Dynin-Omni: Omnimodal Unified Large Diffusion Language Model

Omnimodal unified models aim to understand text, images, video, and speech and generate coordinated text, image, and speech outputs. The challenge is to extend a single backbone to new modalities efficiently while preserving established capabilities and enabling coordinated cross-modal generation. We present Dynin-Omni, an open-source 8B-scale native omnimodal masked-diffusion model that unifies these capabilities within a single token-level backbone under a shared masked-token prediction objective. We propose a three-stage training recipe for effective omnimodal adaptation that first aligns new modalities without replaying the full task mixture, then applies modality-disentangled merging to recover inherited capabilities. Scheduled padding supervision delays termination learning until semantic grounding is established, enabling flexible-length generation. Moreover, Dynin-Omni is the first masked-diffusion model to natively generate interleaved text, image, and speech within a single reverse diffusion process, enabling mutual conditioning and joint refinement without modality-wise orchestration. Across 20 benchmark settings covering text, image, video, speech, and joint omnimodal generation, Dynin-Omni outperforms prior unified systems on a broad range of directly comparable benchmarks while remaining competitive with modality-specific experts. Stage-wise and controlled analyses show capability recovery and improved length control, while native joint decoding approaches the quality of the best evaluated two-stage generation configuration with a lower sampling budget.