  VAR is a new generation paradigm that employs 'next-scale prediction' as
opposed to 'next-token prediction'. This innovative transformation enables
auto-regressive (AR) transformers to rapidly learn visual distributions and
achieve robust generalization. However, the original VAR model is constrained
to class-conditioned synthesis, relying solely on textual captions for
guidance. In this paper, we introduce VAR-CLIP, a novel text-to-image model
that integrates Visual Auto-Regressive techniques with the capabilities of
CLIP. The VAR-CLIP framework encodes captions into text embeddings, which are
then utilized as textual conditions for image generation. To facilitate
training on extensive datasets, such as ImageNet, we have constructed a
substantial image-text dataset leveraging BLIP2. Furthermore, we delve into the
significance of word positioning within CLIP for the purpose of caption
guidance. Extensive experiments confirm VAR-CLIP's proficiency in generating
fantasy images with high fidelity, textual congruence, and aesthetic
excellence. Our project page are https://github.com/daixiangzi/VAR-CLIP
