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About this paper
Meituan LongCat Team, Hanghang Ma, Haoxian Tan, Jiale Huang, Junqiang Wu, Jun-Yan He, Lishuai Gao, Songlin Xiao and 5 more
Meituan's LongCat-Image introduces an open-source, bilingual foundation model for image generation and editing, achieving state-of-the-art performance with a compact 6B parameter architecture. The model establishes new industry standards for Chinese character rendering, reaching 90.7% accuracy on a custom benchmark, and demonstrates robust image editing capabilities, often outperforming larger models.
Key insights
- State-of-the-art performance in image generation and editing can be achieved with a compact 6B parameter model, demonstrating that efficiency and meticulous training strategies can challenge the brute-force scaling paradigm.
- Dedicated data synthesis and curriculum learning, such as generating over 10 million text-in-image samples and dynamic sampling for specific characters, are essential for achieving robust multilingual text rendering.
- Releasing the full training toolchain and intermediate checkpoints fosters transparency and significantly lowers the barrier for community research and further development.
Summary by alphaXiv.
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