title: RepText: Rendering Visual Text via Replicating
summary: Although contemporary text-to-image generation models have achieved
  remarkable breakthroughs in producing visually appealing images, their capacity
  to generate precise and flexible typographic elements, especially non-Latin
  alphabets, remains constrained. To address these limitations, we start from an
  naive assumption that text understanding is only a sufficient condition for
  text rendering, but not a necessary condition. Based on this, we present
  RepText, which aims to empower pre-trained monolingual text-to-image generation
  models with the ability to accurately render, or more precisely, replicate,
  multilingual visual text in user-specified fonts, without the need to really
  understand them. Specifically, we adopt the setting from ControlNet and
  additionally integrate language agnostic glyph and position of rendered text to
  enable generating harmonized visual text, allowing users to customize text
  content, font and position on their needs. To improve accuracy, a text
  perceptual loss is employed along with the diffusion loss. Furthermore, to
  stabilize rendering process, at the inference phase, we directly initialize
  with noisy glyph latent instead of random initialization, and adopt region
  masks to restrict the feature injection to only the text region to avoid
  distortion of the background. We conducted extensive experiments to verify the
  effectiveness of our RepText relative to existing works, our approach
  outperforms existing open-source methods and achieves comparable results to
  native multi-language closed-source models. To be more fair, we also
  exhaustively discuss its limitations in the end.
paper_authors._id: 68104dd9ec94d9d54ebde2c8
paper_authors.name: Haofan Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-06-04T12:24:52.715Z
paper_authors._id: 637745113a63a2983ffbde13
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
paper_authors.fullname: Haofan Wang
paper_authors.type: user
paper_authors.user: wanghaofan
paper_authors._id: 68104dd9ec94d9d54ebde2c9
paper_authors.name: Yujia Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T09:20:54.897Z
paper_authors._id: 66471d8f4356b3b33548ee95
paper_authors.avatarUrl: /avatars/783beebc837d91684f8a959733b48e5b.svg
paper_authors.fullname: Yujia Xu
paper_authors.type: user
paper_authors.user: YujiaX
paper_authors._id: 68104dd9ec94d9d54ebde2ca
paper_authors.name: Yimeng Li
paper_authors._id: 68104dd9ec94d9d54ebde2cb
paper_authors.name: Junchen Li
paper_authors._id: 68104dd9ec94d9d54ebde2cc
paper_authors.name: Chaowei Zhang
paper_authors._id: 68104dd9ec94d9d54ebde2cd
paper_authors.name: Jing Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-29T08:03:03.819Z
paper_authors._id: 6649b84af50d4711191ab04c
paper_authors.avatarUrl: /avatars/dfe85eb28ae970e718c37cc6bc459457.svg
paper_authors.fullname: WJ
paper_authors.type: user
paper_authors.user: SNOWAI
paper_authors._id: 68104dd9ec94d9d54ebde2ce
paper_authors.name: Kejia Yang
paper_authors._id: 68104dd9ec94d9d54ebde2cf
paper_authors.name: Zhibo Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-29T12:13:22.884Z
paper_authors._id: 66d963e52e82d53d3b81031b
paper_authors.avatarUrl: /avatars/302dbffc033ff47813a2435a2cec02f1.svg
paper_authors.fullname: Zhibo Chen
paper_authors.type: user
paper_authors.user: winhelp
paper_title: RepText: Rendering Visual Text via Replicating
paper_summary: Although contemporary text-to-image generation models have achieved
  remarkable breakthroughs in producing visually appealing images, their capacity
  to generate precise and flexible typographic elements, especially non-Latin
  alphabets, remains constrained. To address these limitations, we start from an
  naive assumption that text understanding is only a sufficient condition for
  text rendering, but not a necessary condition. Based on this, we present
  RepText, which aims to empower pre-trained monolingual text-to-image generation
  models with the ability to accurately render, or more precisely, replicate,
  multilingual visual text in user-specified fonts, without the need to really
  understand them. Specifically, we adopt the setting from ControlNet and
  additionally integrate language agnostic glyph and position of rendered text to
  enable generating harmonized visual text, allowing users to customize text
  content, font and position on their needs. To improve accuracy, a text
  perceptual loss is employed along with the diffusion loss. Furthermore, to
  stabilize rendering process, at the inference phase, we directly initialize
  with noisy glyph latent instead of random initialization, and adopt region
  masks to restrict the feature injection to only the text region to avoid
  distortion of the background. We conducted extensive experiments to verify the
  effectiveness of our RepText relative to existing works, our approach
  outperforms existing open-source methods and achieves comparable results to
  native multi-language closed-source models. To be more fair, we also
  exhaustively discuss its limitations in the end.
paper_ai_summary: RepText enhances pre-trained monolingual text-to-image models to generate precise multilingual text in specified fonts using ControlNet and a text perceptual loss.
paper_ai_keywords: ControlNet glyph text perceptual loss diffusion loss noisy glyph latent region masks text rendering