Knowledge / Generative AI

Multimodal Generative Models

Models that jointly process text, images, audio, video, and other modalities.

By Siddhant Krishna · Published 2026-10-06 · Updated 2026-10-06

Modal Fusion

Multimodal systems must map different data types into representations that can interact. Fusion can occur through shared embeddings, cross-attention, token-like representations, encoders, decoders, or combinations of these mechanisms.

Multimodal Generation

Text-to-image.
Image-to-text.
Text-to-audio.
Speech-to-speech.
Text-to-video.
Image-to-video.
Multimodal reasoning and tool use.

References

Vaswani et al. (2017), Advances in Neural Information Processing Systems.
https://arxiv.org/abs/1706.03762
Ho, Jain & Abbeel (2020).
https://arxiv.org/abs/2006.11239
Stanford Institute for Human-Centered Artificial Intelligence (2026).
https://hai.stanford.edu/ai-index/2026-ai-index-report

TransformersSelf-attention, encoder-decoder architectures, positional information, and the foundation of modern language generation.Diffusion ModelsForward noising, denoising, score estimation, conditioning, and diffusion-based generation.

Modal Fusion

Multimodal Generation

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