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

Despite the progress made in the style transfer task, most previous workfocus on transferring only relatively simple features like color or texture,while missing more abstract concepts such as overall art expression orpainter-specific traits. However, these abstract semantics can be captured bymodels like DALL-E or CLIP, which have been trained using huge datasets ofimages and textual documents. In this paper, we propose StylerDALLE, a styletransfer method that exploits both of these models and uses natural language todescribe abstract art styles. Specifically, we formulate the language-guidedstyle transfer task as a non-autoregressive token sequence translation, i.e.,from input content image to output stylized image, in the discrete latent spaceof a large-scale pretrained vector-quantized tokenizer, e.g., the discretevariational auto-encoder (dVAE) of DALL-E. To incorporate style information, wepropose a Reinforcement Learning strategy with CLIP-based language supervisionthat ensures stylization and content preservation simultaneously. Experimentalresults demonstrate the superiority of our method, which can effectivelytransfer art styles using language instructions at different granularities.Code is available at https://github.com/zipengxuc/StylerDALLE.

StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model