guochengqian / Magic123

воскресенье, 6 августа 2023 г. в 00:00:03

https://github.com/guochengqian/Magic123

Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors

Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors

arXiv | webpage

Guocheng Qian ^1,2, Jinjie Mai ¹, Abdullah Hamdi ³, Jian Ren ², Aliaksandr Siarohin ², Bing Li ¹, Hsin-Ying Lee ², Ivan Skorokhodov ^1,2, Peter Wonka ¹, Sergey Tulyakov ², Bernard Ghanem ¹

¹ King Abdullah University of Science and Technology (KAUST), ² Snap Inc., ³ Visual Geometry Group, University of Oxford

Training convergence of a demo example:

Compare Magic123 without textual inversion with abaltions using only 2D prior (SDS) or using only 3D prior (Zero123):

magic123-results.mp4

Effects of Joint Prior. Increasing the strength of 2D prior leads to more imagination, more details, and less 3D consistencies.

Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors. Code is built upon Stable-DreamFusion repo.

NEWS:

[2023/07/25] Code is available at GitHub
[2023/07/03] Paper is available at arXiv
[2023/06/25] Much better performance than the submitted version is achieved by 1）reimplementing Magic123 using Stable DreamFusion code, 2）fixing some gradient issues, 3）leveraging the tricks
[2023] Initial version of Magic123 submitted to conference

Install

Install Environment

source install.sh

Download pre-trained models

Zero-1-to-3 for 3D diffusion prior. We use 105000.ckpt by default, reimplementation borrowed from Stable Diffusion repo, and is available in guidance/zero123_utils.py.
```
cd pretrained/zero123
wget https://huggingface.co/cvlab/zero123-weights/resolve/main/105000.ckpt
cd .../../
```

MiDaS for depth estimation. We use dpt_beit_large_512.pt. Put it in folder pretrained/midas/

mkdir -p pretrained/midas
cd pretrained/midas
wget https://github.com/isl-org/MiDaS/releases/download/v3_1/dpt_beit_large_512.pt
cd ../../

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.

Step1: Extract depth

python preprocess_image.py --path /path/to/image

Step 2: Textural inversion [Optional]

Magic123 uses the defualt textural 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:

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

$token_name is a the special token, usually name that by examplename $init_token is a single token to describe the image using natural language

For example:

bash scripts/texural_inversion/textural_inversion.sh runwayml/stable-diffusion-v1-5 data/demo/ironman/rgba.png out/textual_inversion/ironman _ironman_ ironman --max_train_steps 3000

Don't forget to move the final learned_embeds.bin under data/demo/ironman/

Run

Run Magic123 for a single example

Takes ~40 mins for the coarse stage and ~20 mins for the second stage on a 32G V100.

bash scripts/magic123/run_both_priors.sh $GPU_NO $JOBNAME_First_Stage $JOBNAME_Second_Stage $PATH_to_Example_Directory $IMAGE_BASE_NAME $Enable_First_Stage $Enable_Second_Stage {More_Arugments}

As an example, run Magic123 in the dragon example using both stages in GPU 0 and set the jobname for the first stage as default and the jobname for the second stage as dmtet, by the following command:

bash scripts/magic123/run_both_priors.sh 0 default dmtet data/realfusion15/metal_dragon_statue rgba.png 1 1

More arguments (e.g. --lambda_guidance 1 40) can be appended to the command line such as:

bash scripts/magic123/run_both_priors.sh 0 default dmtet data/realfusion15/metal_dragon_statue rgba.png 1 1 --lambda_guidance 1 40

Run Magic123 for a group of examples

Run all examples in a folder, check the scripts scripts/magic123/run_folder_both_priors.sh
Run all examples in a given list, check the scripts scripts/magic123/run_list_both_priors.sh

Run Magic123 on a single example without textural inversion

Textural inversion is tedious (requires ~2.5 hours optimization), if you want to test Magic123 quickly on your own example without texural inversion (might degrade the performance), try the following:

first, foreground and depth estimation

python preprocess_image.py --path data/demo/ironman/ironman.png

Run Magic123 coarse stage without textural inversion, takes ~40 mins

export RUN_ID='default-a-full-body-ironman'
export DATA_DIR='data/demo/ironman'
export IMAGE_NAME='rgba.png'
export FILENAME=$(basename $DATA_DIR)
export dataset=$(basename $(dirname $DATA_DIR))
CUDA_VISIBLE_DEVICES=0 python main.py -O \
--text "A high-resolution DSLR image of a full body ironman" \
--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

Run Magic123 fine stage without textural inversion, takes around ~20 mins

export RUN_ID='default-a-full-body-ironman'
export RUN_ID2='dmtet'
export DATA_DIR='data/demo/ironman'
export IMAGE_NAME='rgba.png'
export FILENAME=$(basename $DATA_DIR)
export dataset=$(basename $(dirname $DATA_DIR))
CUDA_VISIBLE_DEVICES=0 python main.py -O \
--text "A high-resolution DSLR image of a full body ironman" \
--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 \
--known_view_interval 4 \
--latent_iter_ratio 0 \
--guidance SD zero123 \
--lambda_guidance 1e-3 0.01 \
--guidance_scale 100 5 \
--rm_edge \
--bg_radius -1 \
--save_mesh

