Image generation is a rapidly developing field, with new techniques emerging all the time. One recent development is the use of generative adversarial networks (GANs) to create realistic images from text descriptions. GANs are a type of neural network that consists of two competing networks: a generator network that creates images, and a discriminator network that tries to distinguish between real images and fake images generated by the generator. By training these two networks against each other, GANs can learn to generate images that are indistinguishable from real images.
This technique has been used to create a wide variety of images, including faces, animals, landscapes, and objects. GANs have also been used to create images of people that don't exist, which can be used for a variety of purposes, such as training machine learning models or creating realistic avatars.
One of the challenges with GANs is that they can sometimes generate images that are too realistic, or that contain unrealistic details. This can make it difficult to use GANs for tasks such as generating images for training machine learning models, as the models may learn to overfit to the unrealistic images generated by the GAN.
Despite these challenges, GANs are a powerful tool for image generation, and they are likely to continue to play an important role in this field in the years to come.