Official PyTorch implementation of BlobGAN: Spatially Disentangled Scene Representations

BlobGAN: Spatially Disentangled Scene Representations
_{Official PyTorch Implementation}

Paper | Project Page | Video | Interactive Demo

This repository contains:

• 🚂 Pre-trained BlobGAN models on three datasets: bedrooms, conference rooms, and a combination of kitchens, living rooms, and dining rooms
• 💻 Code based on PyTorch Lightning ⚡ and Hydra 🐍 which fully supports CPU, single GPU, or multi GPU/node training and inference

We also provide an 📓 interactive demo notebook to help get started using our model. Download this notebook and run it on your own Python environment, or test it out on Colab. You can:

• 🖌️ ️ Generate and edit realistic images with an interactive UI
• 📹 Create animated videos showing off your edited scenes

And, coming soon!

• 📸 Upload your own image and convert it into blobs!
• 🧬 Programmatically modify images and reproduce results from our paper

Setup

Run the commands below one at a time to download the latest version of the BlobGAN code, create a Conda environment, and install necessary packages and utilities.

git clone https://github.com/dave-epstein/blobgan.git
mkdir -p blobgan/logs/wandb
conda create -n blobgan python=3.9
conda activate blobgan
conda install pytorch=1.11.0 torchvision=0.12.0 torchaudio cudatoolkit=11.3 -c pytorch
conda install cudatoolkit-dev=11.3 -c conda-forge
pip install tqdm==4.64.0 hydra-core==1.1.2 omegaconf==2.1.2 clean-fid==0.1.23 wandb==0.12.11 ipdb==0.13.9 lpips==0.1.4 einops==0.4.1 inputimeout==1.0.4 pytorch-lightning==1.5.10 matplotlib==3.5.2 mpl_interactions[jupyter]==0.21.0
wget -q --show-progress https://github.com/ninja-build/ninja/releases/download/v1.10.2/ninja-linux.zip
sudo unzip -q ninja-linux.zip -d /usr/local/bin/
sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force

Running pretrained models

See scripts/load_model.py for an example of how to load a pre-trained model (using the provided load_model function, which can be called from elsewhere) and generate images with it. You can also run the file from the command line to generate images and save them to disk. For example:

python scripts/load_model.py --model_name bed --dl_dir models --save_dir out --n_imgs 32 --save_blobs --label_blobs

See the command's help for more details and options: scripts/load_model.py --help

Training your own model

Before training your model, you'll need to modify src/configs/experiments/local.yaml to include your WandB information and machine-specific configuration (such as path to data -- dataset.path or dataset.basepath -- and number of GPUs trainer.gpus). To turn off logging entirely, pass logger=false, or to only log to disk but not write to server, pass wandb.offline=true. Our code currently only supports WandB logging.

Here's an example command which will train a model on LSUN bedrooms. We list the configuration modules to load for this experiment (blobgan, local, jitter) and then specify any other options as we desire. For example, if we wanted to train a model without jitter, we could just remove that module from the experiments array.

python src/run.py +experiment=[blobgan,local,jitter] wandb.name='10-blob BlobGAN on bedrooms'

In some shells, you may need to add extra quotes around some of these options to prevent them from being parsed immediately on the command line.

Train on the LSUN category of your choice by passing in dataset.category, e.g. dataset.category=church. Tackle multiple categories at once with dataset=multilsun and dataset.categories=[kitchen,bedroom].

You can also train on any collection of images by selecting dataset=imagefolder and passing in the path. The code expects at least a subfolder named train and optional subfolders named validate and test. The below command also illustrates how to set arbitrary options using Hydra syntax, such as turning off FID logging or changing dataloader batch size:

python src/run.py +experiment=[blobgan,local,jitter] wandb.name='20-blob BlobGAN on Places' dataset.dataloader.batch_size=24 +model.log_fid_every_epoch=false dataset=imagefolder +dataset.path=/path/to/places/ model.n_features=20

Other parameters of interest are likely trainer.log_every_n_steps and model.log_images_every_n_steps which control frequency of logging scalars and images, and checkpoint.every_n_train_steps and checkpoint.save_top_k which dictate checkpoint saving frequency and decide how many most recent checkpoints to keep (-1 means keep everything).

Citation

If our code or models aided your research, please cite our paper:

@misc{epstein2022blobgan,
      title={BlobGAN: Spatially Disentangled Scene Representations},
      author={Dave Epstein and Taesung Park and Richard Zhang and Eli Shechtman and Alexei A. Efros},
      year={2022},
      eprint={2205.02837},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}		

Code acknowledgments

This repository is built on top of rosinality's excellent PyTorch re-implementation of StyleGAN2 and Bill Peebles' GANgealing codebase.