Text-to-image AI models, such as Stable DALL-E and DALL-E 2, work by converting a textual description into a corresponding image. The model is trained on a large dataset of images and their textual descriptions, learning the relationships between language and visual features.

When given a textual description as input, the model generates an image based on the textual features and patterns it has learned during training. The generated image is a combination of features extracted from the text and features learned from the training data. The process of generating an image from text is known as "conditioning," and the text serves as a "condition" for the image generation.

Stable DALL-E and DALL-E 2 are advanced versions of this type of AI model, featuring improved training methods and a more robust architecture, enabling them to generate a wider variety of high-quality images from textual descriptions.
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