DALL·E 2 employs a sophisticated neural network architecture known as a transformer, which enables it to process and generate images based on textual input. By combining text and image modalities, the model learns to associate words with visual patterns, allowing it to create high-quality and diverse images.
This process involves encoding the input text into a numerical representation, which is then fed into the transformer network to generate an initial image embedding. The model then refines this embedding through a series of transformer layers, gradually transforming it into a final image output.
Through multi-modal learning, DALL·E 2 can capture complex relationships between words and visual concepts, enabling it to produce detailed and coherent images that align with the provided text description. The model can generate a wide range of visual content, from surreal and imaginative scenes to realistic depictions of everyday objects.