(ICCV 2023) Text-Driven Generative Domain Adaptation with Spectral Consistency Regularization

• Paper: Coming Soon

Setup

Requirements

conda install --yes -c pytorch pytorch=1.10.1 torchvision cudatoolkit=<CUDA_VERSION>
pip install ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git

Download StyleGAN2 checkpoint

• Download pretrained StyleGAN2 generator from here.

Training

Usage

To convert a generator from one domain to another, run the training script in the ZSSGAN directory:

python train.py size=1024  \
                task=photo2sketch \
                lambda_spectral=1 \
                batch=2  \
                n_sample=4 \
                lr=0.002 \
                frozen_gen_ckpt="/path/to/stylegan2-ffhq-config-f.pt" \
                train_gen_ckpt="/path/to/stylegan2-ffhq-config-f.pt" \
                iter=301 \
                source_class="photo" \
                target_class="sketch" \
                output_interval=50 \
                clip_models="[ViT-B/32,ViT-B/16]" \
                clip_model_weights="[1.0,1.0]" \
                save_interval=150

Experimental Results

Text-guided Image Generation

One-shot Domain Adaptation