Tips: 

- we revised the paper and add BigGAN inversion (real condition-based images) -- 10.24.2022/ 11.16.2022 (v2)

- we have upgraded the code to enhance the inversion performance and also revised the paper for easy to follow. -- 6.6.2022 (v1)

This is the official code release for "Diverse Adaptive Encoder for Deep GAN Inversion".

The code contains a set of encoders that match pre-trained GANs (PGGAN, StyleGAN1, StyleGAN2, BigGAN). BTW, DSE can match other GANs in the same way.

• training encoder with center attentions (scale for align images)

python E_align.py

• training encoder with Grad-CAM-based attentions (scale for misalign images)

python E_mis_align.py

• embedding real images to latent space (using StyleGANv1 and w).

  a. You can put real images at './checkpoint/realimg_file/' (default file as args.img_dir)

  b. You should load pre-trained Encoder at './checkpoint/E/E_blur(case2)_styleganv1_FFHQ_state_dict.pth'

  c. Then run:

python embedding_img.py

• discovering attribute directions with latent space : embedded_img_processing.py

Note: Pre-trained Model should be download first , and default save to './chechpoint/'

• validate performance (Pre-trained GANs and baseline)

  1. using generations.py to generate reconstructed images (generate GANs images if needed)
  2. Files in the directory "./baseline/" could help you to quickly format images and latent vectors (w).
  3. Put comparing images to different files, and run comparing-baseline.py

• ablation study : look at ''./ablations-study/''

• Case 1: Training most pre-trained GANs with encoders. at './model/E/E.py' (quickly converge for reconstructed GANs' image)
• Case 2: Training StyleGANv1 on FFHQ for ablation study and real face image process at './model/E/E_Blur.py' (margin blur and more GPU memory for pixels gradients)

note: put pre-trained GANs weight file at ''./checkpoint/' directory

• StyleGAN_V1 (should contain 3 files: Gm, Gs, center-tensor):
  • Cat 256:
    • ./checkpoint/stylegan_V1/cat/cat256_Gs_dict.pth
    • ./checkpoint/stylegan_V1/cat/cat256_Gm_dict.pth
    • ./checkpoint/stylegan_V1/cat/cat256_tensor.pt
  • Car 256: same above
  • Bedroom 256:
• StyleGAN_V2 (Only one files : pth):
  • FFHQ 1024:
    • ./checkpoint/stylegan_V2/stylegan2_ffhq1024.pth
• PGGAN ((Only one files : pth)):
  • Horse 256:
    • ./checkpoint/PGGAN/
• BigGAN (Two files : model as .pt and config as .json ):
  • Image-Net 256:
    • ./checkpoint/biggan/256/G-256.pt
    • ./checkpoint/biggan/256/biggan-deep-256-config.json

note: different GANs should set different parameters carefully.

• choose --mtype for StyleGANv1=1, StyleGANv2=2, PGGAN=3, BIGGAN=4
• choose Encoder start_features (--z_dim) carefully, the value are: 16->1024x1024, 32->512x512, 64->256x256
• if go on training, set --checkpoint_dir_E which path save pre-trained Encoder model
• --checkpoint_dir_GAN is needed, StyleGANv1 is a directory(contains 3 filers: Gm, Gs, center-tensor) , others are file path (.pth or .pt)

    parser = argparse.ArgumentParser(description='the training args')
    parser.add_argument('--iterations', type=int, default=210000) # epoch = iterations//30000
    parser.add_argument('--lr', type=float, default=0.0015)
    parser.add_argument('--beta_1', type=float, default=0.0)
    parser.add_argument('--batch_size', type=int, default=2)
    parser.add_argument('--experiment_dir', default=None) #None
    parser.add_argument('--checkpoint_dir_GAN', default='./checkpoint/stylegan_v2/stylegan2_ffhq1024.pth') #None  ./checkpoint/stylegan_v1/ffhq1024/ or ./checkpoint/stylegan_v2/stylegan2_ffhq1024.pth or ./checkpoint/biggan/256/G-256.pt
    parser.add_argument('--config_dir', default='./checkpoint/biggan/256/biggan-deep-256-config.json') # BigGAN needs it
    parser.add_argument('--checkpoint_dir_E', default=None)
    parser.add_argument('--img_size',type=int, default=1024)
    parser.add_argument('--img_channels', type=int, default=3)# RGB:3 ,L:1
    parser.add_argument('--z_dim', type=int, default=512) # PGGAN , StyleGANs are 512. BIGGAN is 128
    parser.add_argument('--mtype', type=int, default=2) # StyleGANv1=1, StyleGANv2=2, PGGAN=3, BigGAN=4
    parser.add_argument('--start_features', type=int, default=16)  # 16->1024x1024, 32->512x512, 64->256x256, 128->128x128

We offered pre-trainned GANs and their corresponding encoders here: models (default setting is the case1 ).

GANs:

• StyleGANv1-(FFHQ1024, Car512, Cat256) models which contain 3 files Gm, Gs and center-tensor.
• PGGAN and StyleGANv2. A single .pth file gets Gm, Gs and center-tensor together.
• BigGAN 128x128 ,256x256, and 512x512: each type contain a config file and model (.pt)

Encoders:

• StyleGANv1 FFHQ (case 2) for real-image embedding and process.
• StyleGANv2 LSUN Cat 256, they are one models from case 1 (Grad-CAM based attentions) and both models from case 2 (Grad-Cam based and Center-aligned Attentions for ablation study):
• StyleGANv2 FFHQ (case 1)
• Biggan-256 (case 1)

If you want to try more GANs, cite more pre-trained GANs below:

Pre-trained GANs:

StyleGANv1: https://github.com/podgorskiy/StyleGan.git, ( Converting code for official pre-trained model is here: https://github.com/podgorskiy/StyleGAN_Blobless.git) StyleGANv2 and PGGAN: https://github.com/genforce/genforce.git BigGAN: https://github.com/huggingface/pytorch-pretrained-BigGAN

Comparing Works:

In-Domain GAN: https://github.com/genforce/idinvert_pytorch pSp: https://github.com/eladrich/pixel2style2pixel ALAE: https://github.com/podgorskiy/ALAE.git

Related Works:

Grad-CAM & Grad-CAM++: https://github.com/yizt/Grad-CAM.pytorch SSIM Index: https://github.com/Po-Hsun-Su/pytorch-ssim

Our method implementation partly borrow from the above works (ALAE and Related Works). We would like to thank those authors.

The code of this repository is released under the Apache 2.0 license.
The directories models/biggan and models/stylegan2 are provided under the MIT license.

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