Text-to-image personalization lets users teach a diffusion model a new concept from just a few reference images. But existing methods typically train each concept independently, which is inefficient and does not support adding concepts over time. A new paper on arXiv introduces residual-space adaptation to address this limitation.
The approach adjusts the model in a low-dimensional residual space rather than updating all weights. This is designed to enable continual learning, so a model can acquire new concepts sequentially without forgetting previously learned ones. The method is aimed at few-shot settings, where only a handful of images are available per concept.
By operating in residual space, the technique seeks to make ongoing personalization more scalable and practical than independent training. The paper does not report experimental results in the abstract, so the effectiveness of the method is not yet verified.