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About this paper
Yuanfeng Xu, Yuhao Chen, Liang Lin, Guangrun Wang
Researchers from Sun Yat-sen University introduced CoM-DAD, a hierarchical probabilistic framework that unifies text and image generation by decoupling semantic planning in a continuous latent space from token synthesis in a discrete space. This approach achieves superior generative quality and efficiency, demonstrating a 5x inference speedup over GPT-2 for text and effective cross-modal alignment without extensive paired data.
Key insights
- Decoupling abstract semantic planning from token-level synthesis into distinct continuous and discrete diffusion processes effectively bridges the topological gap in multimodal generation.
- A dynamic, variable-rate noise schedule for discrete absorbing diffusion fosters a "main-first, details-later" generative behavior, ensuring global coherence from sparse initial states.
- Effective cross-modal alignment can be achieved by training a shared semantic space through stochastic mixed-modal transport, circumventing the need for computationally intensive dual-encoders.
Summary by alphaXiv.
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