Meissonic: Revitalizing Masked Generative Transformers for Efficient
  High-Resolution Text-to-Image Synthesis
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.
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Jinbin Bai
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2024-10-14T07:48:20.758Z
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Tian Ye
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Owen
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Wei Chow
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zhou wei
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WeiChow
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Enxin Song
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EnxinSong
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Enxin
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Qing-Guo Chen
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Qing-Guo Chen
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cqgwin
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Xiangtai Li
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2024-10-14T07:48:15.462Z
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Xiangtai Li
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LXT
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Zhen Dong
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2024-10-14T07:48:19.035Z
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Zhen Dong
user
zhendongucb
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Lei Zhu
670c859d0b54b420bb463316
Shuicheng Yan
Meissonic: Revitalizing Masked Generative Transformers for Efficient
  High-Resolution Text-to-Image Synthesis
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.
Meissonic, a non-autoregressive masked image model, achieves high-quality image generation by incorporating advanced techniques and outperforms existing models like SDXL.
diffusion models Stable Diffusion autoregressive language models LlamaGen VQVAE tokens non-autoregressive masked image modeling (MIM) positional encoding strategies sampling conditions micro-conditions feature compression layers high-resolution images text-to-image synthesis