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Donate arxiv logo > cs > arXiv:2410.08261 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computer Vision and Pattern Recognition arXiv:2410.08261 (cs) [Submitted on 10 Oct 2024] Title:Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis Authors:Jinbin Bai, Tian Ye, Wei Chow, Enxin Song, Qing-Guo Chen, Xiangtai Li, Zhen Dong, Lei Zhu, Shuicheng Yan View a PDF of the paper titled Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis, by Jinbin Bai and 8 other authors View PDF Abstract: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. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2410.08261 [cs.CV] (or arXiv:2410.08261v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2410.08261 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jinbin Bai [view email] [v1] Thu, 10 Oct 2024 17:59:17 UTC (43,207 KB) Full-text links: Access Paper: View a PDF of the paper titled Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis, by Jinbin Bai and 8 other authors * View PDF * TeX Source * Other Formats view license Current browse context: cs.CV < prev | next > new | recent | 2024-10 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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