X-VILA is an advanced foundation model designed to enhance the capabilities of large language models (LLMs) by integrating image, video, and audio modalities. The model achieves cross-modality understanding, reasoning, and generation through a two-phase alignment mechanism: textual alignment and visual alignment. 1. **Textual Alignment**: X-VILA aligns input and output representations of different modalities with the textual embedding space of the LLM. This involves using a unified embedding space for input and fine-tunable modality-specific diffusion models for output. 2. **Visual Alignment**: To address the loss of visual information during textual alignment, X-VILA introduces a Visual Embedding Highway (VEH) module. This module facilitates direct guidance of visual decoders, ensuring better preservation of visual features. The training process of X-VILA is divided into three phases: - **Encoder-LLM-Decoder Alignment Training**: Aligns modality-specific encoders and decoders with the LLM inputs and outputs. - **Interleaved Multi-Modal Pre-Training**: Enhances in-context learning performance using interleaved instruction data. - **X-to-X Cross-Modality Instruction Tuning**: Includes a two-step alignment process: textual alignment and visual alignment. X-VILA demonstrates significant improvements in cross-modality alignment and generation tasks, outperforming previous methods. It also showcases emergent properties across modalities, even without similar training data. The project will be open-source, providing a valuable resource for future research in multi-modality foundation models.X-VILA is an advanced foundation model designed to enhance the capabilities of large language models (LLMs) by integrating image, video, and audio modalities. The model achieves cross-modality understanding, reasoning, and generation through a two-phase alignment mechanism: textual alignment and visual alignment. 1. **Textual Alignment**: X-VILA aligns input and output representations of different modalities with the textual embedding space of the LLM. This involves using a unified embedding space for input and fine-tunable modality-specific diffusion models for output. 2. **Visual Alignment**: To address the loss of visual information during textual alignment, X-VILA introduces a Visual Embedding Highway (VEH) module. This module facilitates direct guidance of visual decoders, ensuring better preservation of visual features. The training process of X-VILA is divided into three phases: - **Encoder-LLM-Decoder Alignment Training**: Aligns modality-specific encoders and decoders with the LLM inputs and outputs. - **Interleaved Multi-Modal Pre-Training**: Enhances in-context learning performance using interleaved instruction data. - **X-to-X Cross-Modality Instruction Tuning**: Includes a two-step alignment process: textual alignment and visual alignment. X-VILA demonstrates significant improvements in cross-modality alignment and generation tasks, outperforming previous methods. It also showcases emergent properties across modalities, even without similar training data. The project will be open-source, providing a valuable resource for future research in multi-modality foundation models.