README.md.backup

# Official implementation of Diffusion Autoencoders

A CVPR 2022 paper:

> Preechakul, Konpat, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn. 2021. “Diffusion Autoencoders: Toward a Meaningful and Decodable Representation.” arXiv [cs.CV]. arXiv. http://arxiv.org/abs/2111.15640.

## Usage

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`

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


<style type="text/css">
    img {
        height: 256px;
    }
</style>

| ![](imgs/sandy.JPG)  |  ![](imgs_align/sandy.png) |  ![](imgs_manipulated/sandy-wavyhair.png) |
|---|---|---|


### Checkpoints

We provide checkpoints for the following models:

1. DDIM: **FFHQ128** ([72M](https://drive.google.com/drive/folders/1-J8FPNZOQxSqpfTpwRXawLi2KKGL1qlK?usp=sharing), [130M](https://drive.google.com/drive/folders/17T5YJXpYdgE6cWltN8gZFxRsJzpVxnLh?usp=sharing)), [**Bedroom128**](https://drive.google.com/drive/folders/19s-lAiK7fGD5Meo5obNV5o0L3MfqU0Sk?usp=sharing), [**Horse128**](https://drive.google.com/drive/folders/1PiC5JWLcd8mZW9cghDCR0V4Hx0QCXOor?usp=sharing)
2. DiffAE (autoencoding only): [**FFHQ256**](https://drive.google.com/drive/folders/1hTP9QbYXwv_Nl5sgcZNH0yKprJx7ivC5?usp=sharing), **FFHQ128** ([72M](https://drive.google.com/drive/folders/15QHmZP1G5jEMh80R1Nbtdb4ZKb6VvfII?usp=sharing), [130M](https://drive.google.com/drive/folders/1UlwLwgv16cEqxTn7g-V2ykIyopmY_fVz?usp=sharing)), [**Bedroom128**](https://drive.google.com/drive/folders/1okhCb1RezlWmDbdEAGWMHMkUBRRXmey0?usp=sharing), [**Horse128**](https://drive.google.com/drive/folders/1Ujmv3ajeiJLOT6lF2zrQb4FimfDkMhcP?usp=sharing)
3. DiffAE (with latent DPM, can sample): [**FFHQ256**](https://drive.google.com/drive/folders/1MonJKYwVLzvCFYuVhp-l9mChq5V2XI6w?usp=sharing), [**FFHQ128**](https://drive.google.com/drive/folders/1E3Ew1p9h42h7UA1DJNK7jnb2ERybg9ji?usp=sharing), [**Bedroom128**](https://drive.google.com/drive/folders/1okhCb1RezlWmDbdEAGWMHMkUBRRXmey0?usp=sharing), [**Horse128**](https://drive.google.com/drive/folders/1Ujmv3ajeiJLOT6lF2zrQb4FimfDkMhcP?usp=sharing)
4. DiffAE's classifiers (for manipulation): [**FFHQ256's latent on CelebAHQ**](https://drive.google.com/drive/folders/1QGkTfvNhgi_TbbV8GbX1Emrp0lStsqLj?usp=sharing), [**FFHQ128's latent on CelebAHQ**](https://drive.google.com/drive/folders/1E3Ew1p9h42h7UA1DJNK7jnb2ERybg9ji?usp=sharing)

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://drive.google.com/drive/folders/1ww7itaSo53NDMa0q-wn-3HWZ3HHqK1IK?usp=sharing)
- [CelebAHQ](https://drive.google.com/drive/folders/1SX3JuVHjYA8sA28EGxr_IoHJ63s4Btbl?usp=sharing) 
- [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
```