The　production　of　lace　patterns　faces　significant　challenges　due　to　the　creative　limitations　and　high　costs　associated　with　manual　design.　Diffusion　models　offer　a　promising　solution　for　lace　image　generation,　with　U-Net-based　diffusion　architectures　being　widely　adopted　for　their　strong　multi-scale　feature　extraction　capabilities.　In　these　architectures,　the　design　of　skip　connections　plays　a　particularly　critical　role　in　preserving　fine-grained　details.　However,　existing　skip　connection　weight　tuning　methods　perform　poorly　when　applied　to　lace　images,　which　are　characterized　by　intricate　textures　and　complex　patterns.　These　methods　often　fail　to　effectively　maintain　the　detailed　structure　and　overall　integrity　of　lace　designs.　In　this　work,　we　adopt　the　EDM2　diffusion　model　for　lace　image　generation　and　introduce　a　training-free　skip　connection　weight　tuning　strategy.　During　the　inference　phase,　the　skip　connection　weights　in　the　U-Net　structure　are　selectively　adjusted:　shallow　layers　retain　their　original　weights,　while　the　weights　of　deeper　skip　connections　are　progressively　increased　up　to　1.1.　This　targeted　adjustment　enhances　the　model＇s　ability　to　preserve　fine　texture　information　from　deeper　features.　Experimental　results　and　analysis　demonstrate　that　the　proposed　method　consistently　reduces　both　FID　and　FDDinova　scores　across　multiple　training　epochs.　The　approach　is　simple　to　implement,　incurs　no　additional　training　cost,　and　exhibits　strong　practicality　and　scalability.　It　offers　a　new　perspective　for　generating　high-quality　lace　texture　images.　　©　2025　IEEE.