Meissonic: Revitalizing Masked Generative Transformers for Efficient
  High-Resolution Text-to-Image Synthesis
Authors: name: Jinbin Bai, name: Tian Ye, name: Wei Chow, name: Enxin Song, name: Qing-Guo Chen, name: Xiangtai Li, name: Zhen Dong, name: Lei Zhu, name: Shuicheng Yan

Diffusion models, such as Stable Diffusion, have made significant strides in
visual generation, yet their paradigm remains fundamentally different from
autoregressive language models, complicating the development of unified
language-vision models. Recent efforts like LlamaGen have attempted
autoregressive image generation using discrete VQVAE tokens, but the large
number of tokens involved renders this approach inefficient and slow. In this
work, we present Meissonic, which elevates non-autoregressive masked image
modeling (MIM) text-to-image to a level comparable with state-of-the-art
diffusion models like SDXL. By incorporating a comprehensive suite of
architectural innovations, advanced positional encoding strategies, and
optimized sampling conditions, Meissonic substantially improves MIM's
performance and efficiency. Additionally, we leverage high-quality training
data, integrate micro-conditions informed by human preference scores, and
employ feature compression layers to further enhance image fidelity and
resolution. Our model not only matches but often exceeds the performance of
existing models like SDXL in generating high-quality, high-resolution images.
Extensive experiments validate Meissonic's capabilities, demonstrating its
potential as a new standard in text-to-image synthesis. We release a model
checkpoint capable of producing 1024 times 1024 resolution images.