A basic diffusion model is trained to add and corrupt the training data with Gaussian noise until it is pure noise, then reverse the process to denoise and restore the image.

Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability, particularly for reasoning and planning (known as System 2 abilities) has been lacking.

Google’s Gemini Diffusion demo didn’t get much airtime at I/O, but its blazing speed—and potential for coding—has AI insiders speculating about a shift in the model wars.

To do this, the model is trained, like a diffusion model, to observe the image destruction process, but learns to take an image at any level of obscuration (i.e. with a little information missing ...

On Wednesday, Stability AI released Stable Diffusion XL 1.0 (SDXL), its next-generation open weights AI image synthesis model. It can generate novel images from text descriptions and produces more ...