But where do such hallucinations come from?

However, current AI models often fail to retain this ability.

Diffusion models are powerful, but they require a lot of time and data to train. We propose Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training time costs while improving data efficiency, which thus helps democratize diffusion model training to broader users. At the core of our innovations is a new conditional score function at the patch level, where the patch location in the original image is included as additional coordinate channels, while the patch size is randomized and diversified throughout training to encode the cross-region dependency at multiple scales. Sampling with our method is as easy as in the original diffusion model.

Based on this, we propose a novel approach, Diffusion-DICE, that directly performs this transformation using diffusion models.

Beyond estimating parameters of interest from data, one of the key goals of statistical inference is to properly quantify uncertainty in these estimates.

Our method, V oxMol, generates molecules in a fundamentally different way than the current state of the art ( i.e.,