{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dense-text-to-image-generation-with-attention","title":"Dense Text-to-Image Generation with Attention Modulation","arxiv_id":"2308.12964","date":"2023-08-24","proceeding":"ICCV 2023 1","authors":["Yunji Kim","Jiyoung Lee","Jin-Hwa Kim","Jung-Woo Ha","Jun-Yan Zhu"],"abstract":"Existing text-to-image diffusion models struggle to synthesize realistic images given dense captions, where each text prompt provides a detailed description for a specific image region. 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