added eigen requriement and installation

install.sh

@@ -6,13 +6,13 @@ module load eigen
 
 # for aws ubuntu.  install eigen
 #sudo apt update && sudo apt upgrade
-#sudo apt install libeigen3-dev
+#sudo apt install git wget libeigen3-dev -y
 
  # a100: 8.0; v100: 7.0; 2080ti: 7.5; titan xp: 6.1
 export TORCH_CUDA_ARCH_LIST="7.0;7.5;8.0"
 
 # use python venv
-python -m venv venv_magic123
+python3 -m venv venv_magic123
 source venv_magic123/bin/activate
 
 # use conda

readme.md

@@ -35,12 +35,19 @@ Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object
 
 
 # Install
+We only test on Ubuntu system. Make sure git, wget, Eigen are installed.  
+```
+apt update && apt upgrade
+apt install git wget libeigen3-dev -y
+```
 
 ### Install Environment 
 
 ```bash
 source install.sh
 ```
+Note: in this `install.sh`, we use python venv by default. If you prefer conda, uncomment the conda and comment venv in the file and run the same command. 
+
 
 ### Download pre-trained models
 
@@ -63,7 +70,7 @@ source install.sh
 
 # Usage
 ## Preprocess [Optional]
-We have included all preprocessed files in `./data` directory. Preprocessing is only necessary if you want to test on your own examples. 
+We have included all preprocessed files in `./data` directory. Preprocessing is only necessary if you want to test on your own examples. Takes seconds.  
 
 ### Step1: Extract depth 
 ```
@@ -72,7 +79,7 @@ python preprocess_image.py --path /path/to/image
 
 
 ### Step 2: Textural inversion [Optional]
-Magic123 uses the defualt [textural inversion](https://huggingface.co/docs/diffusers/training/text_inversion) from diffuers, which consumes around 2.5 hours on a 32G V100. If you do not want to spend time in this textural inversion, you can: (1) study whether there is other faster textural inversion; or (2) do not use textural inversion in the loss of texture and shape consistencies.  To run textural inversion: 
+Magic123 uses the defualt [textural inversion](https://huggingface.co/docs/diffusers/training/text_inversion) from diffuers, which consumes around 2 hours on a 32G V100. If you do not want to spend time in this textural inversion, you can: (1) study whether there is other faster textural inversion; or (2) do not use textural inversion in the loss of texture and shape consistencies.  To run textural inversion: 
 
 ```
 bash scripts/texural_inversion/textural_inversion.sh $GPU_IDX runwayml/stable-diffusion-v1-5 /path/to/example/rgba.png /path/to/save $token_name $init_token --max_train_steps 5000

scripts/snap/aws_folder.sh

@@ -1,21 +0,0 @@
-script_name=$1
-runid=$2
-runid2=$3
-topdir=$4
-imagename=$5
-run1=$6
-run2=$7
-arguments="${@:8}"
-
-timestamp=$(date +'%Y%m%d')
-for i in $topdir/*; do
-    echo "$i"
-    [ -d "$i" ] && echo "$i exists."
-    example=$(basename $i)
-    echo ${@:8}
-    python scripts/snap/submit_cluster_job.py --yaml_folder scripts/snap/yamls \
-        --gpu_memory 40 --gpu_num 1 --force_node --cpu_num_per_gpu 6 --memory_per_gpu 30.0 --replicas 1 \
-        --project_name magic123 --project_support_alias img2mesh \
-        --job_name gqian-$timestamp-$runid-$runid2-$example \
-        --command "bash $script_name 0 $runid $runid2 $i $imagename $run1 $run2 $arguments "
-done

scripts/snap/aws_list.sh

@@ -1,27 +0,0 @@
-script_name=$1
-runid=$2
-runid2=$3
-imagename=$4
-run1=$5
-run2=$6
-arguments="${@:7}"
-
-
-examples=(
-    'data/realfusion15/two_donuts/'
-    'data/realfusion15/watercolor_horse/'
-)
-
-
-timestamp=$(date +'%Y%m%d')
-for i in "${examples[@]}"; do
-    echo "$i"
-    [ -d "$i" ] && echo "$i exists."
-    example=$(basename $i)
-    echo ${@:8}
-    python scripts/snap/submit_cluster_job.py --yaml_folder scripts/snap/yamls \
-        --gpu_memory 40 --gpu_num 1 --force_node --cpu_num_per_gpu 6 --memory_per_gpu 30.0 --replicas 1 \
-        --project_name magic123 --project_support_alias img2mesh \
-        --job_name gqian-$timestamp-$runid-$runid2-$example \
-        --command "bash $script_name 0 $runid $runid2 $i $imagename $run1 $run2 $arguments "
-done

scripts/snap/submit_cluster_debug.sh

@@ -1,8 +0,0 @@
-jobname=$1
-timestamp=$(date +'%Y%m%d')
-[ -d "$i" ] && echo "$i exists."
-python scripts/snap/submit_cluster_job.py --yaml_folder scripts/snap/yamls \
-        --gpu_memory 40 --gpu_num 1 --force_node --cpu_num_per_gpu 6 --memory_per_gpu 30.0 --replicas 1 \
-        --project_name magic123 --project_support_alias img2mesh \
-        --job_name gqian-$timestamp-$1 \
-        --command "while :; do sleep 1000; done"

