![]() ![]() gif file will be saved in gifs/ directory. -mode option determines the visualization type: generating either the images or interpolation.-n_steps determines the number of steps taken when interpolating from G(z 1) to G(z 2) (default set to 40).-n_sample determines the number of images to be sampled (default set to 25).The second argument when running traversal_gif.py denotes the number of images you want to interpolate between.# visualize the interpolations of source and targetĬUDA_VISIBLE_DEVICES=0 python generate.py -ckpt_source /path/to/source -ckpt_target /path/to/source -load_noise noise.pt -mode interpolate # generate two image grids of 5x5 for source and targetĬUDA_VISIBLE_DEVICES=0 python generate.py -ckpt_source /path/to/source -ckpt_target /path/to/target -load_noise noise.pt To generate images from a pre-trained GAN, run the following command: We will add the remaining ones soon.ĭownload the pre-trained model(s), and store it into. įor now, we have only included the pre-trained models using FFHQ as the source domain, i.e. We provide the pre-trained models for different source and adapted (target) GAN models. Install all the libraries through pip install -r requirements.txt.Note: The base model is taken from StyleGAN2's implementation from Linux Our method helps adapt the source GAN where one-to-one correspondence is preserved between the source G s(z) and target G t(z) images. PyTorch implementation of adapting a source GAN (trained on a large dataset) to a target domain using very few images. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Few-shot Image Generation via Cross-domain Correspondence ![]()
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