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Technion - Israel Institute of Technology

Google Research

SinGAN: Learning a Generative Model from a Single Natural Image

Tamar Rott Shaham   Tali Dekel   Tomer Michaeli




   Single Training Image

Random samples from a single image

Image generation learned from a single training Image. We propose SinGAN–a new unconditional generative model trained on a single natural image. Our model learns the image’s patch statistics across multiple scales, using a dedicated multi-scale adversarial training scheme; it can then be used to generate new realistic image samples that preserve the original patch distribution while creating new object configurations and structures.





We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image. SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image. This allows generating new samples of arbitrary size and aspect ratio, that have significant variability, yet maintain both the global structure and the fine textures of the training image. In contrast to previous single image GAN schemes, our approach is not limited to texture images, and is not conditional (i.e. it generates samples from noise). User studies confirm that the generated samples are commonly confused to be real images. We illustrate the utility of SinGAN in a wide range of image manipulation tasks.


SinGAN for image manipulation


Paint to image





Training Image