Pith. sign in

REVIEW 6 major objections 6 minor 1 cited by

Manga Generation via Layout-controllable Diffusion

T0 review · 6 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that a single forward pass of a layout-controllable diffusion model can generate a complete multi-panel manga page from per-panel text scripts, matching the number of panels to the scripts while producing diverse…

desk verdict A legit new task and a useful dataset, but the layout-control claim is asserted from qualitative examples while the numbers only measure global image similarity. read the letter →

arxiv 2412.19303 v1 pith:UE7MAAK6 submitted 2024-12-26 cs.CV

Siyu Chen , Dengjie Li , Zenghao Bao , Yao Zhou , Lingfeng Tan , Yujie Zhong , Zheng Zhao This is my paper · ORCID
classification cs.CV
keywords mangagenerationtext-to-imagediffusionlayoutcontrolmulti-panelManga109Storydatasettransformerstoryvisualizationspeechbubblemasking
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces manga generation as a distinct task: turning a plain-text story into a complete multi-panel Japanese manga page in a single forward pass, rather than generating one image at a time. To study it, the authors build Manga109Story, a dataset of over 21,000 pages whose panels are captioned by a multimodal language model guided by structured metadata, including panel coordinates, character and text annotations, dialogue-speaker pairings, and a predicted panel reading order. They then propose MangaDiffusion, a latent diffusion transformer whose attention operates both within each panel and across panels at matched positions, so each panel's content is controlled by its own script while the model learns page-level layout and reading order. The paper reports that the method matches the number of generated panels to the number of scripts, produces reasonable and diverse layouts, and reduces garbled speech-bubble artifacts through a masking strategy. If these claims hold, the approach opens a practical route from large text corpora to illustrated manga reading material.

What carries the argument

The load-bearing object is the MangaDiffusion transformer, a latent diffusion model in which each panel of a page is encoded separately by a pretrained VAE and patchified into tokens, then processed by two alternating attention blocks: an intra-panel block that mixes all tokens within one panel under the control of that panel's caption, using adaLN-single conditioning (an adaptive layer-normalization mechanism that injects timestep and caption features), and an inter-panel block that mixes tokens at the same spatial position across all panels, so layout and reading-order information can flow between panels without mixing their contents. Two masks carry the argument: an inter-panel mask that drops padding panels, empty slots with 'EMPTY' captions used to batch pages with fewer than the maximum of eight panels, from cross-panel attention, and an intra-panel mask built from a speech-bubble segmentation model that excludes bubble regions from attention and from the denoising loss. At inference, the K decoded panel images are merged into the final page by taking the pixel-wise minimum at each position, a choice that forces the model to coordinate overlapping or adjacent regions so seams do not double-expose. The model is initialized from a pretrained video-diffusion transformer, treating panels as a sequence of frames, and trained with the standard denoising objective plus the mask-gated loss.

What would settle it

Have human annotators score whether each Manga109Story caption actually describes its own panel on a random sample of pages; if a sizable fraction of captions fail this check, the claimed semantic correspondence between panels and scripts is not being learned from verified data. Separately, generate pages from scripts of every length from one to eight and count the panels in the output: the claim of exact panel-count control is falsified if outputs systematically add or drop panels relative to the scripts.

Watch

Extended reading notes

Core claim

The central claim is that multi-panel manga pages can be generated end-to-end, in a single forward pass, from a batch of per-panel scripts, with the number of panels locked to the number of scripts and the page layout left free rather than fixed to a grid. The authors argue that previous story-generation models either synthesize one image per prompt, needing multiple passes and ignoring page-level structure, or produce a single image that cannot be given per-panel content; manga generation, by contrast, must respect reading order, layout, character consistency, and panel-script correspondence within one page. MangaDiffusion achieves this by splitting a page into panel images, encoding them into latent tokens, and running transformer blocks that first mix tokens inside each panel, conditioned on that panel's caption, and then mix tokens across panels at identical spatial positions, which lets the model learn layouts and cross-panel coherence. A padding mechanism with an inter-panel mask makes the model tolerant of pages with anywhere from one to eight panels, and an intra-panel mask hides speech-bubble regions from both attention and loss so generated pages carry fewer garbled text bubbles. Quantitative results show FID and CLIP-I scores comparable to a fine-tuned text-to-image baseline on the Manga109Story test set, while qualitative results emphasize exact panel-count control and flexible layouts.

