Artificial Intelligence (AI) text-to-image is a subfield of AI that deals with the generation of images from text descriptions. The goal of text-to-image generation is to create an image that is semantically consistent with the given text description. There are various approaches to text-to-image generation, including:
- Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, that work together to generate an image from text.
- Variational Autoencoders (VAEs): VAEs consist of an encoder that converts text to a latent representation, and a decoder that generates an image from the latent representation.
- Attention-based models: Attention-based models use attention mechanisms to focus on specific parts of the text when generating an image.
- Retrieval-based models: Retrieval-based models retrieve a pre-existing image that is similar to the text description and use it as the generated image.
- Text-to-Scene Networks (TSN): TSN is a type of text-to-image generation that focuses on generating 3D scenes from text.
- Text-to-Video Networks (TVN): TVN is a type of text-to-image generation that focuses on generating video clips from text.
It is an active area of research and new techniques and architectures are being developed to improve the quality and diversity of the generated images.