English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep Generative Models (DGMs) are widely used to create innovative designsacross multiple industries, ranging from fashion to the automotive sector. Inaddition to generating images of high visual quality, the task of structuraldesign generation imposes more stringent constrains on the semantic expression,e.g., no floating material or missing part, which we refer to as plausibilityin this work. We delve into the impact of noise schedules of diffusion modelson the plausibility of the outcome: there exists a range of noise levels atwhich the model's performance decides the result plausibility. Also, we proposetwo techniques to determine such a range for a given image set and devise anovel parametric noise schedule for better plausibility. We apply this noiseschedule to the training and sampling of the well-known diffusion model EDM andcompare it to its default noise schedule. Compared to EDM, our schedulesignificantly improves the rate of plausible designs from 83.4% to 93.5% andFr\'echet Inception Distance (FID) from 7.84 to 4.87. Further applications ofadvanced image editing tools demonstrate the model's solid understanding ofstructure.

On the Noise Scheduling for Generating Plausible Designs with Diffusion Models