Official PyTorch implementation of "BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation" (NeurIPS 2021)

Related tags

Deep Learning generative-adversarial-network gan style-transfer image-generation
Overview

BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
Official PyTorch implementation of the NeurIPS 2021 paper

teaser

Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng

Y-tech, Kuaishou Technology

Project page | Paper

Abstract: Generative Adversarial Networks (GANs) have made a dramatic leap in high-fidelity image synthesis and stylized face generation. Recently, a layer-swapping mechanism has been developed to improve the stylization performance. However, this method is incapable of fitting arbitrary styles in a single model and requires hundreds of style-consistent training images for each style. To address the above issues, we propose BlendGAN for arbitrary stylized face generation by leveraging a flexible blending strategy and a generic artistic dataset. Specifically, we first train a self-supervised style encoder on the generic artistic dataset to extract the representations of arbitrary styles. In addition, a weighted blending module (WBM) is proposed to blend face and style representations implicitly and control the arbitrary stylization effect. By doing so, BlendGAN can gracefully fit arbitrary styles in a unified model while avoiding case-by-case preparation of style-consistent training images. To this end, we also present a novel large-scale artistic face dataset AAHQ. Extensive experiments demonstrate that BlendGAN outperforms state-of-the-art methods in terms of visual quality and style diversity for both latent-guided and reference-guided stylized face synthesis.

Updates

✔️ (2021-11-19) Inference code and pretrained models have been released!

000041 000021

Pre-trained Models

You can download the following pretrained models to ./pretrained_models:

Model Discription
blendgan BlendGAN model (together with style_encoder)
psp_encoder PSP Encoder model
style_encoder Individual Style Encoder model (optional)

Inference

1. Generate image pairs with random face codes

  • for latent-guided generation, run:
python generate_image_pairs.py --size 1024 --pics N_PICS --ckpt ./pretrained_models/blendgan.pt --outdir results/generated_pairs/latent_guided/
  • for reference-guided generation, run:
python generate_image_pairs.py --size 1024 --pics N_PICS --ckpt ./pretrained_models/blendgan.pt --style_img ./test_imgs/style_imgs/100036.png --outdir results/generated_pairs/reference_guided/

2. Style tranfer with given face images

python style_transfer_folder.py --size 1024 --ckpt ./pretrained_models/blendgan.pt --psp_encoder_ckpt ./pretrained_models/psp_encoder.pt --style_img_path ./test_imgs/style_imgs/ --input_img_path ./test_imgs/face_imgs/ --outdir results/style_transfer/

3. Generate interpolation videos

python gen_video.py --size 1024 --ckpt ./pretrained_models/blendgan.pt --psp_encoder_ckpt ./pretrained_models/psp_encoder.pt --style_img_path ./test_imgs/style_imgs/ --input_img_path ./test_imgs/face_imgs/ --outdir results/inter_videos/

Bibtex

If you use this code for your research, please cite our paper:

@inproceedings{liu2021blendgan,
    title = {BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation},
    author = {Liu, Mingcong and Li, Qiang and Qin, Zekui and Zhang, Guoxin and Wan, Pengfei and Zheng, Wen},
    booktitle = {Advances in Neural Information Processing Systems},
    year = {2021}
}

Credits

StyleGAN2 model and implementation:
https://github.com/rosinality/stylegan2-pytorch
Copyright (c) 2019 Kim Seonghyeon
License (MIT) https://github.com/rosinality/stylegan2-pytorch/blob/master/LICENSE

IR-SE50 model and implementations:
https://github.com/TreB1eN/InsightFace_Pytorch
Copyright (c) 2018 TreB1eN
License (MIT) https://github.com/TreB1eN/InsightFace_Pytorch/blob/master/LICENSE

pSp model and implementation:
https://github.com/eladrich/pixel2style2pixel
Copyright (c) 2020 Elad Richardson, Yuval Alaluf
License (MIT) https://github.com/eladrich/pixel2style2pixel/blob/master/LICENSE

Please Note:

Acknowledgements

We sincerely thank all the reviewers for their comments. We also thank Zhenyu Guo for help in preparing the comparison to StarGANv2. This code borrows heavily from the pytorch re-implementation of StyleGAN2 by rosinality.

Comments
  • Add Weights & Biases Image Logging

    Add Weights & Biases Image Logging

    Hi @onion-liu

    I am an ML Engineer at Weights & Biases and I wanted to know if you were actively reviewing PRs at the moment? We would love to make a PR to add Weights & Biases' image logging of images if you had time to review it?

    We have built integrations into transformers, YOLOv5, PyTorch Lightning etc so we should be able to make a quick and clean PR that shouldn't take too much time for you to review :)

    opened by morganmcg1 4
  • The implementation of WBM

    The implementation of WBM

    I really like your paper!! Thank you for sharing your work. I am a beginner in GAN and image editing and interested in your implementation of WBM module which is used to blend latent codes in two domain. I want to ask would you like to release your code about WBM?☺ Thanks.

    opened by zhanjiahui 2
  • google colab workbook

    google colab workbook

    Here is a collab workbook for you or others :

    • https://colab.research.google.com/github/3dsf/BlendGAN/blob/main/BlendGAN_3dsf.ipynb

    It won't display the results in the book, but should download results when cell(s) have completed running.

    It is written in a way, that users should be able to change folders/files with minimal effort.

    note: video example is not currently working due to system ram overflow #3

    opened by 3dsf 2
  • Video Example overflowing ram

    Video Example overflowing ram

    I've tried locally and on colab. The ram over flows on writing of the video -- locally I have 32 GB.

    I might try writing a different video implementation for you in a week or two, but maybe you'll find a fix sooner.

    opened by 3dsf 2
  • About the style-latent D

    About the style-latent D

    Hi, thanks for your awesome work!

    I read the BlendGAN paper, and I'm confused about the role the style-latent discriminator plays in the network. I wonder that will the results be negatively affected a lot if I remove this module away from the network.

    Do you have any intuition or idea behind the design of style-latent D?

    Looking forward to hearing from you! Thanks again.

    opened by silence-tang 0
  • Not good result

    Not good result

    Hi, I use your model to run reference-guided generation. However, I give a strong 3D style reference, while the result seems to be an oil panting just like this: image

    Is there anything wrong with me?

    opened by rainsoulsrx 0
  • Add Docker environment & web demo

    Add Docker environment & web demo

    Hey @onion-liu! 👋

    This pull request makes it possible to run your model inside a Docker environment, which makes it easier for other people to run it. We're using an open source tool called Cog to make this process easier.

    This also means we can make a web page where other people can try out your model! We have included the face alignment to the input images. View it here: https://replicate.ai/onion-liu/blendgan

    Do claim your page here so you can own the page, customise the Example gallery as you like, and push any future update to the web demo. We'll feature it on our website and tweet about it too.

    In case you're wondering who I am, I'm from Replicate, where we're trying to make machine learning reproducible. We got frustrated that we couldn't run all the really interesting ML work being done. So, we're going round implementing models we like. 😊

    opened by chenxwh 0
Owner
onion
GAN, Style Transfer, Image Enhancement, Infrared Image, HDR
onion
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