The field of text-to-image and image editing is moving towards more controllable and efficient methods. Recent developments have focused on improving the quality and consistency of generated images, as well as enabling fine-grained editing capabilities. Notably, innovative approaches have been proposed to address the challenges of maintaining background similarity, reducing cognitive burden, and generating coherent narratives. These advancements have the potential to significantly impact various applications, including story visualization, image editing, and narrative inquiry. Noteworthy papers include: LatentEdit, which introduces an adaptive latent fusion framework for consistent semantic editing. Visually Grounded Narratives, which proposes a new paradigm for narrative inquiry, alleviating the cognitive burden of interpreting extensive text-based materials. TaleDiffusion, which generates multi-character stories with accurate dialogue rendering. Plot'n Polish, which enables zero-shot story visualization and disentangled editing. EditIDv2, which achieves editable ID customization with data-lubricated ID feature integration for text-to-image generation.