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During recent years, Generative Adversarial Networks (GANs) have been introduced as algorithms to build reservoir models. We discuss challenges and solutions when building conditional GANs that replicate fluvial reservoir facies given trend maps and net-to-gross values as inputs. We show how using depth-wise separable convolution to replace standard convolution in SPADE-based conditional GANs can improve the output variability when fed with different random vectors.