  Story Visualization is an advanced task of computed vision that targets
sequential image synthesis, where the generated samples need to be realistic,
faithful to their conditioning and sequentially consistent. Our work proposes a
novel architectural and training approach: the Impartial Transformer achieves
both text-relevant plausible scenes and sequential consistency utilizing as few
trainable parameters as possible. This enhancement is even able to handle
synthesis of 'hard' samples with occluded objects, achieving improved
evaluation metrics comparing to past approaches.
