The recent paradigm shift from model-based to data-driven approaches has involved a growing number of data-fusion tasks. Specifically for pansharpening, unsupervised deep learning methods have been recently explored with the goal of overcoming the generalization limits shown by early pansharpening convolutional neural networks based on supervised training schemes. Furthermore, some of these exploit the target-adaptive modality to face the scarcity of training data. On the downside, combining usupervised training and target adaptivity causes a non-negligible increase of the computational cost. This work presents a new target adaptive scheme that allows to keep limited the computational cost at inference time while preserving accuracy.