Learning to Generate Stylized Handwritten Text via a Unified Representation of Style, Content, and Noise
Authors: Honglie Wang
Yan-Ming Zhang
Wangzi Yao
Fei Yin
Cheng-Lin Liu
Venue: ICLR 2026 Poster (2026)
Keywords: handwriting text generation
flow matching
in-contaext generation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose InkSpire, a unified diffusion transformer that models style, content, and noise for high-fidelity, multi-line handwritten text generation in both English and Chinese.

Abstract: Handwritten Text Generation (HTG) seeks to synthesize realistic and personalized handwriting by modeling stylistic and structural traits. While recent diffusion-based approaches have advanced generation fidelity, they typically rely on auxiliary style or content encoders with handcrafted objectives, leading to complex training pipelines and limited interaction across factors. In this work, we present InkSpire, a diffusion transformer based model that unifies style, content, and noise within a shared latent space. By eliminating explicit encoders, InkSpire streamlines optimization while enabling richer feature interaction and stronger in-context generation. To further enhance flexibility, we introduce a multi-line masked infilling strategy that allows training directly on raw text-line images, together with a revised positional encoding that supports arbitrary-length multi-line synthesis and fine-grained character editing. Moreover, InkSpire is trained on a bilingual Chinese–English corpus, enabling a single model to handle both Chinese and English handwriting generation with high fidelity and stylistic diversity, thereby overcoming the need for language-specific systems. Extensive experiments on IAM and ICDAR2013 demonstrate that InkSpire achieves superior structural accuracy and stylistic diversity compared to prior state-of-the-art methods.

Published as a conference paper at ICLR 2026
LEARNING TO GENERATE STYLIZED HANDWRITTEN TEXT VIA A UNIFIED REPRESENTATION OF STYLE, CONTENT, AND NOISE
Honglie Wang 122 Yan-Ming Zhang Wangzi Yao Fei Yin 1.2 Cheng-Lin Liu!:2 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institution of Automation Chinese Academy of Sciences, Beijing 100190, China
[wanghonglie2023, yawwangzi20233jee (ymzhang, fyin, liuci]enipr ia an
ABSTRACT
Handwritten Text Generation (HTG) seeks to synthesize realistic and personalized handwriting by modeling stylistic and structural traits. While recent diffusion- based approaches have advanced generation fidelity, they typically rely on aux- iliary style or content encoders with handerafted objectives, leading complex training pipelines and limited interaction across factors. In this work, we present InkSpire, diffusion transformer based model that unifies style, content, and noise within a shared latent space. By eliminating explicit encoders, InkSpire stream- lines optimization while enabling richer feature interaction and stronger in in-context generation. To further enhance flexibility, we introduce multi-line masked infill- ing strategy that allows training directly on raw text-line images, together with a revised positional encoding that supports arbitrary-length multi-line synthesis and fine- ime-grained character editing. Moreover, InkSpire is trained on a bilingual Chi- Finglsh corpus, enabling single model handle both Chinese and English handwriting generation with high fidelity and stylistic diversity, thereby overcom- ing the need for language-specific systems. Extensive experiments on IAM and ICDAR2013 demonstrate that InkSpire achieves superior structural accuracy and stylistic diversity compared prior mate-of-the-as methods.
INTRODUCTION
Handwritten Text Generation (HTG) aims synthesize realistic and personalized handwriting from arbitrary digital input by modeling traits such as slant, cursiveness, and stroke dynamics. Leveraging the scalability of modern models, HTG enables human-like handwriting generation with broad ap- plications in assistive technology, personalized rendering, font design, historical manuscript restora- tion, and writer identification.
Diffusion models have recently emerged as the dominant paradigm for offline handwritten text syn- thesis, surpassing GAN-based approaches in generation quality. Early attempts at style condition- ing (c.g., WordStylist (Nikolaidou et al., (2023), GC-DDPM (Ding et al., 2023). CTIG-DM (Zhu al. )023)) relied on fixed writer IDs, which constrained stylistic diversity. Subsequent methods introduced dedicated style encoders trained with tailored objectives to capture broader stylistic vari- ations. For instance, One-DM (Dai et al., 2024) employs Laplacian contrastive loss to emphasize fine-grained features, while DiffusionPen (Nikolaidou et al. 2024) integrates triplet and classifica- tion losses to enhance style discrimination. With respect to content fidelity, TGC-Diff (Wang et al.) 2025) proposes high-frequency mask loss preserve structural details. While these approaches improve model performance, they still handle style, content, and noise as separate factors, each constrained by manually crafted losses, which increases the difficulty of optimization.

