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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Diffusion models have recently exhibited remarkable abilities to synthesizestriking image samples since the introduction of denoising diffusionprobabilistic models (DDPMs). Their key idea is to disrupt images into noisethrough a fixed forward process and learn its reverse process to generatesamples from noise in a denoising way. For conditional DDPMs, most existingpractices relate conditions only to the reverse process and fit it to thereversal of unconditional forward process. We find this will limit thecondition modeling and generation in a small time window. In this paper, wepropose a novel and flexible conditional diffusion model by introducingconditions into the forward process. We utilize extra latent space to allocatean exclusive diffusion trajectory for each condition based on some shiftingrules, which will disperse condition modeling to all timesteps and improve thelearning capacity of model. We formulate our method, which we call\textbf{ShiftDDPMs}, and provide a unified point of view on existing relatedmethods. Extensive qualitative and quantitative experiments on image synthesisdemonstrate the feasibility and effectiveness of ShiftDDPMs.

ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories