HyperDiffusion

Official code repository of "HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion" @ ICCV 2023

Paper | Project website | Video | Data

News

I'll release rest of the weights/checkpoints after post-refactor tests are complete. You can see here for what's uploaded.

• [06.09.2023] Code and airplane weights/checkpoints released

Method Overview

Dependencies

• Tested on Ubuntu 20.04
• Python 3.7
• PyTorch 1.13.0
• CUDA 11.7
• Weights & Biases (We heavily rely on it for visualization and monitoring)

For full list please see hyperdiffusion_env.yaml file

Data

All the data needed to train and evaluate HyperDiffusion is in this Drive folder. There are three main folders there:

• Checkpoints contains trained diffusion model for each category, you'll need them for evaluation
• MLP Weights involves already overfitted MLP weights.
• Point Clouds (2048) has the set of 2048 points sampled from meshes to be used for metric calculation and baseline training.

Get Started

We have a .yaml file that you can create a conda environment from. Simply run,

conda env create --file hyperdiffusion_env.yaml
conda activate hyper-diffusion

We specify our runtime parameters using .yaml files which are inside configs folder. There are different yaml files for each category and task.

Then, download MLP Weights from our Drive and put it into mlp_weights folder. Config files assume that weights are in that folder.

For 3D, download Point Clouds (2048) folder from Drive and save its content to data folder. Eventually, data folder should look like this:

data
|-- 02691156
|-- 02691156_2048_pc
|-- 02958343
|-- 02958343_2048_pc
|-- 03001627
|-- 03001627_2048_pc
|-- animals

Note: Category id to name conversion is as follows: 02691156 -> airplane, 02958343 -> car, 03001627 -> chair

Evaluation

Download Checkpoints folder from Drive. Assign the path of that checkpoint to the best_model_save_path parameter.

to start evaluating, airplane category:

python main.py --config-name=train_plane mode=test best_model_save_path=<path/to/checkpoint>

(checkpoints coming soon!) car category:

python main.py --config-name=train_car mode=test best_model_save_path=<path/to/checkpoint>

(checkpoints coming soon!) chair category (we have special operations for chair, see our Supplementary Material for details):

python main.py --config-name=train_chair mode=test best_model_save_path=<path/to/checkpoint> test_sample_mult=2 dedup=True

(checkpoints coming soon) 4D animals category:

python main.py --config-name=train_4d_animals mode=test best_model_save_path=<path/to/checkpoint>

Training

To start training, airplane category:

python main.py --config-name=train_plane

(MLP weights coming soon) car category:

python main.py --config-name=train_car

(MLP weights coming soon) chair category:

python main.py --config-name=train_chair

(MLP weights coming soon) 4D animals category:

python main.py --config-name=train_4d_animals

We are using hydra, you can either specify parameters from corresponding yaml file or directly modify them from terminal. For instance, to change the number of epochs:

python main.py --config-name=train_plane epochs=1

Overfitting

We already provide overfitted shapes but if you want to do it yourself make sure that you put downloaded ShapeNet shapes (we applied ManifoldPlus pre-processing) into data folder. After that, we first create point clouds and then start overfitting on those point clouds; following lines do exactly that:

python siren/experiment_scripts/train_sdf.py --config-name=overfit_plane strategy=save_pc
python siren/experiment_scripts/train_sdf.py --config-name=overfit_plane

Code Map

Directories

• configs: Containing training and overfitting configs.
• data: Downloaded point cloud files including train-val-test splits go here (see Get Started)
• diffusion: Contains all the diffusion logic. Borrowed from OpenAI .
• ldm: Latent diffusion codebase for Voxel baseline. Borrowed from official LDM repo.
• mlp_weights: Includes overfitted MLP weights should be downloaded to here (see Get Started).
• siren: Modified SIREN codebase. Includes shape overfitting logic.
• static: Images for README file.
• Pointnet_Pointnet2_pytorch: Includes Pointnet2 definition and weights for 3D FID calculation.

Generated Directories

• lightning_checkpoints: This will be created once you start training for the first time. It will include checkpoints of the diffusion model, the sub-folder names will be the unique name assigned by the Weights & Biases in addition to timestamp.
• outputs: Hydra creates this folder to store the configs but we mainly send our outputs to Weights & Biases, so, it's not that special.
• orig_meshes: Here we put generated weights as .pth and sometimes generated meshes.
• wandb: Weights & Biases will create this folder to store outputs before sending them to server.

Files

Utils

• augment.py: Including some augmentation methods, though we don't use them in the main paper.
• dataset.py: WeightDataset and VoxelDataset definitions which are torch.Dataset descendants. Former one is related to our HyperDiffusion method, while the latter one is for Voxel baseline.
• hd_utils.py: Many utility methods ranging from rendering to flattening MLP weights.

Evaluation

• torchmetrics_fid.py: Modified torchmetrics fid implementation to calculate 3D-FID.
• evaluation_metrics_3d.py: Methods to calculate MMD, COV and 1-NN from DPC. Both for 3D and 4D.

Entry Point

• hyperdiffusion_env.yaml: Conda environment file (see Get Started section).
• main.py: Entry point of our codebase.

Models

• mlp_models.py: Definition of ReLU MLPs with positional encoding.
• transformer.py: GPT definition from G.pt paper.
• embedder.py: Positional encoding definition.
• hyperdiffusion.py: Definition of our method, it includes training, testing and validation logics in the form of a Pytorch Lightning module.

Training Plots

We share training plots for better reproducibility. Links take you to Weights & Biases reports. (Note: Some links sometimes don't work for unknown reasons)

Plane | Car | Chair | 4D Animals

Acknowledgments

We mainly used codebases of SIREN and G.pt papers to build our repository. We also referred to DPC for codes like evaluation metrics. We used OpenAI Guided Diffusion as our diffusion backbone. LDM codebase was useful for us to implement our voxel baseline.

Citation

If you find our work useful, please cite using the following BibTex entry:

@misc{erkoç2023hyperdiffusion,
  title={HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion}, 
  author={Ziya Erkoç and Fangchang Ma and Qi Shan and Matthias Nießner and Angela Dai},
  year={2023},
  eprint={2303.17015},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}