To switch attention mechanisms, change the BLOCK_TYPE in model.py line 289.
The code this project was based can be found at link.
The research paper can be found here.

The datasets used in the paper can be found at link.

The code is structured as follows:

• models.py: all the models' structure definition.

• operation.py: the helper functions and data loading methods during training.

• train.py: the main entry of the code, execute this file to train the model, the intermediate results and checkpoints will be automatically saved periodically into a folder "train_results".

• eval.py: generates images from a trained generator into a folder, which can be used to calculate FID score.

• benchmarking: the functions we used to compute FID are located here, it automatically downloads the pytorch official inception model.

• lpips: this folder contains the code to compute the LPIPS score, the inception model is also automatically download from official location.

• scripts: this folder contains many scripts you can use to play around the trained model. Including:

  1. style_mix.py: style-mixing as introduced in the paper;
  2. generate_video.py: generating a continuous video from the interpolation of generated images;
  3. find_nearest_neighbor.py: given a generated image, find the closest real-image from the training set;
  4. train_backtracking_one.py: given a real-image, find the latent vector of this image from a trained Generator.

Place all your training images in a folder, and simply call

python train.py --path /path/to/RGB-image-folder

You can also see all the training options by:

python train.py --help

The code will automatically create a new folder (you have to specify the name of the folder using --name option) to store the trained checkpoints and intermediate synthesis results.

Once finish training, you can generate 100 images (or as many as you want) by:

cd ./train_results/name_of_your_training/
python eval.py --n_sample 100