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

The initial noise image has demonstrated a significant influence on imagegeneration, and manipulating the initial noise image can effectively increasecontrol over the generation. All of the current generation is based only on asingle initial noise drawn from a normal distribution, which may not be suitedto the desired content specified by the prompt. In this research, we propose anovel approach using pre-collected, semantically-informed pixel blocks frommultiple initial noises for the initial image construction to enhance controlover the image generation. The inherent tendencies of these pixel blocks caneasily generate specific content, thus effectively guiding the generationprocess towards the desired content. The pursuit of tailored initial imageconstruction inevitably leads to deviations from the normal distribution, andour experimental results show that the diffusion model exhibits a certaindegree of tolerance towards the distribution of initial images. Our approachachieves state-of-the-art performance in the training-free layout-to-imagesynthesis task, demonstrating the adaptability of the initial imageconstruction in guiding the content of the generated image. Our code will bemade publicly available.

Semantic-Driven Initial Image Construction for Guided Image Synthesis in Diffusion Model