a generator and a discriminator. The generator creates images based on a random input, while the discriminator evaluates the generated images and provides feedback to the generator. This feedback loop enables the generator to improve its performance over time, producing increasingly realistic images.
One popular example of an AI-powered image generator is DALL-E 2, developed by OpenAI. DALL-E 2 uses a technique called diffusion models to generate images from textual descriptions. It works by breaking down the input text into concepts and then using these concepts to guide the generation of the image. The model is trained on a large dataset of text-image pairs, allowing it to understand the relationship between language and visual concepts. Once trained, DALL-E 2 can create original and realistic images that incorporate multiple concepts and attributes, such as a cute corgi living in a house made out of sushi. Additionally, DALL-E 2 offers features like outpainting and inpainting, which allow users to extend or modify existing images in creative ways. The resulting images are often highly detailed and realistic, blurring the line between human-created and AI-generated art.