# Official implementation of Diffusion Autoencoders A CVPR 2022 (ORAL) paper ([paper](https://openaccess.thecvf.com/content/CVPR2022/html/Preechakul_Diffusion_Autoencoders_Toward_a_Meaningful_and_Decodable_Representation_CVPR_2022_paper.html), [site](https://diff-ae.github.io/), [5-min video](https://youtu.be/i3rjEsiHoUU)): ``` @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}, } ``` ## Usage ⚙️ Try a Colab walkthrough: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1OTfwkklN-IEd4hFk4LnweOleyDtS4XTh/view?usp=sharing) 🤗 Try a web demo: [![Replicate](https://replicate.com/cjwbw/diffae/badge)](https://replicate.com/cjwbw/diffae) Note: Since we expect a lot of changes on the codebase, please fork the repo before using. ### Prerequisites See `requirements.txt` ``` pip install -r requirements.txt ``` ### Quick start A jupyter notebook. For unconditional generation: `sample.ipynb` For manipulation: `manipulate.ipynb` For interpolation: `interpolate.ipynb` For autoencoding: `autoencoding.ipynb` Aligning your own images: 1. Put images into the `imgs` directory 2. Run `align.py` (need to `pip install dlib requests`) 3. Result images will be available in `imgs_align` directory
Original in imgs directory
 Aligned with align.py
 Using manipulate.ipynb
### Checkpoints We provide checkpoints for the following models: 1. DDIM: **FFHQ128** ([72M](https://drive.google.com/drive/folders/1-fa46UPSgy9ximKngBflgSj3u87-DLrw), [130M](https://drive.google.com/drive/folders/1-Sqes07fs1y9sAYXuYWSoDE_xxTtH4yx)), [**Bedroom128**](https://drive.google.com/drive/folders/1-_8LZd5inoAOBT-hO5f7RYivt95FbYT1), [**Horse128**](https://drive.google.com/drive/folders/10Hq3zIlJs9ZSiXDQVYuVJVf0cX4a_nDB) 2. DiffAE (autoencoding only): [**FFHQ256**](https://drive.google.com/drive/folders/1-5zfxT6Gl-GjxM7z9ZO2AHlB70tfmF6V), **FFHQ128** ([72M](https://drive.google.com/drive/folders/10bmB6WhLkgxybkhso5g3JmIFPAnmZMQO), [130M](https://drive.google.com/drive/folders/10UNtFNfxbHBPkoIh003JkSPto5s-VbeN)), [**Bedroom128**](https://drive.google.com/drive/folders/12EdjbIKnvP5RngKsR0UU-4kgpPAaYtlp), [**Horse128**](https://drive.google.com/drive/folders/12EtTRXzQc5uPHscpjIcci-Rg-OGa_N30) 3. DiffAE (with latent DPM, can sample): [**FFHQ256**](https://drive.google.com/drive/folders/1-H8WzKc65dEONN-DQ87TnXc23nTXDTYb), [**FFHQ128**](https://drive.google.com/drive/folders/11pdjMQ6NS8GFFiGOq3fziNJxzXU1Mw3l), [**Bedroom128**](https://drive.google.com/drive/folders/11mdxv2lVX5Em8TuhNJt-Wt2XKt25y8zU), [**Horse128**](https://drive.google.com/drive/folders/11k8XNDK3ENxiRnPSUdJ4rnagJYo4uKEo) 4. DiffAE's classifiers (for manipulation): [**FFHQ256's latent on CelebAHQ**](https://drive.google.com/drive/folders/117Wv7RZs_gumgrCOIhDEWgsNy6BRJorg), [**FFHQ128's latent on CelebAHQ**](https://drive.google.com/drive/folders/11EYIyuK6IX44C8MqreUyMgPCNiEnwhmI) Checkpoints ought to be put into a separate directory `checkpoints`. Download the checkpoints and put them into `checkpoints` directory. It should look like this: ``` checkpoints/ - bedroom128_autoenc - last.ckpt # diffae checkpoint - latent.ckpt # predicted z_sem on the dataset - bedroom128_autoenc_latent - last.ckpt # diffae + latent DPM checkpoint - bedroom128_ddpm - ... ``` ### LMDB Datasets We do not own any of the following datasets. We provide the LMDB ready-to-use dataset for the sake of convenience. - [FFHQ](https://1drv.ms/f/s!Ar2O0vx8sW70uLV1Ivk2pTjam1A8VA) - [CelebAHQ](https://1drv.ms/f/s!Ar2O0vx8sW70uL4GMeWEciHkHdH6vQ) **Broken links** Note: I'm trying to recover the following links. - [CelebA](https://drive.google.com/drive/folders/1HJAhK2hLYcT_n0gWlCu5XxdZj-bPekZ0?usp=sharing) - [LSUN Bedroom](https://drive.google.com/drive/folders/1O_3aT3LtY1YDE2pOQCp6MFpCk7Pcpkhb?usp=sharing) - [LSUN Horse](https://drive.google.com/drive/folders/1ooHW7VivZUs4i5CarPaWxakCwfeqAK8l?usp=sharing) The directory tree should be: ``` datasets/ - bedroom256.lmdb - celebahq256.lmdb - celeba.lmdb - ffhq256.lmdb - horse256.lmdb ``` 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: - FFHQ (https://github.com/NVlabs/ffhq-dataset) - CelebAHQ (https://github.com/switchablenorms/CelebAMask-HQ) - CelebA (https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) - LSUN (https://github.com/fyu/lsun) The conversion codes are provided as: ``` data_resize_bedroom.py data_resize_celebhq.py data_resize_celeba.py data_resize_ffhq.py data_resize_horse.py ``` Google drive: https://drive.google.com/drive/folders/1abNP4QKGbNnymjn8607BF0cwxX2L23jh?usp=sharing ## Training 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 ``` **Celeba64** This experiment can be run on 2080Ti's. ``` # diffae python run_celeba64.py ```