Load-bearing premise

The whole training signal for what each panel depicts rests on captions produced automatically by a multimodal language model, guided by an automatically predicted reading order, with no human check, so if a caption describes the wrong panel or the order is wrong, the model is trained to pair stories with the wrong images.

Editorial extensions

If this is right

  • Panel-count fidelity: given K scripts, the model outputs exactly K panels, so a story split by an LLM into any number of scenes (up to eight) yields a page with that many panels and no manual layout work.
  • Flexible layouts in reading order: because inter-panel attention mixes same-position tokens across panels and the model never sees horizontally flipped pages, generated pages place panels in a learnable, non-grid arrangement that follows the training distribution's reading order.
  • Cross-panel coherence: the inter-panel block propagates visual information between panels, so characters and backgrounds stay roughly consistent across a page, and the paper reports that overlapping regions of adjacent panels are nearly identical in the merged page.
  • Cleaner practical output: masking speech-bubble regions during training reduces garbled text bubbles in generated pages, which suits applications where dialogue text is added later by the user.
  • A usable text-to-manga pipeline: an LLM segments a plain-text story into scripts and MangaDiffusion renders the page in one pass, enabling automatic conversion of textual stories into manga form at scale.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Given the paper's own acknowledgment that character consistency and panel-caption consistency have room to develop, a human-validated slice of Manga109Story captions would isolate how much of that gap comes from noisy training captions rather than model capacity, since the captions are generated without verification.
  • Because the model never sees mirrored pages, the layout and reading-order prior is inferred from right-to-left Japanese pages only; feeding the same pipeline mirrored pages or left-to-right comics would test whether the learned layout signal is transferable or style-locked.
  • The paper proposes that panels can stand for objects in multi-object generation, but it does not evaluate that setting; a natural next experiment is to run the same intra/inter block split on a multi-object benchmark with per-object prompts and measure whether count fidelity and coherence carry over.
  • The pixel-wise minimum merge works cleanly only when panels tile the page without meaningful overlap; the near-identical overlap regions the paper reports suggest the inter-panel block implicitly learns to duplicate content across seams, which is a testable hypothesis about what the cross-panel attention actually encodes.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

6 major / 6 minor

Summary. The paper introduces a new task, manga generation from plain text: given a story, the system outputs a complete multi-panel manga page. The authors construct the Manga109Story dataset from Manga109 by combining panel coordinates, character/text information, panel reading order, and MLLM-generated captions and story summaries. They propose MangaDiffusion, a diffusion transformer with intra-panel and inter-panel attention blocks, trained on panel images extracted from full pages. At inference, an LLM splits the story into K scripts, the model generates K panels in one forward pass, and a pixel-wise minimum merge produces the final page. The paper reports FID and CLIP-I on a 163-page single-title test set and provides qualitative examples.

Significance. If the claims were fully substantiated, this would be the first end-to-end text-to-manga-page system that controls panel count and layout while maintaining inter-panel coherence, and the Manga109Story dataset would be a useful community resource. The architecture idea of separating intra-panel and inter-panel attention, and masking padding panels, is clean and potentially reusable. However, the current evaluation does not measure the central layout and panel-count claims, and the small quantitative margin over a fine-tuned baseline is not robustly established. The contribution is therefore not yet demonstrated at the level promised by the abstract.