Run ablation studies

Run Magic123 with only 2D prior with textural inversion (Like RealFusion but we achieve much better performance through training stragies and the coarse-to-fine pipeline)
```
bash scripts/magic123/run_2dprior.sh 0 default dmtet data/realfusion15/metal_dragon_statue rgba.png 1 1
```
Run Magic123 with only 2D prior without textural inversion (Like RealFusion but we achieve much better performance through training stragies and the coarse-to-fine pipeline)
```
bash scripts/magic123/run_2dprior_notextinv_ironman.sh 0 default 1 1
```
note: change the path and the text prompt inside the script if you wana test another example.
Run Magic123 with only 3D prior (Like Zero-1-to-3 but we achieve much better performance through training stragies and the coarse-to-fine pipeline)
```
bash scripts/magic123/run_3dprior.sh 0 default dmtet data/demo/ironman rgba.png 1 1
```

Tips and Tricks

Fix camera distance (radius_range) and FOV (fovy_range) and tune the camera polar range (theta_range). Note it is better to keep camera jittering to reduce grid artifacts.
Smaller range of time steps for the defusion noise (t_range). We find [0.2, 0.6] gives better performance for image-to-3D tasks.
Using normals as latent in the first 2000 improves generated geometry a bit gernerally (but not always). We turn on this for Magic123 corase stage in the script --normal_iter_ratio 0.2
We erode segmentation edges (makes the segmentation map 2 pixels shrinked towards internal side) to remove artifacts due to segmentation erros. This is turned on in the fine stage in magic123 in the script through --rm_edge
Other general tricks such as improved texural inversion, advanced diffusion prior (DeepFloyd, SD-XL), stronger 3D prior (Zero123-XL), and larger batch size can be adopted as well but not studied in this work.
textural inversion is not very necessary for well-known things (e.g. ironman) and easily described textures and geoemtries, since pure texts contains these texture information and will be understood by diffusion models. We use textural inversion by default in all experiments.

Acknowledgement

This work is build upon Stable DreamFusion, many thanks to the author Kiui Jiaxiang Tang and many other contributors.

Stable DreamFusion

@misc{stable-dreamfusion,
    Author = {Jiaxiang Tang},
    Year = {2022},
    Note = {https://github.com/ashawkey/stable-dreamfusion},
    Title = {Stable-dreamfusion: Text-to-3D with Stable-diffusion}
}

We also get inspirations from a list of amazing research works and open-source projects, thanks a lot to all the authors for sharing!

DreamFusion: Text-to-3D using 2D Diffusion

@article{poole2022dreamfusion,
    author = {Poole, Ben and Jain, Ajay and Barron, Jonathan T. and Mildenhall, Ben},
    title = {DreamFusion: Text-to-3D using 2D Diffusion},
    journal = {arXiv},
    year = {2022},
}

Magic3D: High-Resolution Text-to-3D Content Creation

@inproceedings{lin2023magic3d,
   title={Magic3D: High-Resolution Text-to-3D Content Creation},
   author={Lin, Chen-Hsuan and Gao, Jun and Tang, Luming and Takikawa, Towaki and Zeng, Xiaohui and Huang, Xun and Kreis, Karsten and Fidler, Sanja and Liu, Ming-Yu and Lin, Tsung-Yi},
   booktitle={IEEE Conference on Computer Vision and Pattern Recognition ({CVPR})},
   year={2023}
 }

Zero-1-to-3: Zero-shot One Image to 3D Object

@misc{liu2023zero1to3,
    title={Zero-1-to-3: Zero-shot One Image to 3D Object},
    author={Ruoshi Liu and Rundi Wu and Basile Van Hoorick and Pavel Tokmakov and Sergey Zakharov and Carl Vondrick},
    year={2023},
    eprint={2303.11328},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

RealFusion: 360° Reconstruction of Any Object from a Single Image

@inproceedings{melaskyriazi2023realfusion,
    author = {Melas-Kyriazi, Luke and Rupprecht, Christian and Laina, Iro and Vedaldi, Andrea},
    title = {RealFusion: 360 Reconstruction of Any Object from a Single Image},
    booktitle={CVPR}
    year = {2023},
    url = {https://arxiv.org/abs/2302.10663},
}

Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior

@article{tang2023make-it-3d,
    title={Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior},
    author={Tang, Junshu and Wang, Tengfei and Zhang, Bo and Zhang, Ting and Yi, Ran and Ma, Lizhuang and Chen, Dong},
    journal={arXiv preprint arXiv:2303.14184},
    year={2023}
}

Stable Diffusion and the diffusers library.

@misc{rombach2021highresolution,
    title={High-Resolution Image Synthesis with Latent Diffusion Models},
    author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
    year={2021},
    eprint={2112.10752},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@misc{von-platen-etal-2022-diffusers,
    author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Thomas Wolf},
    title = {Diffusers: State-of-the-art diffusion models},
    year = {2022},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/huggingface/diffusers}}
}

Cite

If you find this work useful, a citation will be appreciated via:

@article{qian2023magic123,
  title={Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors},
  author={Qian, Guocheng and Mai, Jinjie and Hamdi, Abdullah and Ren, Jian and Siarohin, Aliaksandr and Li, Bing and Lee, Hsin-Ying and Skorokhodov, Ivan and Wonka, Peter and Tulyakov, Sergey and others},
  journal={arXiv preprint arXiv:2306.17843},
  year={2023}
}