# Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork Source: [Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:28:36.143Z Authors: Xin Yuan, Zhe Lin, Jason Kuen, Jianming Zhang, John Collomosse Published: 2022-08-17T19:25:00.000Z ## Abstract We develop an approach for text-to-image generation that embraces additional retrieval images, driven by a combination of implicit visual guidance loss and generative objectives. Unlike most existing text-to-image generation methods which merely take the text as input, our method dynamically feeds cross-modal search results into a unified training stage, hence improving the quality, controllability and diversity of generation results. We propose a novel hypernetwork modulated visual-text encoding scheme to predict the weight update of the encoding layer, enabling effective transfer from visual information (e.g. layout, content) into the corresponding latent domain. Experimental results show that our model guided with additional retrieval visual data outperforms existing GAN-based models. On COCO dataset, we achieve better FID of $9.13$ with up to $3.5 \\times$ fewer generator parameters, compared with the state-of-the-art method. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()