An unconditional generative model with self-attention module for single image generation

Generative Adversarial Networks (GANs) have revolutionized the field of deep learning by enabling the production of high-quality synthetic data. However, the effectiveness of GANs largely depends on the size and quality of training data. In many real-world applications, collecting large amounts of high-quality training data is time-consuming, and expensive. Accordingly, in recent years, GAN models that use limited data have begun to be developed. In this study, we propose a GAN model that can learn from a single training image. Our model is based on the principle of multiple GANs operating sequentially at different scales, where each GAN learns the features of the training image and transfers them to the next GAN, ultimately generating examples with different realistic structures at the final scale. In our model, we utilized a self-attention and new scaling method to increase the realism and quality of the generated images. The experimental results show that our model performs image generation successfully. In addition, we demonstrated the robustness of our model by testing it in different image manipulation applications. As a result, our model can successfully produce realistic, high-quality, diverse images from a single training image, providing short training time and good training stability.