scripts/snap/submit_cluster_job.py

@@ -1,99 +0,0 @@
-
-import yaml
-import os
-import argparse
-
-
-def generate_yaml(
-                  gpu_memory=40, # 40G or 80G?
-                  gpu_num=1, 
-                  cpu_num_per_gpu=6,
-                  memory_per_gpu=30,
-                  replicas=1,
-                  project_name='magic123',
-                  project_support_alias='img2mesh',
-                  pre_run_event='mkdir -p /fsx/code && ln -s /nfs/code/gqian /fsx/code/ && cd /fsx/code/gqian/img2mesh', 
-                  command="while :; do sleep 1000; done", 
-                  job_name='debug',  
-                  force_node=False,
-                  **kwargs
-                  ):
-    data = {
-        'docker_image': '440036398022.dkr.ecr.us-west-2.amazonaws.com/facecraft-ml:efa',
-        'project_name': project_name,
-        'project_support_alias': project_support_alias,
-        'team': 'creative_vision',
-        #'fsx': 'fs-0b933bba2f17fe699', # 100T genai filesystem
-        'fsx': 'fs-056caaa56fa5cc5f3', # 2T personal filesystem of gqian
-        'gpu_type': 'nvidia-tesla-a100',
-        'gpu_num': gpu_num,
-        'cpu_num': str(int(cpu_num_per_gpu * gpu_num)),
-        'memory': str(int(memory_per_gpu * gpu_num)),
-        'gpu_memory': str(int(gpu_memory)),
-        'pytorchjob': {
-            'replicas': replicas
-        },
-        'efa': True,
-        'script': {
-            'pre_run_event': str(pre_run_event),
-            'command': str(command),
-            'jobs': [
-                {'name': job_name}
-            ]
-        }
-    }
-    if gpu_num == 1 and not force_node:
-        gpu_yaml = {
-            'custom_node_labels': {
-            'use_case': 'p4d_debug',
-        },
-        }
-    else:
-        gpu_yaml = {
-            'custom_node_labels': {
-            'snap.com/spine': 'unknown',
-            'snap.com/region': 'us-west-2c', 
-        },
-        }
-    data.update(gpu_yaml) 
-    return data
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Script to generate yaml file for AWS Pytorch Job")
-    parser.add_argument('--yaml_folder', type=str, default='./', help='path to save the yaml folder')
-    parser.add_argument('--gpu_memory', type=int, default=40, help='GPU memory (in GB)')
-    parser.add_argument('--gpu_num', type=int, default=1, help='Number of GPUs')
-    parser.add_argument('--cpu_num_per_gpu', type=int, default=6, help='Number of CPUs per GPU')
-    parser.add_argument('--memory_per_gpu', type=float, default=30.0, help='Memory per GPU')
-    parser.add_argument('--replicas', type=int, default=1, help='Number of replicas')
-    parser.add_argument('--project_name', type=str, default='magic123', help='Project name')
-    parser.add_argument('--project_support_alias', type=str, default='img2mesh', help='Project support alias')
-    #parser.add_argument('--pre_run_event', type=str, default='export PATH=/nfs/code/gqian/miniconda3/bin:$PATH && conda init bash && source ~/.bashrc && cd /nfs/code/gqian/img2mesh ', help='Pre-run event command')
-    parser.add_argument('--pre_run_event', type=str, default='cd /nfs/code/gqian/img2mesh', help='Pre-run event command')
-    parser.add_argument('--command', type=str, default='while :; do sleep 1000; done', help='Command')
-    parser.add_argument('--job_name', type=str, default='debug', help='Job name')
-    parser.add_argument('--force_node', action='store_true',
-                         help="use normal cluster not debug cluster")
-    args, unknown = parser.parse_known_args()
-    args.job_name = args.job_name.replace('_', '-').replace('.', '-')[:51] # do not support _ and . in job name, and max length is limited (around 70)
-
-    # "bash scripts/magic123/run_single_bothpriors.sh 0 r256 data/nerf4/drums rgba.png --h 300 --w 300"
-    data = generate_yaml(**vars(args))
-    yaml_str = yaml.safe_dump(data)
-
-    # Write the YAML content to a file
-    os.makedirs(args.yaml_folder, exist_ok=True)
-    yaml_path = os.path.join(args.yaml_folder, f'{args.job_name}.yaml')
-    with open(yaml_path, 'w') as file:
-        file.write(yaml_str)
-    print(f'YAML file saved to {yaml_path}')
-    
-    # launch the job using snap_rutls
-    os.system(f'yes yes | snap_rutils cluster run {yaml_path} -s')
-    
-    # show the job status
-    os.system(f'kubectl get pods | grep {args.job_name} ')
-
-    # show the job logs
-    os.system(f'kubectl logs {args.job_name}-worker-0')

scripts/snap/submit_cluster_job.sh

@@ -1,18 +0,0 @@
-script_name=$1
-runid=$2
-runid2=$3
-i=$4
-imagename=$5
-run1=$6
-run2=$7
-arguments="${@:8}"
-
-timestamp=$(date +'%Y%m%d')
-[ -d "$i" ] && echo "$i exists."
-example=$(basename $i)
-echo ${@:8}
-python scripts/snap/submit_cluster_job.py --yaml_folder scripts/snap/yamls \
-        --gpu_memory 40 --gpu_num 1 --force_node --cpu_num_per_gpu 6 --memory_per_gpu 30.0 --replicas 1 \
-        --project_name magic123 --project_support_alias img2mesh \
-        --job_name gqian-$timestamp-$runid-$runid2-$example \
-        --command "bash $script_name 0 $runid $runid2 $i $imagename $run1 $run2 $arguments "