  Transformer is eminently suitable for auto-regressive image synthesis which
predicts discrete value from the past values recursively to make up full image.
Especially, combined with vector quantised latent representation, the
state-of-the-art auto-regressive transformer displays realistic high-resolution
images. However, sampling the latent code from discrete probability
distribution makes the output unpredictable. Therefore, it requires to generate
lots of diverse samples to acquire desired outputs. To alleviate the process of
generating lots of samples repetitively, in this article, we propose to take a
desired output, a style image, as an additional condition without re-training
the transformer. To this end, our method transfers the style to a probability
constraint to re-balance the prior, thereby specifying the target distribution
instead of the original prior. Thus, generated samples from the re-balanced
prior have similar styles to reference style. In practice, we can choose either
an image or a category of images as an additional condition. In our qualitative
assessment, we show that styles of majority of outputs are similar to the input
style.
