The recent advancements in diffusion models have significantly pushed the boundaries of image generation, inpainting, and 3D scene editing. A notable trend is the integration of 3D consistency into image inpainting, ensuring semantically coherent and realistic results across different viewpoints. This is achieved through innovative techniques that incorporate alternative perspectives into the denoising process, creating an inductive bias for 3D priors without explicit 3D supervision. Parallel high-resolution image generation has also seen progress, with methods leveraging asynchronous structure guidance to address pattern repetition and computational efficiency issues. These approaches enable faster generation speeds and reduced memory usage, crucial for interactive applications. Additionally, the field has witnessed novel methods for 3D scene editing that ensure view consistency and realism by warping attention features across multiple views, aligning them with scene geometry. In the realm of remote sensing, diffusion models are being enhanced to improve contextual coherence, capturing spatial interdependencies between foreground and background for more accurate landscape depiction. Data augmentation and knowledge distillation using diffusion models are also advancing SAR oil spill segmentation, addressing challenges related to limited labeled data and speckle noise. Overall, these developments highlight a shift towards more realistic, efficient, and contextually aware image generation and editing techniques, driven by advancements in diffusion models.