The field of image editing and synthesis is currently witnessing significant advancements with the development of innovative diffusion-based models. These models are capable of achieving high-fidelity visual content while preserving structural integrity and semantic consistency. The trend is shifting towards designing flexible, training-free frameworks that can seamlessly integrate with existing architectures and support complex geometric transformations, fine-grained modifications, and cross-modal alignment. Furthermore, researchers are exploring the potential of diffusion models for zero-shot image editing, text-guided semantic manipulation, and inference-time optimization. Noteworthy papers in this area include: DanceText, which introduced a layered editing strategy for multilingual text editing in images, and CrossWKV, which proposed a novel cross-attention mechanism for state-based models. Additionally, InstaRevive and Structure-Preserving Zero-Shot Image Editing via Stage-Wise Latent Injection in Diffusion Models demonstrate promising results in image enhancement and zero-shot image editing, respectively. ReflectionFlow and Towards Generalized and Training-Free Text-Guided Semantic Manipulation also showcase innovative approaches to inference-time optimization and text-guided semantic manipulation.