hqtransformer's Introduction

Locally Hierarchical Auto-Regressive Modeling for Image Generation (HQ-Transformer)

The official implementation of "Locally Hierarchical Auto-Regressive Modeling for Image Generation"

Tackgeun You, Saehoon Kim, Chiheon Kim, Doyup Lee, Bohyung Han, (NeurIPS 2022)

Requirements

We have tested our codes on the environment below

Python 3.7.10 / Pytorch 1.10.0 / torchvision 0.10.0 / CUDA 11.3 / Ubuntu 18.04 .

Please run the following command to install the necessary dependencies

pip install -r requirements.txt

Coverage of Released Codes

Implementation of HQ-VAE and HQ-Transformer
Pretrained checkpoints of HQ-VAE and HQ-Transformer
Training pipeline of HQ-VAE
Image generation and its evaluation pipeline of HQ-VAE and HQ-Transformer

HQ-Transformer Sampling demo

Refer the jupyter notebook script.

Experiment Command and Pretrained Checkpoints

Experiment commands and configurations are described here in experiment commands. We provide pretrained checkpoints of HQ-VAE and HQ-Transformers to reproduce the main results in the paper.

BibTex

@inproceedings{you2022hqtransformer,
  title={Locally Hierarchical Auto-Regressive Modeling for Image Generation},
  author={You, Tackgeun and Kim, Saehoon and Kim, Chiheon and Lee, Doyup and Han, Bohyung},
  booktitle={Proceedings of the International Conference on Neural Information Processing Systems},
  year={2022}
}

License

MIT License.

Acknowledgement

Our implementation is based on rq-vae-transformer and minDALL-E. Our transformer-related implementation is inspired by minGPT. We appreciate the authors of VQGAN for making their codes available to public.

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