Semantic image synthesis aims to transfer semantic label maps to photo-realistic images. Despite the significant successes achieved to date by state-of-the-art methods, there is a major gap between the quality of photo-realistic images and the quality of synthesized images. This gap is caused by training stability problems such as diversity of image generation, and the lack of semantic information. Also, this kind of task still poses a significant problem concerning computational time. Furthermore, opening a way to use a consistent and unified loss function for different tasks, datasets, and various generated images will be considerable assistance to tackle the challenges of training stability. In this survey, we discussed the Generative Adversarial Networks (GANs) model because of the ability to synthesize good samples directly. A literature discussion between different methods used to improve the result of GAN have been discussed which aims to produce better results and generate more samples. Moreover, a combination of different techniques from different fields was discussed.