Generative Adversarial Networks (GANs) have been used for the task of image generation and has achieved impressive results. There is always a challenge to train networks that generate large scale images since they tend to be huge and training needs a lot of data. In this work, we tackle this problem by dividing it into two smaller parts. We first generate small scale images using GANs then use a super resolution network to enlarge the generated images resulting in large scale images. Using a super resolution network helps in adding more details to the image which results in a better-quality image. This technique has been tested with a small amount of data to generate 128x128 pixel images and obtained better inception scores over the baseline GAN.