major comments (6)
  1. [Section 5.2, Table 1] The central claims of panel-count control and layout quality are not measured by the reported metrics. FID assesses global image distribution and CLIP-I measures whole-page similarity; neither can detect an incorrect number of panels or an invalid layout. The abstract and Section 5.3 state that MangaDiffusion 'particularly ensures the number of panels, reasonable and diverse page layouts,' but this is supported only by hand-picked qualitative examples in Figure 6. Please add panel-level metrics such as panel-count accuracy, average panel IoU with ground-truth panel coordinates, reading-order accuracy, and a human evaluation of layout plausibility.
  2. [Section 4.2 and 4.3] The claim that the model 'ensures the number of panels' is partly circular. The model always produces 8 panel latents; padding panels are masked out in the inter-panel attention, and the final page is composited from exactly K panels, where K is determined by the LLM script split. The generative model never chooses the number of panels, so no learned behavior is being measured by the observed agreement between K and the number of scripts. This should be framed as a design guarantee of the pipeline, and the LLM's script-segmentation accuracy should be evaluated separately if the claim concerns the full system.
  3. [Section 4.2, inference (pixel-wise minimum)] The pixel-wise minimum merging rule can conceal layout and reading-order failures. When panels overlap, the darkest pixel wins at each location regardless of which panel's content is correct, so an incorrectly arranged set of panels can still yield a visually plausible page. The supplementary observation in Figure 7 that overlapping content is 'almost identical' in one selected example does not establish that the merged page's layout matches the ground-truth panel arrangement. Without comparing the merged page to the ground-truth panel coordinates (e.g., by panel IoU or ordering accuracy), the qualitative layout claims in Section 5.3 remain unverified.
  4. [Section 5.2 and 5.4] The quantitative advantage over the fine-tuned Pixart-Σ baseline is small and unreplicated. On a single 163-page title, MangaDiffusion yields FID 143.5 vs 149.1 and CLIP-I 83.6 vs 84.6, with no confidence intervals or multiple random seeds; the CLIP-I value is actually lower for MangaDiffusion. The paper should report variance across seeds or runs to support any claim of superiority; as written, only 'comparable' is defensible.
  5. [Section 5.4 (bubble ablation)] The bubble-removal ablation (MangaDiffusion wo.bubble) has substantially worse FID and CLIP-I (179.8 and 75.7) than the version that retains bubbles, so the automated metrics contradict the claimed practical benefit of removing speech bubbles. The explanation that ground-truth pages contain bubbles accounts for the score drop, but it also demonstrates that FID and CLIP-I are not aligned with the stated application goal. A task-specific evaluation, such as a human preference study on whether the bubble-free output is more usable, or an evaluation of subsequent text insertion, is needed to support this contribution.
  6. [Section 3] The quality of the Manga109Story captions is not validated. The captions are produced by GPT-4o from an XML file that includes panel reading order from an automatic estimator. If the panel order estimator makes a mistake, the MLLM is asked to describe panels in the wrong narrative order, and the resulting captions are still used as ground truth. No human validation or error analysis on a random sample is reported, so the claimed semantic correspondence between panel drawings and panel scripts cannot be independently assessed.
minor comments (6)
  1. [Throughout] There are multiple typos, including 'MangaDiffuion' in the caption of Figure 6, 'datset' in Section 3, 'EMTPY' in Figure 3, and 'specker detection' in the Related Work section.
  2. [Section 5.1] The experimental protocol omits several inference details: the number of sampling steps, the classifier-free guidance scale (if any), and the random seeds; please specify these for reproducibility.
  3. [Section 5.2] The baseline prompting is not fully described: the paper states that T2I models take 'concatenated panel captions,' but the exact concatenation format is not given.
  4. [Section 3] The paper does not provide a link to the Manga109Story dataset; since dataset construction is a stated contribution, a release plan or URL should be included.
  5. [Section 4.3, Equation (1)] The notation 'z /∈ M' is ambiguous because z is a latent tensor; please indicate whether the mask applies per token or per pixel and how the mask is downsampled to latent resolution.
  6. [Figure 4] Figure 4 is difficult to read; labeling the intra-panel and inter-panel blocks and the mask application more clearly would improve clarity.

Circularity Check

1 steps flagged · score 6.0 of 10

The paper's headline claim that MangaDiffusion 'particularly ensures the number of panels' is a construction-level guarantee (K scripts always decode to K panels), so that sub-claim reduces to the pipeline definition; the learned layout and content claims are not circular.

  1. self definitional [Section 4.2 (Manga Pipeline), Section 4.3 (Architecture), Section 5.3 (Qualitative Results)]
    "we split the user’s plain text into K continuous script segments, which are then inputted into the generation model to control the generation of each panel image. / The scripts and randomly sampled Gaussian noise are then fed into our proposed MangaDiffusion model for manga generation, resulting in K ordered panels. / The inter-panel mask is used to mask out the padding panels, so that these padding panels do not participate in the attention calculation of the inter-panel block."

