kszu/UniGAN

UniGAN: Unisex product design made easy

Flask app setup

1. Git fetch the latest version of this repository to bring down the up-to-date Flask app (found in the server directory
2. From the command line, cd into the server directory
3. If you haven't already installed the Python library virtualenv, run pip install virtualenv
4. Run the command python3.7 -m venv env to initiate your environment
5. Run the command source env/bin/activate to activate your virtual environment
6. From within your virtual environment, run the command python app.py. This should open up the Flask app at localhost:5000

AttGAN walkthrough

New

• We clean up the code to make it more readable! The old version is here: v1.

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AttGAN
_{TIP Nov. 2019, arXiv Nov. 2017}

TensorFlow implementation of AttGAN: Facial Attribute Editing by Only Changing What You Want

Related

• Other implementations of AttGAN

  • AttGAN-PyTorch by Yu-Jing Lin

  • AttGAN-PaddlePaddle by ceci3 and zhumanyu (AttGAN is one of the official reproduced models of PaddlePaddle)

• Closely related works

  • An excellent work built upon our code - STGAN (CVPR 2019) by Ming Liu

  • Changing-the-Memorability (CVPR 2019 MBCCV Workshop) by acecreamu

  • Fashion-AttGAN (CVPR 2019 FSS-USAD Workshop) by Qing Ping

• An unofficial demo video of AttGAN by 王一凡

Exemplar Results

• See results.md for more results, we try higher resolution and more attributes (all 40 attributes!!!)

• Inverting 13 attributes respectively

  from left to right: Input, Reconstruction, Bald, Bangs, Black_Hair, Blond_Hair, Brown_Hair, Bushy_Eyebrows, Eyeglasses, Male, Mouth_Slightly_Open, Mustache, No_Beard, Pale_Skin, Young

  

Usage

• Environment

  • Python 3.6

  • TensorFlow 1.15

  • OpenCV, scikit-image, tqdm, oyaml

  • we recommend Anaconda or Miniconda, then you can create the AttGAN environment with commands below

    conda create -n AttGAN python=3.6
    
    source activate AttGAN
    
    conda install -c anaconda tensorflow-gpu=1.15
    
    conda install -c anaconda opencv
    
    conda install -c anaconda scikit-image
    
    conda install -c anaconda tqdm
    
    conda install -c conda-forge oyaml

• Data Preparation

  • CelebA-unaligned (10.2GB, higher quality than the aligned data)

    • download the dataset

      • img_celeba.7z (move to ./data/img_celeba/img_celeba.7z): Google Drive or Baidu Netdisk

      • annotations.zip (move to ./data/img_celeba/annotations.zip): Google Drive

    • unzip and process the data

      7z x ./data/img_celeba/img_celeba.7z/img_celeba.7z.001 -o./data/img_celeba/
      
      unzip ./data/img_celeba/annotations.zip -d ./data/img_celeba/
      
      python ./scripts/align.py

• Run AttGAN

  • NOTICE: if you create a new conda environment, remember to activate it before any command

    source activate AttGAN

  • training (see examples.md for more training commands)

    CUDA_VISIBLE_DEVICES=0 \
    python train.py \
    --load_size 143 \
    --crop_size 128 \
    --model model_128 \
    --experiment_name AttGAN_128

  • testing

    • single attribute editing (inversion)

      CUDA_VISIBLE_DEVICES=0 \
      python test.py \
      --experiment_name AttGAN_128

    • multiple attribute editing (inversion) example

      CUDA_VISIBLE_DEVICES=0 \
      python test_multi.py \
      --test_att_names Bushy_Eyebrows Pale_Skin \
      --experiment_name AttGAN_128

    • attribute sliding example

      CUDA_VISIBLE_DEVICES=0 \
      python test_slide.py \
      --test_att_name Pale_Skin \
      --test_int_min -2 \
      --test_int_max 2 \
      --test_int_step 0.5 \
      --experiment_name AttGAN_128

  • loss visualization

    CUDA_VISIBLE_DEVICES='' \
    tensorboard \
    --logdir ./output/AttGAN_128/summaries \
    --port 6006

  • convert trained model to .pb file

    python to_pb.py --experiment_name AttGAN_128

• Using Trained Weights

  • alternative trained weights (move to ./output/*.zip)

    • AttGAN_128.zip (987.5MB)

      • including G, D, and the state of the optimizer

    • AttGAN_128_generator_only.zip (161.5MB)

      • G only

    • AttGAN_384_generator_only.zip (91.1MB)

  • unzip the file (AttGAN_128.zip for example)

    unzip ./output/AttGAN_128.zip -d ./output/

  • testing (see above)

• Example for Custom Dataset

  • AttGAN-Cartoon

Citation

If you find AttGAN useful in your research work, please consider citing:

@ARTICLE{8718508,
author={Z. {He} and W. {Zuo} and M. {Kan} and S. {Shan} and X. {Chen}},
journal={IEEE Transactions on Image Processing},
title={AttGAN: Facial Attribute Editing by Only Changing What You Want},
year={2019},
volume={28},
number={11},
pages={5464-5478},
keywords={Face;Facial features;Task analysis;Decoding;Image reconstruction;Hair;Gallium nitride;Facial attribute editing;attribute style manipulation;adversarial learning},
doi={10.1109/TIP.2019.2916751},
ISSN={1057-7149},
month={Nov},}