Text-to-image synthesis

Text-to-image synthesis refers to generative AI models that create images from textual descriptions. Key aspects:

Generate photographs, illustrations, artwork based on text prompts.
Train on datasets mapping text captions to images.
Leverage diffusion models, GANs, VAEs as base architectures.
Allow control over content, style, composition via prompt engineering.
Enable unprecedented creativity and automation in art/media.
But risks around bias, toxicity, and copyright require caution.

Pioneering models include DALL-E, Imagen, and Stable Diffusion. Applications span art, design, content creation, accessibility.

Text-to-image synthesis represents a breakthrough in multimodality - connecting natural language and vision. It could enable immersive imagery grounded in specific concepts.

But the technology also faces open challenges around coherence, factual accuracy, and responsible development. Continued progress should balance innovation with ethics.