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This study explores an alternative approach to seismic conditioning for generative adversarial networks (GAN) in facies modelling. Instead of adversarial learning, the proposed training workflow adopts transfer learning to train a conditional generator, starting from a pre-trained unconditional generator. This reuses pre-trained neural network models, reducing the device cost of training a conditional GAN from scratch. By fine-tuning the configurations of this training workflow, we achieve a conditional generator that can create high-fidelity facies models conditioned to acoustic impedance maps.