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About this paper
Chonghuinan Wang, Zhikai Chen, Chunwei Wang, Yecong Wan, Junwei Yang, Zhixin Wang, Wei Zhang, Jiaqi Xu and 4 more
ILLUME-X, developed by researchers from Harbin Institute of Technology, Huawei Noah’s Ark Lab, and collaborators, is a unified multimodal paradigm that generates high-quality, free-form interleaved text-image sequences by enhancing data efficiency and stabilizing multimodal training. The framework achieved state-of-the-art performance on ISG-Bench with an average score of 6.26, outperformed Emu 3.5 on the new ILScore (5.34 vs 5.33), and exhibited significantly lower inference latency, processing images over five times faster.
Key insights
- Explicitly demarcating modality transitions with special tokens (BOI/EOI/EOS) within a unified causal attention mechanism enables the model to effectively handle free-form interleaved sequences.
- A comprehensive data curation approach, leveraging both real-world video data and sophisticated synthetic generation with self-reflection, is crucial for stable and diverse multimodal training.
- Adapting Classifier-Free Guidance (CFG) for multimodal conditions, using separate scaling coefficients for text and image, significantly enhances semantic alignment and output quality in interleaved generation.
Summary by alphaXiv.
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