magic123
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source venv_magic123/bin/activate
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which python
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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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RUN_ID=$2 # jobname for the first stage
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RUN_ID2=$3 # jobname for the second stage
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DATA_DIR=$4 # path to the directory containing the images, e.g. data/nerf4/chair
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IMAGE_NAME=$5 # name of the image file, e.g. rgba.png
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step1=$6 # whether to use the first stage
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step2=$7 # whether to use the second stage
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FILENAME=$(basename $DATA_DIR)
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dataset=$(basename $(dirname $DATA_DIR))
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echo reconstruct $FILENAME under dataset $dataset from folder $DATA_DIR ...
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if (( ${step1} )); then
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CUDA_VISIBLE_DEVICES=$1 python main.py -O \
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--text "A high-resolution DSLR image of <token>" \
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--sd_version 1.5 \
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--image ${DATA_DIR}/${IMAGE_NAME} \
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--learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
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--workspace out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse \
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--optim adam \
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--iters 5000 \
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--guidance SD zero123 \
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--lambda_guidance 1.0 40 \
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--guidance_scale 100 5 \
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--latent_iter_ratio 0 \
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--normal_iter_ratio 0.2 \
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--t_range 0.2 0.6 \
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--bg_radius -1 \
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--save_mesh \
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${@:8}
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fi
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if (( ${step2} )); then
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CUDA_VISIBLE_DEVICES=$1 python main.py -O \
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--text "A high-resolution DSLR image of <token>" \
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--sd_version 1.5 \
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--image ${DATA_DIR}/${IMAGE_NAME} \
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--learned_embeds_path ${DATA_DIR}/learned_embeds.bin \
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--workspace out/magic123-${RUN_ID}-${RUN_ID2}/$dataset/magic123_${FILENAME}_${RUN_ID}_${RUN_ID2} \
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--dmtet --init_ckpt out/magic123-${RUN_ID}-coarse/$dataset/magic123_${FILENAME}_${RUN_ID}_coarse/checkpoints/magic123_${FILENAME}_${RUN_ID}_coarse.pth \
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--iters 5000 \
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--optim adam \
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--latent_iter_ratio 0 \
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--guidance SD zero123 \
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--lambda_guidance 1e-3 0.01 \
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--guidance_scale 100 5 \
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--rm_edge \
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--bg_radius -1 \
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--save_mesh
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fi
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13
scripts/magic123/run_folder_both_priors.sh
Executable file
13
scripts/magic123/run_folder_both_priors.sh
Executable file
@@ -0,0 +1,13 @@
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device=$1
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runid=$2 # jobname for the first stage
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runid2=$3 # jobname for the second stage
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topdir=$4 # path to the directory containing the images, e.g. data/nerf4
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imagename=$5
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step1=$6
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step2=$7
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for i in $topdir/*; do
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echo "$i"
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[ -d "$i" ] && echo "$i exists."
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bash scripts/magic123/run_both_priors.sh $device $runid "$i" $imagename $step1 $step2 ${@:8}
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done
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25
scripts/magic123/run_list_both_priors.sh
Executable file
25
scripts/magic123/run_list_both_priors.sh
Executable file
@@ -0,0 +1,25 @@
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device=$1
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runid=$2 # jobname for the first stage
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runid2=$3 # jobname for the second stage
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imagename=$4
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step1=$5
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step2=$6
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examples=(
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'data/realfusion15/teddy_bear/'
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'data/realfusion15/mental_dragon_statue/'
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'data/realfusion15/colorful_teapot/'
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'data/realfusion15/fish_real_nemo/'
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'data/realfusion15/two_cherries'
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'data/realfusion15/watercolor_horse/'
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'data/nerf4/chair'
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'data/nerf4/drums'
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'data/nerf4/ficus'
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'data/nerf4/mic'
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)
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for i in "${examples[@]}"; do
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echo "$i"
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[ -d "$i" ] && echo "$i exists."
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bash scripts/magic123/run_both_priors.sh $device $runid "$i" $imagename $step1 $step2 ${@:7}
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done
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