  Batik, a unique blend of art and craftsmanship, is a distinct artistic and
technological creation for Indonesian society. Research on batik motifs is
primarily focused on classification. However, further studies may extend to the
synthesis of batik patterns. Generative Adversarial Networks (GANs) have been
an important deep learning model for generating synthetic data, but often face
challenges in the stability and consistency of results. This research focuses
on the use of StyleGAN2-Ada and Diffusion techniques to produce realistic and
high-quality synthetic batik patterns. StyleGAN2-Ada is a variation of the GAN
model that separates the style and content aspects in an image, whereas
diffusion techniques introduce random noise into the data. In the context of
batik, StyleGAN2-Ada and Diffusion are used to produce realistic synthetic
batik patterns. This study also made adjustments to the model architecture and
used a well-curated batik dataset. The main goal is to assist batik designers
or craftsmen in producing unique and quality batik motifs with efficient
production time and costs. Based on qualitative and quantitative evaluations,
the results show that the model tested is capable of producing authentic and
quality batik patterns, with finer details and rich artistic variations. The
dataset and code can be accessed
here:https://github.com/octadion/diffusion-stylegan2-ada-pytorch