---

Published as a conference paper at ICLR 2026
(a) One-DM
(b) TGC-Diff
(c) Ours
Lair
Miiz intedd WeEen hhoras woll
== Lain + LiopNcE + Lerc
L Lait frigh-
ugui in pertieu instarce
ogth pertioi irstance
Tlet wabll Hag- Nown
+
Hove
Style Encoder
Style Encoder
Diffusion Model
Fasion Module
Diffusion Model
Diffusion Model
Content Encoder
More ineeet
Magen
Toons Wolk
Kigh R2 pou Natie citte
Optn i the prtiaaa instance
Byt Ri Purlaliin onffuces
depth tt's porticllor instance
Viac Higu
Migh Rg pullail weffact
Figure 1: Structural comparison of handwritten text-line generation methods. Unlike previous ap- proaches, our model integrates style and content modeling without additional encoders, yielding streamlined architecture.
To efficiently integrate diffusion models into HTG systems, it is crucial to examine the role aux- iliary style and content encoders. As illustrated in Figure aa), One-DM fuses style and content features to provide useful guidance for style learning but fails to capture fine-grained structures and spatial information. In contrast, TGC-Diff in Figure constructs content features within the same latent space as noise, facilitating a seamless connection between content and noise represen- tations and enhancing structural fidelity and spatial consistency. If content and noise can be jointly represented in a shared latent space, is it possible to design a single unified diffusion model that simultaneously processes style, content, and noise? Such a framework would not only obviate the need for redundant encoders with complex handcrafted losses, but also improve performance by enabling efficient interaction within a common latent representation.
Building on this insight, we introduce InkSpire, diffusion transformer model for stylized handwrit- ing generation, which "inspired" by in-context latent "ink" tokens. As illustrated in Figure Tcc,, InkSpire leverages shared latent space for style, content, and noise, replacing separate encoders with a streamlined architecture that facilitates effective feature interaction. Moreover, recent ad- vances in large coxt-to-mmaee diffusion models have demonstrated strong in-context generation pabilities. To apply this unified modeling ability to our HTG task, we design a multi-line masked infilling strategy for training and remove the text encoder enable purely visual conditioning. In addition, we revise the positional encoding mechanism to support multi-line generation of arbitrary length and train the model on a mixed Chinese-English dataset, thereby enabling bilingual stylized handwriting synthesis. By integrating these innovations, InkSpire achieves nigh-fidelity and stylis- tically diverse handwriting generation and editing, while streamlining the overall training process.
In summary, the main contributions of this paper are as follows:
We propose InkSpire, a novel handwriting generation framework that unifies the model- ing of style, content, and noise without relying explicit style or content encoders. By leveraging in-context generation with diffusion transformer models, InkSpire simplifies the training pipeline while preserving high fidelity and stylistic diversity.
We introduce multi-line masked infilling strategy that enables the model to be trained directly on raw multi-line text images, without requiring complex data preprocessing To- gether with revised positional encoding scheme, InkSpire supports the generation of multi-line handwriting of arbitrary length as well as fine-grained, character-level editing.
We enable bilingual handwriting generation within a single model by training InkSpire on mixed Chinese- Chinese-English dataset. This design allows the synthesis of high-quality hand- written text both English and Chinese scripts, thereby overcoming the constraints of language-specific handwriting systems.
2

