Index of everything on this site: https://ai.hiina.space/index.md
Several things look like gaps but are not. The role split itself is done: MangaFlow and the hobby repos already have it. Programmatic lettering placed off detected faces is done in TaleDiffusion, agent-comic-kit and book-to-comic. Reference-anchored per-panel generation is standard practice. And single-page text rendering is largely solved by GPT Image 2.
Six gaps are real, meaning I searched for them and they turned up absent. First, there is no human-validated readability or comprehension benchmark for generated comics. ViStoryBench excludes multi-panel layouts, MangaFlow's readability score is unvalidated, and CANVAS validates judges only on storyboard consistency. Second, no system closes the loop from a cold-reading VLM on assembled, lettered pages back to the storyboarder or author. MangaFlow confirms it has no final-page VLM critic. Third, there is no evidence on whether LLM-drawn SVG thumbnails improve modern edit-capable image models over text, so the best data is Control-GPT and Chat2SVG from the SD era. Fourth, there is no controlled multi-page whole-comic benchmark of 2026 image models, and no same-family vs cross-family VLM critique comparison on rendered visual output. Fifth, there are no published comic-reading numbers for current frontier models, and no LLM or VLM work that uses McCloud or Cohn as a rubric. Sixth, there is no agent driving Krita or Inkscape as a layered editor. That one is a gap mostly because nobody needs it: SVG generated by code does the job better. Search coverage was moderate rather than exhaustive. Webtoon and yonkoma generation and Chinese-venue work were not searched, and the field moves monthly, so these gap claims have a short shelf life.
Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space