Currently, due to GPU memory constraints, there's a necessity to divide datasets into smaller batches for processing. On the other hand it should be noted that currently this is the only available method to correct for parallax artefacts XRD-CT images that are larger than 256 x 256 pixels. This is important as nowadays significantly larger XRD-CT images (512 x 512 or larger) can be acquired relatively easily at synchrotron radiation facilities, especially at fourth generation synchrotrons such as the ESRF. We hope our developed approach will encourage the synchrotron community to see the potential of machine learning approaches also as a means to yield higher quality experimental data in terms of artefact removal, not just for optimising data acquisition, and invest more resources in the field.