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https://github.com/akanimax/T2F

T2F: text to face generation using Deep Learning
https://github.com/akanimax/T2F

adversarial-machine-learning gan generative-adversarial-network progressively-growing-gan text-to-image

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T2F: text to face generation using Deep Learning

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README 

        
# ! Attention !

# This project is unfortunately not being worked upon

### Please head over to the following much cooler project that takes the idea of Text-2-Image generation to the next level:



DallE: [Original](https://github.com/openai/DALL-E) [PyTorch](https://github.com/lucidrains/DALLE-pytorch)



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# :star: [NEW] :star:

# T2F - 2.0 Teaser (coming soon ...)



2.0 Teaser




## Please note that all the faces in the above samples are generated ones. The T2F 2.0 will be using MSG-GAN for the image generation module instead of ProGAN. Please refer [link](https://github.com/akanimax/BMSG-GAN) for more info about MSG-GAN. This update to the repository will be comeing soon :+1:.



# T2F

Text-to-Face generation using Deep Learning. This project combines two of the recent architectures  StackGAN  and  ProGAN  for synthesizing faces from textual descriptions.


The project uses  Face2Text  dataset which contains 400 facial images and textual captions for each of them. The data can be obtained by contacting either the **RIVAL** group or the authors of the aforementioned paper.



Some Examples:


Examples



Architecture: 


Architecture Diagram

The textual description is encoded into a summary vector using an LSTM network. The summary vector, i.e. Embedding (psy_t) as shown in the diagram is passed through the Conditioning Augmentation block (a single linear layer) to obtain the textual part of the latent vector (uses VAE like reparameterization technique) for the GAN as input. The second part of the latent vector is random gaussian noise. The latent vector so produced is fed to the generator part of the GAN, while the embedding is fed to the final layer of the discriminator for conditional distribution matching. The training of the GAN progresses exactly as mentioned in the ProGAN paper; i.e. layer by layer at increasing spatial resolutions. The new layer is introduced using the fade-in technique to avoid destroying previous learning.



## Running the code:

The code is present in the `implementation/` subdirectory. The implementation is done using the  PyTorch framework. So, for running this code, please install `PyTorch version 0.4.0` before continuing.



__Code organization:__ 


`configs`: contains the configuration files for training the network. (You can use any one, or create your own) 


`data_processing`: package containing data processing and loading modules 


`networks`: package contains network implementation 


`processed_annotations`: directory stores output of running `process_text_annotations.py` script 


`process_text_annotations.py`: processes the captions and stores output in `processed_annotations/` directory. (no need to run this script; the pickle file is included in the repo.) 


`train_network.py`: script for running the training the network 



__Sample configuration:__



    # All paths to different required data objects

    images_dir: "../data/LFW/lfw"

    processed_text_file: "processed_annotations/processed_text.pkl"

    log_dir: "training_runs/11/losses/"

    sample_dir: "training_runs/11/generated_samples/"

    save_dir: "training_runs/11/saved_models/"



    # Hyperparameters for the Model

    captions_length: 100

    img_dims:

      - 64

      - 64



    # LSTM hyperparameters

    embedding_size: 128

    hidden_size: 256

    num_layers: 3  # number of LSTM cells in the encoder network



    # Conditioning Augmentation hyperparameters

    ca_out_size: 178



    # Pro GAN hyperparameters

    depth: 5

    latent_size: 256

    learning_rate: 0.001

    beta_1: 0

    beta_2: 0

    eps: 0.00000001

    drift: 0.001

    n_critic: 1



    # Training hyperparameters:

    epochs:

      - 160

      - 80

      - 40

      - 20

      - 10

    

    # % of epochs for fading in the new layer

    fade_in_percentage:

      - 85

      - 85

      - 85

      - 85

      - 85



    batch_sizes:

      - 16

      - 16

      - 16

      - 16

      - 16



    num_workers: 3

    feedback_factor: 7  # number of logs generated per epoch

    checkpoint_factor: 2  # save the models after these many epochs

    use_matching_aware_discriminator: True  # use the matching aware discriminator



Use the `requirements.txt` to install all the dependencies for the project. 

    

    $ workon [your virtual environment]

    $ pip install -r requirements.txt



__Sample run:__



    $ mkdir training_runs

    $ mkdir training_runs/generated_samples training_runs/losses training_runs/saved_models

    $ train_network.py --config=configs/11.comf



## Other links:

blog: https://medium.com/@animeshsk3/t2f-text-to-face-generation-using-deep-learning-b3b6ba5a5a93 


training_time_lapse video: https://www.youtube.com/watch?v=NO_l87rPDb8 


ProGAN package (Seperate library): https://github.com/akanimax/pro_gan_pytorch



## #TODO:

1.) Create a simple `demo.py` for running inference on the trained models