UNISON: A Unified Sound Generation and Editing Framework via Deep LLM Fusion

Zhaoqing Li; Haoning Xu; Jingran Su; Yaofang Liu; Zhefan Rao; Huimeng Wang; Jiajun Deng; Tianzi Wang; Zengrui Jin; Rui Liu; Haoxuan Che; Xunying Liu

arXiv · 2605.31530

UNISON: A Unified Sound Generation and Editing Framework via Deep LLM Fusion

Zhaoqing Li·Haoning Xu·Jingran Su·Yaofang Liu·Zhefan Rao·Huimeng Wang·Jiajun Deng·Tianzi Wang·Zengrui Jin·Rui Liu·Haoxuan Che·Xunying Liu

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Abstract

We present UNISON, a latent diffusion framework that unifies speech generation, sound generation, and audio editing within a single model. A single model handles text-to-audio, text-to-speech, zero-shot speaker cloning, mixed speech-and-sound generation, scene-level audio editing, speech-in-scene editing, and timed temporal composition, all of which share a single set of weights. Our architecture features two core designs: (1) Layer-wise deep LLM fusion, which injects hidden states from uniformly sampled layers of a frozen MLLM into corresponding MM-DiT blocks via learned projections, providing depth-matched semantic conditioning that improves instruction following over single-layer baselines; and (2) a unified multi-task architecture where task identity is encoded solely by a channel-wise mask and source audio is provided through VAE-encoded channel concatenation. Training is stabilized by an online GPU-side multi-task data synthesis pipeline with task-homogeneous batching and a two-stage curriculum. With 621M--732M trainable parameters, UNISON achieves results competitive with or exceeding task-specialist models across evaluated domains, while being roughly $4\times$ smaller than comparable unified systems.

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Zhaoqing Li, Haoning Xu, Jingran Su, Yaofang Liu, Zhefan Rao, Huimeng Wang, Jiajun Deng, Tianzi Wang, Zengrui Jin, Rui Liu, Haoxuan Che, Xunying Liu. 2026-06-02. UNISON: A Unified Sound Generation and Editing Framework via Deep LLM Fusion. https://arxiv.org/abs/2605.31530

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