recovery_likelihood
Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma
Requirements
Experiments can be run on a single GPU or Google Cloud TPU v3-8.
Requires python >= 3.5. To install dependencies:
pip install -r requirements.txt
To compute FID/inception scores, download the pre-computed statistics of datasets from:
https://drive.google.com/file/d/1QOLyYHESflcdZu8CsBLZohZzC95HyukK/view?usp=sharing, unzip the file and put
the folder in this repo.
Train with 1 GPU
CIFAR10
python main.py --num_res_blocks=8 --n_batch_train=256
CelebA
python main.py --problem=celeba --num_res_blocks=6 --beta_1=0.5 --batch_size=128
LSUN church_outdoor 64x64 / LSUN bedroom 64x64
python main.py --problem=[lsun_church64/lsun_bedroom64] --batch_size=128
LSUN church_outdoor 128x128
python main.py --problem=lsun_church128 --beta_1=0.5
LSUN bedroom 128x128
python main.py --problem=lsun_bedroom128 --beta_1=0.5 --num_res_blocks=5
Compute full FID / IS scores after training on CIFAR10
python main.py --eval --num_res_blocks=8 --noise_scale=0.99 --fid_n_batch=2000
For faster training, reduce the value of num_res_blocks.
Train with Google Cloud TPU
Add --tpu=True to the above scripts for 1 GPU. Also need to set --tpu_name and --tpu_zone as shown in Google Cloud.
Pretrained models
https://drive.google.com/file/d/1eneA6T5jQIyVFLFSOrSfJvDeUJJMh9xk/view?usp=sharing
This code is for T6 setting. Will upload T1k setting soon!
Citation
If you find our work helpful to your research, please cite:
@article{gao2020learning,
title={Learning Energy-Based Models by Diffusion Recovery Likelihood},
author={Gao, Ruiqi and Song, Yang and Poole, Ben and Wu, Ying Nian and Kingma, Diederik P},
journal={arXiv preprint arXiv:2012.08125},
year={2020}
}
