Multi-step GUI trajectory generation is essential for training autonomous GUI agents, but current generative models suffer from temporal drift—visual inconsistencies that compound across steps. Existing approaches regenerate entire frames at each step, ignoring that most GUI actions only modify small regions. We propose Copy-Then-Inpaint, a three-stage pipeline that addresses this by: (1) predicting change regions via a vision-language model, (2) applying masked inpainting to generate only changed content, and (3) compositing results to preserve unchanged pixels. On GEBench Type 2 (n = 200), our method significantly improves temporal consistency (CONS +5.7, p < 0.01) and overall quality (+6.1 GE-Score) without sacrificing task completion. Ablation studies confirm that semantic mask alignment is essential and that mask dilation is necessary for coherent generation at region boundaries.