# Manga Generation via Layout-controllable Diffusion

arXiv: https://arxiv.org/abs/2412.19303 · HTML: https://mlsanity.com/paper/2412.19303/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2024.

## Built on by (most cited first, 5 of 5)

- [Generative AI for Cel-Animation: A Survey](https://mlsanity.com/paper/2501.06250/) (2025)
- [ViStoryBench: Comprehensive Benchmark Suite for Story Visualization](https://mlsanity.com/paper/2505.24862/) (2025)
- [DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models](https://mlsanity.com/paper/2512.01686/) (2025)
- [MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation](https://mlsanity.com/paper/2605.28173/) (2026)
- [Visually Grounded Narratives: Reducing Cognitive Burden in Researcher-Participant Interaction](https://mlsanity.com/paper/2509.00381/) (2025)

## Abstract (arXiv)

Generating comics through text is widely studied. However, there are few studies on generating multi-panel Manga (Japanese comics) solely based on plain text. Japanese manga contains multiple panels on a single page, with characteristics such as coherence in storytelling, reasonable and diverse page layouts, consistency in characters, and semantic correspondence between panel drawings and panel scripts. Therefore, generating manga poses a significant challenge. This paper presents the manga generation task and constructs the Manga109Story dataset for studying manga generation solely from plain text. Additionally, we propose MangaDiffusion to facilitate the intra-panel and inter-panel information interaction during the manga generation process. The results show that our method particularly ensures the number of panels, reasonable and diverse page layouts. Based on our approach, there is potential to converting a large amount of textual stories into more engaging manga readings, leading to significant application prospects.