    The number of panels is an input, not an output: the LLM fixes K, the model is always given K non-padding latent panel slots (padded to 8 only for batching), the padding is masked out of attention, and the page is assembled from the K decoded panels by pixel-wise minimum. Therefore the 'ensured' panel count is identical to the construction itself; Section 5.3 presents this design property as an empirical result. Layout reasonableness and diversity, character consistency, and caption relevance remain learned, empirically testable claims and are not circular.

full rationale

The only load-bearing reduction-by-construction I can exhibit is the panel-count claim. In the pipeline, K is chosen by LLM planning, K panel latents are always decoded, and the final page is a min-merge of exactly those K panels, so 'the number of panels generated ... is consistent with the prompt' is a restatement of the architecture rather than a learned prediction. This is partial circularity because the same paper's other headline properties (layout diversity, layout reasonableness, inter-panel coherence) are genuinely learned and supported by qualitative examples and by FID/CLIP-I, albeit not by panel-level metrics. I found no load-bearing self-citations: reference [28] (co-authored by Y. Zhong) appears only in related work, and the dataset/order-estimator citations [20, 25, 34] are external. The reliance on GPT-4o captions and the Kovanen-Aizawa reading order without human validation is a data-quality risk, and Section 6 candidly admits the consistency limitations; these are correctness concerns, not circularity. The absence of quantitative panel-count/layout metrics means the construction-level guarantee is the only evidence offered for that sub-claim, which is why it is flagged rather than dismissed.

Assumptions & free parameters 5 free parameters · 5 assumptions · 1 invented entities

The central claim rests on the quality of the automatically generated dataset and on several hand-chosen composition choices; these are the main uncharged premises.

free parameters (5)
  • maximum panel count = 8
    Pages with more than 8 panels are discarded and shorter pages are padded to 8, which fixes the maximum output complexity and biases training toward the most common layouts.
  • padding pixel value = 1
    Padded panel images are filled with white (value 1), a hand choice used with the pixel-wise minimum merge and attention masks.
  • pixel-wise minimum merge rule = min
    K panel images are composed by taking the minimum pixel value at each coordinate, relying on the assumption that overlapping regions are consistent.
  • EMPTY caption token = "EMPTY"
    A fixed caption for padded panels so that captions and panels remain one-to-one in the batch.
  • bubble mask expansion = max bounding box of segmentation
    Segmentation outputs are converted to a single maximum box per bubble to define the intra-panel mask, which may over-mask adjacent content.
assumptions (5)
  • domain assumption GPT-4o generated panel captions and story summaries are semantically correct.
    The Manga109Story dataset is built entirely from MLLM outputs; no human verification is reported, so any systematic caption error directly trains the model to make wrong text-image associations. Location: Section 3.
  • domain assumption The panel order estimator (Kovanen and Aizawa 2015) outputs the true reading order.
    Panel indices assigned from this estimator order both the captions and the story summary; a wrong order corrupts narrative coherence in the training data. Location: Section 3, Figure 2.
  • domain assumption Speech bubble segmentation masks, taken as maximum bounding boxes, cover the bubbles without discarding essential visuals.
    The intra-panel mask removes these regions from attention and loss calculation; overly aggressive masking hides content the model should learn. Location: Section 4.3.
  • domain assumption Resizing manga pages to 512x384 preserves layout structure.
    All pages are resized for training; manga pages have varied aspect ratios and panel arrangements that may not survive uniform resizing. Location: Section 5.1.
  • ad hoc to paper Pixel-wise minimum merging of independently generated panels produces a coherent page.
    The paper introduces this composition rule without justification; it only works if overlapping panel regions are nearly identical, which the model is not explicitly trained to guarantee. Location: Section 4.2, Figure 3.
invented entities (1)
  • Padding panels (blank white panels with EMPTY captions)
    purpose: Standardize all inputs to 8 panels and give the model a fixed-size tensor batch.
    A construction of the paper's pipeline; it has no external observable counterpart, and it is masked during attention to avoid interfering with real panels.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Manga Generation via Layout-controllable Diffusion." pith.science (2026). https://pith.science/paper/UE7MAAK6

@misc{pith2026241219303,
  author       = {Pith},
  title        = {Pith review of: Manga Generation via Layout-controllable Diffusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UE7MAAK6}},
  note         = {Machine review of arXiv:2412.19303}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2412.19303 by the authors.

