LTX-2: Efficient Joint Audio-Visual Foundation Model
Authors: name: Yoav HaCohen, name: Benny Brazowski, name: Nisan Chiprut, name: Yaki Bitterman, name: Andrew Kvochko, name: Avishai Berkowitz, name: Daniel Shalem, name: Daphna Lifschitz, name: Dudu Moshe, name: Eitan Porat, name: Eitan Richardson, name: Guy Shiran, name: Itay Chachy, name: Jonathan Chetboun, name: Michael Finkelson, name: Michael Kupchick, name: Nir Zabari, name: Nitzan Guetta, name: Noa Kotler, name: Ofir Bibi, name: Ori Gordon, name: Poriya Panet, name: Roi Benita, name: Shahar Armon, name: Victor Kulikov, name: Yaron Inger, name: Yonatan Shiftan, name: Zeev Melumian, name: Zeev Farbman

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.