Diffusion Models

Generative adversarial networks (GANs) produce images quickly, but they’re of relatively low quality. Diffusion image generators typically take more time, but they produce higher-quality output. Researchers aimed to achieve the best of both worlds.

Google’s new mobile phones put advanced computer vision and audio research into consumers’ hands. The Alphabet division introduced its flagship Pixel 8 and Pixel 8 Pro smartphones at its annual hardware-launch event. Both units feature AI-powered tools for editing photos and videos.

A tweak to diffusion models, which are responsible for most of the recent excitement about AI-generated images, enables them to produce more realistic output.

Stable Diffusion may amplify biases in its training data in ways that promote deeply ingrained social stereotypes.

Text-to-image generators like DALL·E 2, Stable Diffusion, and Adobe’s new Generative Fill feature can revise images in a targeted way — say, change the fruit in a bowl from oranges to bananas — if you enter a few words that describe the change plus an indication of the areas to be changed.

On the heels of systems that generate video directly from text, new work uses text to adjust the imagery in existing videos. Researchers unveiled Gen-1...

Typical text-to-image generators can generate pictures of a cat, but not your cat. That’s because it’s hard to describe in a text prompt precisely all the things that distinguish your pet from other members of the same species.

OpenAI’s text-to-image generator DALL·E 2 produces pictures with uncanny creativity on demand. Has it invented its own language as well? Ask DALL·E 2 to generate an image that includes text, and often its output will include seemingly random characters.

OpenAI’s DALL·E got an upgrade that takes in text descriptions and produces images in styles from hand-drawn to photorealistic. The new version is a rewrite from the ground up. It uses the earlier CLIP zero-shot image classifier to represent text descriptions.