The efficient and controllable synthesis of visual content in digital media has practical application value for image editing, advertising production and creative design systems. In this paper, we propose an intelligent synthesis framework supported by diffusion generation, which uses stable diffusion redrawing backbone to complete region completion, maintains layout continuity using Canny guided structural branches, and combines adaptive mask refinement and continuous region fusion to improve boundary transition, semantic fidelity, and color consistency. The framework is evaluated on 100 public scene images and 300 supplementary edit samples with a uniform input resolution of 512×512. Quantitative results show that PSNR is 31.84 dB, SSIM is 0.921, and LPIPS is 0.087. The average inference time is 1.92 s/ picture on NVIDIA RTX 4090 platform. The visual consistency of the unstructured constraint variant is lower than that of the full model. The method also generates heat map analysis and multi-view comparison output, which supports the reproducibility evaluation and cross-scene deployment of digital media visual synthesis.
