  Deep Generative Models (DGMs) are widely used to create innovative designs
across multiple industries, ranging from fashion to the automotive sector. In
addition to generating images of high visual quality, the task of structural
design generation imposes more stringent constrains on the semantic expression,
e.g., no floating material or missing part, which we refer to as plausibility
in this work. We delve into the impact of noise schedules of diffusion models
on the plausibility of the outcome: there exists a range of noise levels at
which the model's performance decides the result plausibility. Also, we propose
two techniques to determine such a range for a given image set and devise a
novel parametric noise schedule for better plausibility. We apply this noise
schedule to the training and sampling of the well-known diffusion model EDM and
compare it to its default noise schedule. Compared to EDM, our schedule
significantly improves the rate of plausible designs from 83.4% to 93.5% and
Fr\'echet Inception Distance (FID) from 7.84 to 4.87. Further applications of
advanced image editing tools demonstrate the model's solid understanding of
structure.
