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

We introduce $\infty$-Diff, a generative diffusion model which directlyoperates on infinite resolution data. By randomly sampling subsets ofcoordinates during training and learning to denoise the content at thosecoordinates, a continuous function is learned that allows sampling at arbitraryresolutions. In contrast to other recent infinite resolution generative models,our approach operates directly on the raw data, not requiring latent vectorcompression for context, using hypernetworks, nor relying on discretecomponents. As such, our approach achieves significantly higher sample quality,as evidenced by lower FID scores, as well as being able to effectively scale tohigher resolutions than the training data while retaining detail.

$\infty$-Diff: Infinite Resolution Diffusion with Subsampled Mollified States