Figure 1
Figure 1. Difference between story generation task and manga [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The construction process of Manga109Story dataset. The Manga109 dataset includes basic information such as coordinates [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The entire pipeline of our proposed manga generation method. Users input a plain text story, and with the help of LLM, we plan [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Architecture of MangaDiffusion. During the training stage, we split panel images from a complete manga page. A padding [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Illustration of intra-panel mask. The first row represents [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Visualization results of MangaDiffuion and other T2I methods. Each row represents a story, and the text above the images shows [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: The visualization of panels and the corresponding manga page. We can see that the arrangement of the content of each panel [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: The details of Manga109Story construction process. The presented XML file is synthesized by integrating the data from the [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Preliminary Explorations with GPT-4o(mni) Native Image Generation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A qualitative exploration showing GPT-4o image generation excels at stylization, editing, and personalization but struggles with spatial reasoning, knowledge-based accuracy, and temporal prediction.

Reference graph

Works this paper leans on

62 extracted references · 40 canonical work pages · cited by 1 Pith paper

  1. [1]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ah- mad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 ,

    arXiv

  2. [2]

    Manga-speech-bubble-segmentation

    AksharPatel. Manga-speech-bubble-segmentation. https: / / huggingface . co / AksharPatel / manga - speech-bubble-segmentation. 6

    work page

  3. [3]

    Interactive segmentation for manga

    Yuji Aramaki, Yusuke Matsui, Toshihiko Yamasaki, and Kiyoharu Aizawa. Interactive segmentation for manga. In ACM SIGGRAPH 2014 Posters, pages 1–1. 2014. 3

    work page 2014

  4. [4]

    Auto- matic stylistic manga layout.ACM Transactions on Graphics (TOG), 31(6):1–10, 2012

    Ying Cao, Antoni B Chan, and Rynson WH Lau. Auto- matic stylistic manga layout.ACM Transactions on Graphics (TOG), 31(6):1–10, 2012. 3

    work page 2012

  5. [5]

    Look over here: Attention-directing composition of manga elements

    Ying Cao, Rynson WH Lau, and Antoni B Chan. Look over here: Attention-directing composition of manga elements. ACM Transactions on Graphics (TOG) , 33(4):1–11, 2014. 3

    work page 2014

  6. [6]

    Character-centric story visualization via visual planning and token alignment

    Hong Chen, Rujun Han, Te-Lin Wu, Hideki Nakayama, and Nanyun Peng. Character-centric story visualization via visual planning and token alignment. arXiv preprint arXiv:2210.08465, 2022. 2

    arXiv 2022

  7. [7]

    Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis

    Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023. 5

    arXiv

  8. [8]

    Pixart- \sigma: Weak-to-strong training of diffusion transformer for 4k text-to-image generation

    Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart- \sigma: Weak-to-strong training of diffusion transformer for 4k text-to-image generation. arXiv preprint arXiv:2403.04692, 2024. 6, 7

    arXiv 2024

Show all 62 references
  1. [9]

    Line-based drawing style description for manga classification

    Wei-Ta Chu and Ying-Chieh Chao. Line-based drawing style description for manga classification. In Proceedings of the 22Nd ACM international conference on multimedia , pages 781–784, 2014. 3

    2014

  2. [10]

    Unconstrained text detection in manga: a new dataset and baseline

    Juli ´an Del Gobbo and Rosana Matuk Herrera. Unconstrained text detection in manga: a new dataset and baseline. InCom- puter Vision–ECCV 2020 Workshops: Glasgow, UK, Au- gust 23–28, 2020, Proceedings, Part III 16, pages 629–646. Springer, 2020. 3

    2020

  3. [11]

    Cogview: Mastering text-to-image generation via transformers

    Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, et al. Cogview: Mastering text-to-image generation via transformers. Advances in neural information processing systems, 34:19822–19835, 2021. 2

    2021

  4. [12]

    Scaling recti- fied flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling recti- fied flow transformers for high-resolution image synthesis. In Forty-first International Conference on Mach...

