Synthetic aperture radar (SAR) inherently encounters speckle noise as a characteristic challenge, which degrades image quality significantly. In recent years, learning-based methods have achieved remarkable success in SAR despeckling. However, these methods are often limited by complex speckle-noise patterns and the size of SAR images. To further improve despeckling quality and adapt arbitrary-size SAR images, we propose a novel method based on a conditional diffusion model coupled with a multi-scale and dual attention mechanism. During despeckling, the strong modeling capability of the diffusion model enables more accurate original clear scene restoration. Multi-scale features encompassing low-frequency large structure information and high-frequency details in SAR images are aggregated and input to position and channel dual attention mechanism, which effectively extracts global context information and establishes long-range dependencies, ensuring a broader perspective and more accurate distinction between structure textures and speckle noise. Using smooth noise estimation across overlapping patches to guide the sampling process, the proposed method is capable of handling arbitrary-size SAR images. Extensive experiment demonstrates that the proposed method has outstanding despeckling performance and flexible adaptability for arbitrary-size SAR images.