LTX-2: Efficient Joint Audio-Visual Foundation Model

• URL: http://arxiv.org/abs/2601.03233v1
• Date: Tue, 06 Jan 2026 18:24:41 GMT
• Title: LTX-2: Efficient Joint Audio-Visual Foundation Model
• Abstract summary: LTX-2 is an open-source model capable of generating temporally synchronized audiovisual content.<n>We employ a multilingual text encoder for broader prompt understanding.<n>LTX-2 produces rich, coherent audio tracks that follow the characters, environment, style, and emotion of each scene.
• Score: 3.1804093402153506
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent text-to-video diffusion models can generate compelling video sequences, yet they remain silent -- missing the semantic, emotional, and atmospheric cues that audio provides. We introduce LTX-2, an open-source foundational model capable of generating high-quality, temporally synchronized audiovisual content in a unified manner. LTX-2 consists of an asymmetric dual-stream transformer with a 14B-parameter video stream and a 5B-parameter audio stream, coupled through bidirectional audio-video cross-attention layers with temporal positional embeddings and cross-modality AdaLN for shared timestep conditioning. This architecture enables efficient training and inference of a unified audiovisual model while allocating more capacity for video generation than audio generation. We employ a multilingual text encoder for broader prompt understanding and introduce a modality-aware classifier-free guidance (modality-CFG) mechanism for improved audiovisual alignment and controllability. Beyond generating speech, LTX-2 produces rich, coherent audio tracks that follow the characters, environment, style, and emotion of each scene -- complete with natural background and foley elements. In our evaluations, the model achieves state-of-the-art audiovisual quality and prompt adherence among open-source systems, while delivering results comparable to proprietary models at a fraction of their computational cost and inference time. All model weights and code are publicly released.

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