The recent advancements in image editing and vision Transformers (ViTs) have shown a significant shift towards more efficient and precise model editing techniques. Researchers are increasingly focusing on developing methods that can data-efficiently correct predictive errors in pre-trained models, particularly in the context of subpopulation shifts. This trend is exemplified by the introduction of meta-learning hypernetworks to identify and fine-tune sparse subsets of model parameters, which has proven effective in enhancing the generalization and locality of edits. Additionally, the integration of Diffusion Transformers (DiT) into image editing frameworks has demonstrated superior performance in capturing long-range dependencies and generating high-quality edited images, especially in high-resolution scenarios. The use of multimodal exemplar-based editing and multi-reward conditioning in training models further underscores the move towards more nuanced and efficient editing processes. These developments not only improve the quality of edited images but also enhance the practical applicability of these models by reducing the need for task-specific optimization and increasing processing speed. Overall, the field is progressing towards more sophisticated, adaptable, and user-friendly image editing solutions.