Bibliographic Information: Bai, J., Ye, T., Chow, W., Song, E., Chen, Q.-G., Li, X., Dong, Z., Zhu, L., & Yan, S. (2024). Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis [Technical Report]. arXiv:2410.08261v1 [cs.CV].
Research Objective: This paper introduces Meissonic, a novel text-to-image synthesis model that aims to overcome the limitations of existing masked image modeling (MIM) approaches, particularly in generating high-resolution images and achieving comparable performance to leading diffusion models.
Methodology: Meissonic leverages a multi-modal transformer architecture with several key innovations: a combination of multi-modal and single-modal transformer layers, advanced positional encoding using Rotary Position Embeddings (RoPE), and an adaptive masking rate as a sampling condition. The model is trained progressively through four stages, each focusing on specific aspects of image synthesis quality, and incorporates micro-conditions like original image resolution, crop coordinates, and human preference scores. Feature compression layers are integrated to enable efficient high-resolution generation.
Key Findings: Meissonic demonstrates superior performance in generating high-resolution (1024x1024) images while maintaining efficiency, even on consumer-grade GPUs with limited VRAM. It outperforms existing MIM methods and achieves comparable or superior results to state-of-the-art diffusion models like SDXL in terms of image quality, detail, and text-image alignment.
Main Conclusions: Meissonic presents a significant advancement in MIM-based text-to-image synthesis, offering a viable and efficient alternative to diffusion models. Its ability to generate high-quality, high-resolution images on readily available hardware makes it a valuable tool for various applications.
Significance: This research pushes the boundaries of MIM methods in text-to-image synthesis, demonstrating their potential to compete with and even surpass diffusion models in performance while maintaining efficiency.
Limitations and Future Research: While Meissonic excels in high-resolution image generation, the authors acknowledge that further research is needed to explore its capabilities in generating text within images, a feature currently limited by the choice of text encoder.
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by Jinbin Bai, ... pada arxiv.org 10-14-2024
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