    2024

  5. [13]

    Talecrafter: Interactive story visualization with multiple characters

    Yuan Gong, Youxin Pang, Xiaodong Cun, Menghan Xia, Yingqing He, Haoxin Chen, Longyue Wang, Yong Zhang, Xintao Wang, Ying Shan, et al. Talecrafter: Interactive story visualization with multiple characters. arXiv preprint arXiv:2305.18247, 2023. 2

    2023 arXiv

  6. [14]

    Generative adversarial networks

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Commu- nications of the ACM, 63(11):139–144, 2020. 2

    2020

  7. [15]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 2, 6

    2020

  8. [16]

    Automatic preview generation of comic episodes for digitized comic search

    Keiichiro Hoashi, Chihiro Ono, Daisuke Ishii, and Hiroshi Watanabe. Automatic preview generation of comic episodes for digitized comic search. In Proceedings of the 19th ACM international conference on Multimedia , pages 1489–1492,

  9. [17]

    Separation of line drawings and screentones of manga

    K Ito, Y Matsui, T Yamasaki, and K Aizawa. Separation of line drawings and screentones of manga. In Proceedings Eurographics, 2015. 3

    2015

  10. [18]

    Auto-encoding variational bayes

    Diederik P Kingma. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 6

    2013 arXiv

  11. [19]

    Digital reconstruction of halftoned color comics

    Johannes Kopf and Dani Lischinski. Digital reconstruction of halftoned color comics. ACM Transactions on Graphics (TOG), 31(6):1–10, 2012. 3

    2012

  12. [20]

    A layered method for determining manga text bubble reading order

    Samu Kovanen and Kiyoharu Aizawa. A layered method for determining manga text bubble reading order. In 2015 IEEE International Conference on Image Processing (ICIP), pages 4283–4287, 2015. 2, 3

    2015

  13. [21]

    Flux.1 [dev]

    Black Forest Labs. Flux.1 [dev]. https : / / huggingface . co / black - forest - labs / FLUX.1-dev. 7

  14. [22]

    Word-level fine-grained story visualization

    Bowen Li. Word-level fine-grained story visualization. In European Conference on Computer Vision, pages 347–362. Springer, 2022. 2

    2022

  15. [23]

    Content-sensitive screening in black and white

    Hua Li and David Mould. Content-sensitive screening in black and white. In International Conference on Com- puter Graphics Theory and Applications , pages 166–172. SCITEPRESS, 2011. 3

    2011

  16. [24]

    Storygan: A sequential conditional gan for story vi- sualization

    Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao. Storygan: A sequential conditional gan for story vi- sualization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6329–6338,

  17. [25]

    Manga109dialog: A large-scale dialogue dataset for comics speaker detection

    Yingxuan Li, Kiyoharu Aizawa, and Yusuke Matsui. Manga109dialog: A large-scale dialogue dataset for comics speaker detection. In Proceedings of the IEEE International Conference on Multimedia and Expo, 2024. 2, 3

    2024

  18. [26]

    Reference- based screentone transfer via pattern correspondence and regularization

    Zhansheng Li, Nanxuan Zhao, Zongwei Wu, Yihua Dai, Junle Wang, Yanqing Jing, and Shengfeng He. Reference- based screentone transfer via pattern correspondence and regularization. In Computer Graphics Forum, page e14800. Wiley Online Library, 2023. 3

    2023

  19. [27]

    Sketch2manga: Shaded manga screening from sketch with diffusion models

    Jian Lin, Xueting Liu, Chengze Li, Minshan Xie, and Tien- Tsin Wong. Sketch2manga: Shaded manga screening from sketch with diffusion models. In 2024 IEEE International Conference on Image Processing (ICIP), pages 2389–2395. IEEE, 2024. 3 9

    2024

  20. [28]

    Intelligent grimm-open-ended visual storytelling via latent diffusion models

    Chang Liu, Haoning Wu, Yujie Zhong, Xiaoyun Zhang, Yan- feng Wang, and Weidi Xie. Intelligent grimm-open-ended visual storytelling via latent diffusion models. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6190–6200, 2024. 2

    2024

  21. [29]

    Latte: Latent diffusion transformer for video generation

    Xin Ma, Yaohui Wang, Gengyun Jia, Xinyuan Chen, Zi- wei Liu, Yuan-Fang Li, Cunjian Chen, and Yu Qiao. Latte: Latent diffusion transformer for video generation. arXiv preprint arXiv:2401.03048, 2024. 6

    2024 arXiv

  22. [30]

    Integrating visuospa- tial, linguistic and commonsense structure into story visual- ization

