As the Generator gets better at creating realistic images, it gets better at fooling the Discriminator, and thus a functioning image generation algorithm is created.Īlthough this is still in early development, Adobe has highlighted a couple of potential uses for the technology.
The Generator is responsible for creating new outputs, and the Discriminator has to guess whether any image it is presented with is an output from the Generator or an actual image from the training set.
GANs work by using two neural networks against each other. AdobeĪdobe’s generator currently uses an artificial intelligence technique called a General Adversarial Network, or GAN, and not a diffusion model. Users can also turn images into panoramas with the AI tool. However, the images it outputs are significantly larger. This means it’s great at generating realistic environments from seed images, but it has no text-to-image features (in other words, you can’t enter a text prompt and get a weird result) or any other general generation features. Adobe’s generator was trained exclusively on a dataset of roughly 250,000 high-resolution 360-degree panoramas. DALL-E 2 and Stable Diffusion were trained on billions of text-image pairs that cover every concept from avocados and Avril Lavigne, to zebras and Zendaya. First, it’s trained on a much more limited dataset with a specific purpose in mind. Adobe’s AI image generator is a little different from more general image generators like DALL-E 2 and Stable Diffusion in a couple of key ways.
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