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Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment

Overview

Our RSSA method help align the spatial structural information between source and target GAN to assist adaption. Paper link is here.

Requirements

Note: The base code of the training pipeline is taken from few-shot-gan-adaption's implementation from @Utkarsh Ojha

  • Linux
  • NVIDIA GPU + CUDA CuDNN 11.1
  • PyTorch 1.9.1
  • Python 3.9.1
  • Install all the libraries through pip install -r requirements.txt

Generating & Testing

We provide the pre-trained models for different source and target GAN models. Download the model from Here! Store the source model into the ./checkpoints_ori directory and the target model into the ./checkpoints directory.

Generate images

To generate images from a pre-trained source GAN and target GAN, run the following command:

CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_source /path/to/source_model/ --ckpt_target /path/to/target_model/ --task 10(5) --source source_domain --target target_domain --latent_dir /path/to/latent/ --mode viz_imgs

This will save synthesis samples into ./viz_img directory. Use the --load_noise option to use the noise vectors used for some samples shown in the main paper. For example:

CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_source ./checkpoints_ori/face.pt --ckpt_target ./checkpoints/face2sketches_self_dis_proj_10/final.pt --task 10 --source face --target sketches --latent_dir latent/sketches/latent/ --mode viz_imgs --load_noise noise.pt

Generate interpolation images

To generate interpolation images from source and target GAN, run the following command:

CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_source /path/to/source_model --ckpt_target /path/to/target_model/ --task 10(5) --source source_domain --target target_domain --latent_dir /path/to/latent/ --mode viz_gif --load_noise /path/to/noise_vector/

This will save synthesis interpolation images (for source and target) into ./viz_gif directory. For example:

CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_source ./checkpoints_ori/face.pt --ckpt_target ./checkpoints/face2VanGogh_self_dis_proj_10/final.pt --task 10 --source face --target VanGogh --latent_dir latent/VanGogh_face/latent/ --mode viz_gif

Evaluating Inception Score

First use the trained target model to synthesis images via the following command:

CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_target /path/to/target_model/ --task 10(5) --source source_domain --target target_domain --latent_dir /path/to/latent/ --mode eval_IS

This will synthesis 1000 samples for target domain by default and save them into the ./eval_IS directory. For example:

CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_target ./checkpoints/face2VanGogh_self_dis_proj_10/final.pt --task 10 --source face --target VanGogh --latent_dir latent/VanGogh_face/latent/ --mode eval_IS

Then run eval.py to calculate the mean Inception Score for the synthesis images as below. Note to verify the save directory of the images synthesis by the above command.

CUDA_VISIBLE_DEVICES=0 python eval.py --mode IS --img_pth /path/to/eval4IS/images

For example:

CUDA_VISIBLE_DEVICES=0 python eval.py --mode IS --img_pth ./eval_IS/face2VanGogh_10

Evaluating SCS Score

First generate cross-domain image pairs via the following command:

CUDA_VISIBLE_DEVICES=0 python generate.py --ckpt_source /path/to/source_model/ --ckpt_target /path/to/target_model/ --task 10(5) --source source_domain --target sketches --latent_dir /path/to/latent/ --mode eval_SCS --SCS_samples n

This will save synthesis cross-domain pairs in ./eval_SCS/ (500 pairs by default), verify the task directory (./eval_SCS/church2VanGogh_10 for example) and run eval.py with following command to get the edges for the synthesis pairs and calculate the mean pair-wise SCS:

CUDA_VISIBLE_DEVICES=0 python eval.py --mode SCS --img_pth /path/to/synthesis/pairs/

For example:

CUDA_VISIBLE_DEVICES=0 python eval.py --mode SCS --img_pth eval_SCS/church2VanGogh_10

Training (adapting) your own GAN

Data preparation

The raw images are saved in the ./data/ directory, and the processed images are saved in the ./processed_data/ directory. If you want to train model on your own data, save them in ./data/ and run prepare_data.py to preprocess your raw data as follow:

  • python prepare_data.py --out processed_data/<dataset_name> --size 256 ./data/<dataset_name>

GAN inversion

First invert the training images using invert_gan.py, we also provide inverted latent code in the ./latent/ directory. The image2stylegan code base in this repo does not guarantee good reconstruction performance (shown in the latent/target_domain/images), you can use your own inversion method if you get better results. For example:

CUDA_VISIBLE_DEVICES=0 python invert_gan.py --image_dir data/caricatures/images/ --stylegan2_path checkpoints_ori/face.pt --latent_dir latent/caricatures/

Training

Train your own GAN via commanding as follow.

CUDA_VISIBLE_DEVICES=0 python train_face_proj.py --ckpt /path/to/source/model/ --data_path /path/to/processed/data/ --exp source2target --iter 2002 --self_corr_loss --proj --dis_corr_loss --latent_dir /path/to/latent/ --task 10(5) --exp_name target_domain --n_train 10

For example:

CUDA_VISIBLE_DEVICES=0 python train_face_proj.py --ckpt /checkpoints_ori/face.pt --data_path ./processed_data/sketches_5/  --exp face2sketches --iter 2002 --self_corr_loss --proj --dis_corr_loss --latent_dir latent/sketches/latent/ --task 5 --exp_name sketches

This will save the intermediate checkpoint in the ./checkpoints/face2sketches_self_dis_proj_5 directory and the intermediate samples in the ./samples/face2sketches_self_dis_proj_5 directory.

Note that the spatial alignment is GPU memory consuming, runnig the above code with default configurations will cost 24 GB GPU memory. We run all of our experiments on one NVIDIA RTX 3090 GPU. Modifying the scale of self corr loss and dis corr loss may help reduce the spatial complexity.