Apple Researchers Pretrain First Tri-Modal Masked Diffusion Model on Text, Image-Text, and Audio-Text Data
Apple researchers introduce the first tri-modal Masked Diffusion Models pretrained from scratch on text, image-text, and audio-text data, diverging from prior bi-modal finetuning approaches. The work systematically analyzes multimodal scaling laws, modality mixing ratios, noise schedules, and batch-size effects, and provides optimized inference sampling defaults, including a novel SDE-based method from batch-size analysis.
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