How image generation models are trained? Why they understand prompt words and can generate the images from texts.
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TLDR: There are different ways to train an image generation model, such as using a transformer model or a generative adversarial network. These models use different techniques to generate new images, such as sampling pixel values or interpolating latent space vectors.
If you train an image generation model on a collection of paintings, it can generate new paintings that have the same style and colors as the original ones.
One way to train an image generation model is to use a **transformer** model, which is a type of neural network that can process sequences of data, such as words or pixels. A transformer model can learn how to generate coherent text or images by predicting the next element in a sequence based on the previous ones.
To train a transformer model on images, you need to **unroll** the images into long sequences of pixels, which are the tiny dots that make up an image. Each pixel has a value that represents its color and brightness. The transformer model can then learn the patterns and features of these pixel sequences, such as shapes, edges, textures, etc.
To generate new images, the transformer model can use a technique called **sampling**, which means randomly choosing some pixel values based on the probabilities learned by the model. The model can then use these pixel values as inputs to generate the rest of the image sequence.
Another way to train an image generation model is to use a **generative adversarial network** (GAN), which is a type of neural network that consists of two parts: a generator and a discriminator. The generator tries to create fake images that look real, while the discriminator tries to tell apart real images from fake ones. The generator and the discriminator compete with each other and improve over time.
To train a GAN on images, you need to provide both real and fake images as inputs to the discriminator. The real images are from your dataset, while the fake images are generated by the generator. The discriminator outputs a score that indicates how likely an image is real or fake. The generator tries to fool the discriminator by making its fake images more realistic, while the discriminator tries to catch the generator by making its scores more accurate.
To generate new images, the generator can use a technique called **latent space interpolation**, which means creating new images by combining features from different existing images. The generator has a hidden layer called the latent space, where each image is represented by a vector of numbers. The generator can then create new vectors by mixing and matching elements from different vectors, and use these vectors as inputs to generate new images.
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