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.

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.

When a diffusion model trains on this, it learns how to gradually subtract the noise, moving closer, step by step, to a target output piece of media (e.g. a new image).

AMD has officially enabled Stable Diffusion on its latest generation of Ryzen AI processors, bringing local generative AI image creation to systems equipped with XDNA 2 NPUs. The feature arrives ...

Reaching diffusion-model quality with far less computational resources The team behind sCM trained a continuous-time consistency model on ImageNet 512×512, scaling up to 1.5 billion parameters.

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 ...

Stability AI is out today with a new Stable Diffusion base model that dramatically improves image quality and users’ ability to generate highly detailed images with just a text prompt. Stable ...

The custom VAE that Easy Diffusion comes with, vae-ft-mse-840000-ema-pruned, smooths out some of the model's problems with human eyes and hands. Different models may have custom VAEs, but I rarely ...

Stability AI’s newest model for image generation is Stable Cascade promises to be faster and more powerful than its industry-leading predecessor, Stable Diffusion, which is the basis of many ...