    Adyasha Maharana and Mohit Bansal. Integrating visuospa- tial, linguistic and commonsense structure into story visual- ization. arXiv preprint arXiv:2110.10834, 2021. 2

    2021 arXiv

  23. [31]

    Im- proving generation and evaluation of visual stories via se- mantic consistency

    Adyasha Maharana, Darryl Hannan, and Mohit Bansal. Im- proving generation and evaluation of visual stories via se- mantic consistency. arXiv preprint arXiv:2105.10026, 2021. 2

    2021 arXiv

  24. [32]

    Storydall-e: Adapting pretrained text-to-image transformers for story continuation

    Adyasha Maharana, Darryl Hannan, and Mohit Bansal. Storydall-e: Adapting pretrained text-to-image transformers for story continuation. InEuropean Conference on Computer Vision, pages 70–87. Springer, 2022. 2

    2022

  25. [33]

    Interactive manga retargeting

    Yusuke Matsui, Toshihiko Yamasaki, and Kiyoharu Aizawa. Interactive manga retargeting. In ACM SIGGRAPH 2011 Posters, pages 1–1. 2011. 3

    2011

  26. [34]

    Sketch-based manga retrieval using manga109 dataset

    Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa. Sketch-based manga retrieval using manga109 dataset. Mul- timedia tools and applications , 76:21811–21838, 2017. 2, 3

    2017

  27. [35]

    Synthesizing coherent story with auto-regressive la- tent diffusion models

    Xichen Pan, Pengda Qin, Yuhong Li, Hui Xue, and Wenhu Chen. Synthesizing coherent story with auto-regressive la- tent diffusion models. In Proceedings of the IEEE/CVF Win- ter Conference on Applications of Computer Vision , pages 2920–2930, 2024. 2

    2024

  28. [36]

    Scalable diffusion models with transformers

    William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 4195–4205,

  29. [37]

    Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 2

    2023 arXiv

  30. [38]

    Manga colorization

    Yingge Qu, Tien-Tsin Wong, and Pheng-Ann Heng. Manga colorization. ACM Transactions on Graphics (ToG), 25(3): 1214–1220, 2006. 3

    2006

  31. [39]

    Richness-preserving manga screening

    Yingge Qu, Wai-Man Pang, Tien-Tsin Wong, and Pheng- Ann Heng. Richness-preserving manga screening. ACM Transactions on Graphics (TOG), 27(5):1–8, 2008. 3

    2008

  32. [40]

    Make-a-story: Visual memory conditioned consistent story generation

    Tanzila Rahman, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Shweta Mahajan, and Leonid Sigal. Make-a-story: Visual memory conditioned consistent story generation. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2493–2502, 2023. 2

    2023

  33. [41]

    Zero-shot text-to-image generation

    Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea V oss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International confer- ence on machine learning, pages 8821–8831. Pmlr, 2021. 2

    2021

  34. [42]

    Hierarchical text-conditional image gener- ation with clip latents

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image gener- ation with clip latents. arXiv preprint arXiv:2204.06125, 1 (2):3, 2022. 2

    2022 arXiv

  35. [43]

    An active contour model for speech balloon detection in comics

    Christophe Rigaud, Jean-Christophe Burie, Jean-Marc Ogier, Dimosthenis Karatzas, and Joost Van de Weijer. An active contour model for speech balloon detection in comics. In 2013 12th International Conference on Document Analy- sis and Recognition, pages 1240–1244. IEEE, 2013. 3

    2013

  36. [44]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 2

    2022

  37. [45]

    Photorealistic text-to-image diffusion models with deep language understanding

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information...

    2022

  38. [46]

    Reference-based manga colorization by graph correspondence using quadratic programming

    Kazuhiro Sato, Yusuke Matsui, Toshihiko Yamasaki, and Kiyoharu Aizawa. Reference-based manga colorization by graph correspondence using quadratic programming. InSIG- GRAPH Asia 2014 Technical Briefs, pages 1–4. 2014. 3

    2014

  39. [47]

    Lazy- brush: Flexible painting tool for hand-drawn cartoons

    Daniel S `ykora, John Dingliana, and Steven Collins. Lazy- brush: Flexible painting tool for hand-drawn cartoons. In Computer Graphics Forum , pages 599–608. Wiley Online Library, 2009. 3

    2009

  40. [48]

