The field of image editing and generation is rapidly evolving, with a focus on developing more efficient and effective methods for editing and generating high-quality images. Recent research has explored the use of novel architectures and techniques, such as parameter-efficient multi-style Mixture-of-Experts Low-Rank Adaptation (MoE LoRA) and Frequency-Interactive Attention, to improve the quality and consistency of edited images. Additionally, there is a growing interest in developing methods that can generate images in a more controllable and flexible manner, such as text-to-image generation and scene text editing. Notable papers in this area include FIA-Edit, which achieves high-fidelity and semantically precise edits through a Frequency-Interactive Attention mechanism, and TripleFDS, which proposes a novel framework for scene text editing with disentangled modular attributes. Overall, the field is moving towards developing more advanced and sophisticated methods for image editing and generation, with a focus on improving quality, consistency, and controllability.