IMAGE PROCESSING METHOD AND APPARATUS, COMPUTER DEVICE, STORAGE MEDIUM, AND PROGRAM PRODUCT

原文（中文）

An image processing method, apparatus, and computer-readable storage medium for training image generation models through semantic-aware denoising. The method obtains sample data including a sample image, its category, and identifier set. Training representation information is extracted from the sample identifier to represent training semantics expressing sample features under the category. A noise image is generated by adding marked noise to the sample image encoding. Based on training representation information, noise in the noise image is predicted. Model parameters are updated using differences between marked and predicted noise and between training semantics of the sample identifier and category semantics. The trained model performs denoising on noise images using text description information including sample identifiers to generate diffusion images.