Apple 发布三模态掩码扩散模型设计空间研究
The Design Space of Tri-Modal Masked Diffusion Models
Apple 团队发布首个从零预训练的三模态掩码扩散模型（MDM）研究，在文本、图文和音频文本数据上训练。论文系统分析了多模态缩放规律、模态混合比例、噪声调度和 batch size 影响，提出基于 SDE 的重参数化方法免去调优最优 batch size，并在 3B 模型规模（6.4T tokens）上验证了文本生成、T2I 和 T2S 任务的表现。
AuthorsLouis Béthune, Victor Turrisi, Bruno Kacper Mlodozeniec†**, Pau Rodriguez Lopez, Lokesh Boominathan, Nikhil Bhendawade, Amitis Shidani, Joris Pelemans, Theo X. Olausson‡**, Devon Hjelm, Paul Dixon, João Monteiro, Pierre Ablin, Vishnu Banna, Arno Blaas, Nick Henderson, Kari Noriy, Dan Busbridge, Josh Susskind, Marco Cuturi, Irina Belousova, Luca Zappella, Russ Webb, Jason Ramapuram§**
Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and finetuning a base unimodal model for bi-modal generation. Diverging from previous approaches, we introduce the first tri-modal Masked Diffusion Models (MDM) pretrained from scratch on text, image-text, and audio-text data. We systematically analyze multimodal scaling laws, modality mixing ratios, noise schedules, batch-size effects and provide optimized inference sampling defaults. Our batch-size analysis yields a novel stochastic differential equation (SDE) based reparameterization, eliminating the need for tuning the optimal batch size as reported in recent work. This reparameterization decouples the physical batch size, often chosen based on compute (GPU saturation, FLOP-efficiency, wall-clock time) from the logical batch size, chosen to balance the variance of gradients during stochastic optimization. Finally, we pretrain a preliminary model showcasing the capabilities of a unified design, achieving strong results at 3B model scale (6.4T tokens), in both text generation, T2I tasks, and T2S tasks. Our work represents the largest scale systematic open study of multimodal discrete diffusion models conducted to date, providing valuable insights into scaling behaviors across multiple modalities.
- † University of Cambridge
- ‡ MIT
- § Google DeepMind
- ** Work done while at Apple
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来源：Apple Machine Learning Research · machinelearning.apple.com