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

Free-form text prompts allow users to describe their intentions during imagemanipulation conveniently. Based on the visual latent space of StyleGAN[21] andtext embedding space of CLIP[34], studies focus on how to map these two latentspaces for text-driven attribute manipulations. Currently, the latent mappingbetween these two spaces is empirically designed and confines that eachmanipulation model can only handle one fixed text prompt. In this paper, wepropose a method named Free-Form CLIP (FFCLIP), aiming to establish anautomatic latent mapping so that one manipulation model handles free-form textprompts. Our FFCLIP has a cross-modality semantic modulation module containingsemantic alignment and injection. The semantic alignment performs the automaticlatent mapping via linear transformations with a cross attention mechanism.After alignment, we inject semantics from text prompt embeddings to theStyleGAN latent space. For one type of image (e.g., `human portrait'), oneFFCLIP model can be learned to handle free-form text prompts. Meanwhile, weobserve that although each training text prompt only contains a single semanticmeaning, FFCLIP can leverage text prompts with multiple semantic meanings forimage manipulation. In the experiments, we evaluate FFCLIP on three types ofimages (i.e., `human portraits', `cars', and `churches'). Both visual andnumerical results show that FFCLIP effectively produces semantically accurateand visually realistic images. Project page:https://github.com/KumapowerLIU/FFCLIP.

One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations