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In a time of virtual spaces, the usage of generative adversarial networks is inevitable. Generative adversarial networks (GANs) are generative deep-learning models that can generate realistic data. GANs have been used in many applications like text-to-image, image-to-image, image synthesis, speech synthesis, etc. Its power lies in the diversity and novelty of the generated data. Despite their advantages, GANs are resource-hungry. GANs’ output resolution and high correlation make it more challenging to compress and fit on edge-devices storage and power budget. Hence, traditional compression techniques are not the best fit to use with GANs. Additionally, GANs training instability adds another dimension of difficulty. Therefore, compression techniques that require retraining are challenging for GANs. In this paper, we developed a weight clustering technique to compress GANs without the need for retraining, hence the name post-training compression technique (PTcomp). We also proposed a clustered-based pruning which adds more savings. Experiments on Cyclegan, Deep convolution gan (DCGAN), and Stargan using several datasets show the superiority of our technique against traditional post-training quantization. Our technique provides a 4x to 8x compression ratio with comparable quality to original models and 14% fewer mac operations due to pruning.