@inproceedings{preechakul2021diffusion,
title={Diffusion Autoencoders: Toward a Meaningful and Decodable Representation},
author={Preechakul, Konpat and Chatthee, Nattanat and Wizadwongsa, Suttisak and Suwajanakorn, Supasorn},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022},
}
Checkpoints ought to be put into a separate directory checkpoints.
Download the checkpoints and put them into checkpoints directory. It should look like this:
You can also download from the original sources, and use our provided codes to package them as LMDB files.
Original sources for each dataset is as follows:
We provide scripts for training & evaluate DDIM and DiffAE (including latent DPM) on the following datasets: FFHQ128, FFHQ256, Bedroom128, Horse128, Celeba64 (D2C's crop).
Usually, the evaluation results (FID's) will be available in eval directory.
Note: Most experiment requires at least 4x V100s during training the DPM models while requiring 1x 2080Ti during training the accompanying latent DPM.
FFHQ128
# diffae
python run_ffhq128.py
# ddim
python run_ffhq128_ddim.py
A classifier (for manipulation) can be trained using:
python run_ffhq128_cls.py
FFHQ256
We only trained the DiffAE due to high computation cost.
This requires 8x V100s.
sbatch run_ffhq256.py
After the task is done, you need to train the latent DPM (requiring only 1x 2080Ti)
python run_ffhq256_latent.py
A classifier (for manipulation) can be trained using:
python run_ffhq256_cls.py
Bedroom128
# diffae
python run_bedroom128.py
# ddim
python run_bedroom128_ddim.py
Horse128
# diffae
python run_horse128.py
# ddim
python run_horse128_ddim.py
