• Stars
    star
    131
  • Rank 275,867 (Top 6 %)
  • Language
    Python
  • License
    MIT License
  • Created over 1 year ago
  • Updated 10 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

A diffuser implementation of Zero123. Zero-1-to-3: Zero-shot One Image to 3D Object (ICCV23)

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

A HuggingFace Diffusers implementation of Zero123.

Merged into Diffusers Repo here.

Usage

Pytorch 2.0 for faster training and inference.

conda create -f environment.yml

or

conda create -n zero123-hf python=3.9
conda activate zero123-hf
pip install -r requirements.txt

Install xformer properly to enable efficient transformers.

conda install xformers -c xformers
# from source
pip install -v -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers

Run diffusers pipeline demo:

python test_zero1to3.py

Run our gradio demo for novel view synthesis:

python gradio_new.py

Training

Download Zero123's Objaverse Renderings data:

wget https://tri-ml-public.s3.amazonaws.com/datasets/views_release.tar.gz

Configure accelerator by

accelerate config

Launch training:

Follow Original Zero123, fp32, gradient checkpointing, and EMA are turned on.

accelerate launch train_zero1to3.py \
--train_data_dir /data/zero123/views_release \
--pretrained_model_name_or_path lambdalabs/sd-image-variations-diffusers \
--train_batch_size 192 \
--dataloader_num_workers 16 \
--output_dir logs \
--use_ema \
--gradient_checkpointing \
--mixed_precision no

While bf16/fp16 is also supported by running below

accelerate launch train_zero1to3.py \
--train_data_dir /data/zero123/views_release \
--pretrained_model_name_or_path lambdalabs/sd-image-variations-diffusers \
--train_batch_size 192 \
--dataloader_num_workers 16 \
--output_dir logs \
--use_ema \
--gradient_checkpointing \
--mixed_precision bf16

For monitoring training progress, we recommand wandb for its simplicity and powerful features.

wandb login

Acknowledgement

This repository is based on original Zero1to3 and popular HuggingFace diffusion framework diffusers.

Citation

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

@misc{zero123-hf,
    Author = {Xin Kong},
    Year = {2023},
    Note = {https://github.com/kxhit/zero123-hf},
    Title = {Zero123-hf: a diffusers implementation of zero123}
}

@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}
}