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LTX-2, a new open-source foundational model, generates high-quality, temporally synchronized audiovisual content by employing an asymmetric dual-stream transformer architecture with a 14B-parameter video stream and a 5B-parameter audio stream, coupled through bidirectional audio-video cross-attention layers. The model incorporates temporal positional embeddings and cross-modality AdaLN for shared timestep conditioning, along with a multilingual text encoder and modality-aware classifier-free guidance (modality-CFG) to improve audiovisual alignment and controllability. Evaluations demonstrate that LTX-2 achieves state-of-the-art audiovisual quality and prompt adherence among open-source systems, rivaling proprietary models with significantly reduced computational cost and inference time.
Finally, an open-source text-to-audiovisual model, LTX-2, rivals proprietary systems in quality and prompt adherence while drastically reducing computational cost.
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.
