DiFa: Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks

Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks
Yabo Zhang, Mingshuai Yao, Yuxiang Wei, Zhilong Ji, Jinfeng Bai, Wangmeng Zuo

Abstract:
One-shot generative domain adaption aims to transfer a pre-trained generator on one domain to a new domain using one reference image only. However, it remains very challenging for the adapted generator (i) to generate diverse images inherited from the pre-trained generator while (ii) faithfully acquiring the domain-specific attributes and styles of the reference image. In this paper, we present a novel one-shot generative domain adaption method, i.e., DiFa, for diverse generation and faithful adaptation. For global-level adaptation, we leverage the difference between the CLIP embedding of reference image and the mean embedding of source images to constrain the target generator. For local-level adaptation, we introduce an attentive style loss which aligns each intermediate token of adapted image with its corresponding token of the reference image. To facilitate diverse generation, selective cross-domain consistency is introduced to select and retain the domain-sharing attributes in the editing latent W+ space to inherit the diversity of pre-trained generator. Extensive experiments show that our method outperforms the state-of-the-arts both quantitatively and qualitatively, especially for the cases of large domain gaps. Moreover, our DiFa can easily be extended to zero-shot generative domain adaption with appealing results.

Getting Started

The code relies on the official implementation of StyleGAN-NADA, pSp, e4e, and the pytorch implementation of StyleGAN2.

Installation

• Clone this repo:

  git clone https://github.com/1170300521/DiFa.git
  cd DiFa

• Dependencies:

  We recommend running this repository using Anaconda. All dependencies for defining the environment are provided in environment.yaml.

• Download pretrained StyleGAN2 (or StyleGAN-ADA) to weights and GAN inversion models(pSp or e4e) to weights/psp_weights

Datasets

We provide some reference images in reference_images/.

You can obtain more training images from Artstation-Artistic-face-HQ (AAHQ), MetFaces, The Face of Art, and AFHQ-Wild.

Training

To convert a generator from one domain to another, run the training script ZSSGAN/scripts/train.sh:

CUDA_VISIBLE_DEVICES=1 python train.py  \
                --batch 2  --dataset "ffhq" \
                --n_sample 4 --output_dir "output" \
                --lr 0.002 \
                --frozen_gen_ckpt ../weights/stylegan2-ffhq-config-f.pt \
                --psp_path ../weights/psp_weights/e4e_ffhq_encode.pt \
                --iter 301 \
                --source_class "photo" \
                --target_class "elsa.png" \
                --source_type "mean" \
                --auto_layer_k 18 \
                --auto_layer_iters 0 --auto_layer_batch 8 \
                --output_interval 50 \
                --mixing 0.0 \
                --save_interval 300 \
                --clip_models  "ViT-B/32" "ViT-B/16" \
                --psp_loss_type "dynamic" \
                --clip_model_weights 1.0 1.0 \
                --psp_model_weight 6 \
                --num_keep_first 7 \
                --psp_alpha 0.6 \
                --lambda_direction 1.0 \
                --lambda_partial 1 \
                --sliding_window_size 50 \
                --style_img_dir ../reference_images/FFHQ/elsa.png \

Generally, just specify path to the reference image --style_img_dir and its corresponding --target_class.

Addtionally, you may use larger --psp_alpha to retain more attributes, larger --psp_model_weight to retain each attribute more heavily, larger --lambda_partial to acquire more representative characteristics.

Note that when adapting a generator pre-trained on other source domains, you need to modify --dataset, path to pre-trained generator --frozen_gen_ckpt, and path to inversion models --psp_path.

More arguments and their explanation are provided in ZSSGAN/options/train_options.py.

Inference

To transfer real images, we first utilize e4e to invert them into $W+$ latent codes, and then input the codes into the adapted generator to obtain adapted images.

Specifically, just run the inference script ZSSGAN/scripts/inference.sh:

python inference.py  \
                --dataset 'ffhq' \
                --frozen_gen_ckpt path/to/adapted_generator \
                --psp_path ../weights/psp_weights/e4e_ffhq_encode.pt \
                --real_image_path path/to/real_image \