    Layout analysis of tree-structured scene frames in comic images

    Takamasa Tanaka, Kenji Shoji, Fubito Toyama, and Juichi Miyamichi. Layout analysis of tree-structured scene frames in comic images. InIJCAI, pages 2885–2890. Citeseer, 2007. 3

    2007

  41. [49]

    Synthesis of screentone patterns of manga characters

    Koki Tsubota, Daiki Ikami, and Kiyoharu Aizawa. Synthesis of screentone patterns of manga characters. In 2019 IEEE international symposium on multimedia (ISM) , pages 212–

    2019

  42. [50]

    Shading-guided manga screening from reference

    Huisi Wu, Ziheng Ma, Wenliang Wu, Xueting Liu, Chengze Li, and Zhenkun Wen. Shading-guided manga screening from reference. IEEE Transactions on Visualization and Computer Graphics, 2023. 3

    2023

  43. [51]

    Mangawall: Generat- ing manga pages for real-time applications

    Zhipeng Wu and Kiyoharu Aizawa. Mangawall: Generat- ing manga pages for real-time applications. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 679–683. IEEE, 2014. 3

    2014

  44. [52]

    Manga filling style conversion with screentone varia- tional autoencoder

    Minshan Xie, Chengze Li, Xueting Liu, and Tien-Tsin Wong. Manga filling style conversion with screentone varia- tional autoencoder. ACM Transactions on Graphics (TOG), 39(6):1–15, 2020. 3

    2020

  45. [53]

    Attngan: Fine- grained text to image generation with attentional generative adversarial networks

    Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He. Attngan: Fine- grained text to image generation with attentional generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition , page...

    2018

  46. [54]

    Stack- gan: Text to photo-realistic image synthesis with stacked 10 generative adversarial networks

    Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiao- gang Wang, Xiaolei Huang, and Dimitris N Metaxas. Stack- gan: Text to photo-realistic image synthesis with stacked 10 generative adversarial networks. In Proceedings of the IEEE international conference on computer vision ...

    2017

  47. [55]

    Stack- gan++: Realistic image synthesis with stacked generative ad- versarial networks

    Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiao- gang Wang, Xiaolei Huang, and Dimitris N Metaxas. Stack- gan++: Realistic image synthesis with stacked generative ad- versarial networks. IEEE transactions on pattern analysis and machine intelligence, 41(8):1947–1962, 2018. 2

    1947

  48. [56]

    Two-stage sketch colorization

    Lvmin Zhang, Chengze Li, Tien-Tsin Wong, Yi Ji, and Chunping Liu. Two-stage sketch colorization. ACM Trans- actions on Graphics (TOG), 37(6):1–14, 2018. 3

    2018

  49. [57]

    User-guided line art flat filling with split filling mechanism

    Lvmin Zhang, Chengze Li, Edgar Simo-Serra, Yi Ji, Tien- Tsin Wong, and Chunping Liu. User-guided line art flat filling with split filling mechanism. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9889–9898, 2021. 3

    2021

  50. [58]

    Generating manga from illustrations via mimicking manga creation workflow

    Lvmin Zhang, Xinrui Wang, Qingnan Fan, Yi Ji, and Chun- ping Liu. Generating manga from illustrations via mimicking manga creation workflow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5642–5651, 2021. 3

    2021

  51. [59]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 2

    2023

  52. [60]

    Language-based colorization of scene sketches

    Changqing Zou, Haoran Mo, Chengying Gao, Ruofei Du, and Hongbo Fu. Language-based colorization of scene sketches. ACM Transactions on Graphics (TOG) , 38(6):1– 16, 2019. 3 11 Manga Generation via Layout-controllable Diffusion Supplementary Material

    2019

  53. [61]

    We observe that the contents of the overlapping regions between panels are almost identical (see row 4 in Figure 7)

    Inference Visualization Figure 7 displays the visualization of the inference results, namely, the panels generated by our model, along with the corresponding manga page, which is synthesized by taking the pixel-wise minimum value of each panel. We observe that the contents of ...

  54. [62]

    page", "panel

    Manga109Story Construction Details To provide a clearer demonstration of the construction pro- cess of the Manga109Story dataset, we present a detailed depiction in Figure 8 of the XML file described in Section 3, along with the corresponding MLLM prompt and the gen- erated ex...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.