dreamfields-torch (WIP)

A pytorch implementation of dreamfields as described in Zero-Shot Text-Guided Object Generation with Dream Fields.

An example of a generated neural field by prompt "cthulhu" viewed in real-time:

cthulhu.mp4

Install

The code framework is based on torch-ngp.

git clone https://github.com/ashawkey/dreamfields-torch.git
cd dreamfields-torch

Install with pip

pip install -r requirements.txt

# (optional) install the tcnn backbone
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

Build extension (optional)

By default, we use load to build the extension at runtime. However, this may be inconvenient sometimes. Therefore, we also provide the setup.py to build each extension:

# install all extension modules
bash scripts/install_ext.sh

# if you want to install manually, here is an example:
cd raymarching
python setup.py build_ext --inplace # build ext only, do not install (only can be used in the parent directory)
pip install . # install to python path (you still need the raymarching/ folder, since this only install the built extension.)

Tested environments

• Ubuntu 20 with torch 1.10 & CUDA 11.3 on a TITAN RTX.
• Windows 10 with torch 1.11 & CUDA 11.3 on a RTX 3070.

Currently, --ff only supports GPUs with CUDA architecture >= 70. For GPUs with lower architecture, --tcnn can still be used, but the speed will be slower compared to more recent GPUs.

Usage

First time running will take some time to compile the CUDA extensions.

# text-guided generation
python main_nerf.py --text "cthulhu" --workspace trial --cuda_ray --fp16

# use the GUI
python main_nerf.py --text "cthulhu" --workspace trial --cuda_ray --fp16 --gui

# [experimental] image-guided generation (also use the CLIP loss)
python main_nerf.py --image /path/to/image --workspace trial --cuda_ray --fp16

check the scripts directory for more provided examples.

Difference from the original implementation

• Mip-nerf is not implemented, currently only the original nerf is supported.
• Sampling poses with an elevation range in [-30, 30] degrees, instead of fixed at 30 degree.
• Use the origin loss.

Update Logs

• 5.18: major update.
• 3.16: basic reproduction.

Acknowledgement

• The great paper and official JAX implementation of dreamfields:

  @article{jain2021dreamfields,
      author = {Jain, Ajay and Mildenhall, Ben and Barron, Jonathan T. and Abbeel, Pieter and Poole, Ben},
      title = {Zero-Shot Text-Guided Object Generation with Dream Fields},
      journal = {arXiv},
      month = {December},
      year = {2021},
  }   
  

• The GUI is developed with DearPyGui.