Kai Wang

RefineEdit is introduced, a training-free prompt-to-prompt image editing framework built on a Generative Refinement Network, to couple edit localization with content generation through the global refinement of binary image codes, allowing editing evidence to be reassessed as the image evolves.

This study implements a hybrid-structure-based deep learning framework to generate monthly 2 m specific humidity, 2 m temperature and surface pressure at 1/30° × 1/30° horizontal resolution during 1901–2023, and reproduces learned terrain-related spatial gradients without explicitly enforcing physical equations or terrain constraints.

Diffusion models can be made more efficient and produce better outputs by dynamically allocating compute based on a learned "difficulty" signature, without any retraining.