  Generative transformers have shown their superiority in synthesizing
high-fidelity and high-resolution images, such as good diversity and training
stability. However, they suffer from the problem of slow generation since they
need to generate a long token sequence autoregressively. To better accelerate
the generative transformers while keeping good generation quality, we propose
Lformer, a semi-autoregressive text-to-image generation model. Lformer firstly
encodes an image into $h{\times}h$ discrete tokens, then divides these tokens
into $h$ mirrored L-shape blocks from the top left to the bottom right and
decodes the tokens in a block parallelly in each step. Lformer predicts the
area adjacent to the previous context like autoregressive models thus it is
more stable while accelerating. By leveraging the 2D structure of image tokens,
Lformer achieves faster speed than the existing transformer-based methods while
keeping good generation quality. Moreover, the pretrained Lformer can edit
images without the requirement for finetuning. We can roll back to the early
steps for regeneration or edit the image with a bounding box and a text prompt.
