10 papers
Beyond Reconstruction: Full-Context Generative DiT for Music Generation
Yunjia Li, Menglin Wu, Junyu Dai +13
Hybrid music generators combine the long-range planning of an autoregressive language model with the fidelity of a diffusion- or flow-based acoustic renderer. Yet renderers are tra…
Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering
Junyu Dai, Xinyue Fan, Weiqin Li +14
In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes. The propo…
LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training
Shun Lei, Huaicheng Zhang, Dapeng Wu +8
Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts. Existing language model-based…
LeVo: High-Quality Song Generation with Multi-Preference Alignment
Shun Lei, Yaoxun Xu, Zhiwei Lin +10
Recent advances in large language models (LLMs) and audio language models have significantly improved music generation, particularly in lyrics-to-song generation. However, existing…
SongBloom: Coherent Song Generation via Interleaved Autoregressive Sketching and Diffusion Refinement
Chenyu Yang, Shuai Wang, Hangting Chen +3
Generating music with coherent structure, harmonious instrumental and vocal elements remains a significant challenge in song generation. Existing language models and diffusion-base…
SongPrep: A Preprocessing Framework and End-to-end Model for Full-song Structure Parsing and Lyrics Transcription
Wei Tan, Shun Lei, Huaicheng Zhang +6
Artificial Intelligence Generated Content (AIGC) is currently a popular research area. Among its various branches, song generation has attracted growing interest. Despite the abund…