← 研踪 PaperTrailCVF · CVPR 2026:cvf:45f0970e1ece5ae26ee0f3ee03c1d2f5 · 记录 #1
Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:13
摘要
Masked Discrete Diffusion Models (MDMs) have achieved strong performance across a wide range of multimodal tasks, including image understanding, generation, and editing. However, their inference speed remains suboptimal due to the need to repeatedly process redundant masked tokens at every sampling step. In this work, we propose Sparse-LaViDa, a novel modeling framework that dynamically truncates unnecessary masked tokens at each inference step to accelerate MDM sampling. To preserve generation quality, we introduce specialized register tokens that serve as sparse representations for the truncated tokens. Furthermore, to ensure consistency between training and inference, we design a specialized attention mask that faithfully matches the truncated sampling procedure during training. Built upon the state-of-the-art unified MDM LaViDa-O, Sparse-LaViDa achieves up to a 2xspeedup across diverse tasks including text-to-image generation, image editing, and mathematical reasoning, while maintaining generation quality.
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记录 #1 · 2026 · 最新观测版本
Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models
Masked Discrete Diffusion Models (MDMs) have achieved strong performance across a wide range of multimodal tasks, including image understanding, generation, and editing. However, their inference speed remains suboptimal due to the need to repeatedly process redundant masked tokens at every sampling step. In this work, we propose Sparse-LaViDa, a novel modeling framework that dynamically truncates unnecessary masked tokens at each inference step to accelerate MDM sampling. To preserve generation quality, we introduce specialized register tokens that serve as sparse representations for the truncated tokens. Furthermore, to ensure consistency between training and inference, we design a specialized attention mask that faithfully matches the truncated sampling procedure during training. Built upon the state-of-the-art unified MDM LaViDa-O, Sparse-LaViDa achieves up to a 2xspeedup across diverse tasks including text-to-image generation, image editing, and mathematical reasoning, while maintaining generation quality.
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