This is the code for the paper
Deep Feature Consistent Variational Autoencoder
The paper trained a Variational Autoencoder (VAE) model for face image generation. In addition, it provided a method to manipluate facial attributes by using attribute-specific vector.
- Pretrained model trained on CelebA dataset
- Code for training on GPU
- Code for different analysis
Our implementation is based on Torch and several dependencies.
After installing Torch according to this tutorial, use following code to install dependencies:
sudo apt-get install libprotobuf-dev protobuf-compiler
luarocks install loadcaffe
luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec
luarocks install nngraph
sudo apt-get install libmatio2
luarocks install matio
luarocks install manifold
sudo apt-get install libatlas3-base # for manifold
we use a NVIDIA GPU for training and testing, so you also need install GPU related packages
luarocks install cutorch
luarocks install cunn
luarocks install cudnn
cd celebA
Download img_align_celeba.zip from http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html under the link "Align&Cropped Images".
Download list_attr_celeba.txt for annotation.
unzip img_align_celeba.zip; cd ..
DATA_ROOT=celebA th data/crop_celebA.lua
We need pretrained VGG-19 to compute feature perceptual loss.
cd data/pretrained && bash download_models.sh && cd ../..
Open a new terminal and start the server for display images in the browser
th -ldisplay.start 8000 0.0.0.0
The images can be seen at http://localhost:8000 in the browser
Training with feature perceptual loss.
DATA_ROOT=celebA th main_cvae_content.lua
Training with pixel-by-pixel loss.
DATA_ROOT=celebA th main_cvae.lua
mkdir checkpoints; cd checkpoints
We provide both Encoder and Decoder
cvae_content_123_encoder.t7 and cvae_content_123_decoder.t7 trained with relu1_1, relu2_1, relu3_1 in VGG.
Reconstruction with CelebA dataset:
DATA_ROOT=celebA reconstruction=1 th generate.lua
Face images randomly generated from latent variables:
DATA_ROOT=celebA reconstruction=0 th generate.lua
Following are some examples:
## Linear interpolation between two face imagesth linear_walk_two_images.lua
First preprocess the celebA dataset annotations to separate the dataset to two parts for each attribute, indicating whether containing the specific attribute or not.
cd celebA
python get_binary_attr.py
# should generate file: 'celebA/attr_binary_list.txt'
cd ..
th linear_walk_attribute_vector.lua
Here are some examples:
The code is based on neural-style, dcgan.torch, VAE-Torch and texture_nets.