We propose SinGAN-GIF which is an extension of SinGAN to short video snippets, often referred to as GIFs after the file format they are usually distributed in. Our method learns the distribution of both the image patches as well as their motion pattern. We do so by using a pyramid of 3D convolutional networks along with an image and a video discriminator. We show that though generative video models struggle to generate convincing results, our framework provides a good alternative to harness the power of GANs for various applications, working directly on video frames in entirety instead of working frame by frame.