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

Text-to-Image diffusion models have made tremendous progress over the pasttwo years, enabling the generation of highly realistic images based onopen-domain text descriptions. However, despite their success, textdescriptions often struggle to adequately convey detailed controls, even whencomposed of long and complex texts. Moreover, recent studies have also shownthat these models face challenges in understanding such complex texts andgenerating the corresponding images. Therefore, there is a growing need toenable more control modes beyond text description. In this paper, we introduceUni-ControlNet, a unified framework that allows for the simultaneousutilization of different local controls (e.g., edge maps, depth map,segmentation masks) and global controls (e.g., CLIP image embeddings) in aflexible and composable manner within one single model. Unlike existingmethods, Uni-ControlNet only requires the fine-tuning of two additionaladapters upon frozen pre-trained text-to-image diffusion models, eliminatingthe huge cost of training from scratch. Moreover, thanks to some dedicatedadapter designs, Uni-ControlNet only necessitates a constant number (i.e., 2)of adapters, regardless of the number of local or global controls used. Thisnot only reduces the fine-tuning costs and model size, making it more suitablefor real-world deployment, but also facilitate composability of differentconditions. Through both quantitative and qualitative comparisons,Uni-ControlNet demonstrates its superiority over existing methods in terms ofcontrollability, generation quality and composability. Code is available at\url{https://github.com/ShihaoZhaoZSH/Uni-ControlNet}.

Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models