---

Published as a conference paper at ICLR 2026
We conduct extensive experiments the ICDAR2013 and IAM datasets, demonstrating that InkSpire produces handwriting with superior structural accuracy and stylistic consis- tency, outperforming other methods in both qualitative and quantitative evaluations.
RELATED WORK
Handwritten data is commonly categorized into two modalities: online trajectory sequences and offiine static images. Online handwriting, which captures the dynamic pen trajectory during the writing process, has been widely studied with various generative models, including RNNN-based
approaches (Kotani al. 2020 ZZhao et al. [020 Zhang et al.) 2017), Transformer- feasomee--baedd architec- tures (Dai et al. 2023), and diffusion-based methods (Luhman & Luhman 2020)) Ren et al. 2023))
In contrast, offline handwriting represents static visual appearances, conveying natural characteris-
ties such as stroke thickness, curvature, and ink density.
2.1 OFFLINE HANDWRITING GENERATION
Early approaches to offline handwriting synthesis predominantly relied on Generative Adversarial
Networks (Alonso al. 2019 Xie al.) 2021 Gan & Wang 2021: Kong al.
2022; Liu al.,
2022), where adversarial training was used to generate visually plausible text. Later, transformer-
based models such as HWT (Bhunia et al. 2021) and VATr (Pippi al. 2023a) introduced hybrid
CNN-transformer designs that enhanced style representation learning and improved generalization.
More recently, diffusion models have emerged the dominant paradigm, synthesizing handwriting through iterative denoising with fine-grained control over style and content. State-of-the-rtt meth-
ods (Wang et al. 2025 Pippi al. 2025 Dai al. 2024 Nikolaidou al. 2023 Ding al. 2023
Zhu et al. (2033) demonstrate strong style adaptation from only a few references, enabling diverse
and personalized handwriting generation with minimal supervision.
2.2 IN-CONTEXT GENERATION
Recent advances in inffusion-based generative modeling increasingly adopt in-context generation for controllable and personalized image synthesis. Early works such as InstructPix2Pix (Brooks et al. 2023) and its successors (Boesel & Rombach 2024) fine-tuned diffusion models with synthetic in-
struction-response pairs for diverse editing tasks, marking initial attempts to align generation with user intent. Subsequently. instruction-driven editors including Emu Edit (Sheynin et al. 2024). Om- niGen (Xiao et al., 2025). HiDream-I1 (Cai et al. (025). ICEdit (Zhang et al.) 2025), ). JRM (Yang al. 2026) and Reward (Long et al. 2026) leveraged refined datasets and task- specific archi- tectural enhancements to improve alignment and fidelity. Huang et al. (2024) extend this paradigm by introducing task-specific LoRA branches within diffusion transformers. Although unified models for both editing and generation have been explored in printed document understanding (Tang et al.) 2023 Chen et al. 2202),, our approach is the first to bring in-context modeling strength of unified Buttingaand-enneran framework into the handwriting domain.
METHODS
3.1 PRELIMINARY
3.1.1
DATA NOTION
The multi-line handwritten dataset is represented collection of transcribed image-text pairs ((X, C)}, where X denotes a stylized text-line image and C denotes the corresponding textual con- tent. Let C = (c',c,... ... c²) denote text sequence of length n, where each element corresponds to either a character in logographic writing systems (e.g., Chinese) or a word in phonemic writing systems (e.g., English).
3

---

Published as a conference paper at ICLR 2026
Absolute Representation: 1No:3o-P1:P1l
Relative Representation:
[Ax,Ay,w, hl
: Xg xr. Ay: yo Yprer_baseline
h
(Xo:Yo)
4y
X1 h Y1 Yo
(x10')
This figure illustrates the transformation from absolute to relative positional representa- tions layout information. All positions are normalized by the original paragraph image width.
Figure 2:
3.1.2 TASK ANALYSIS
The objective of handwritten text-line generation is to synthesize text-line images that accurately convey the given textual content while faithfully imitating the writing style of target author, specified by style reference samples X.. Formally, the task can be expressed as modeling the conditional distribution: p(X C.
However, directly modeling this distribution is challenging due to the lack of explicit character positions. Following prior work Wang et al. (2025); Yao et al. (2025), we introduce a content image X., where each character in the text-line image X. is replaced with glyph rendered in standard font according to the stylized layout. This allows us to model the joint distribution of the text-line image and its content image: p(X, X C, X,). In line with Ren et al. (2023), the joint distribution can be factorized as:
PXX X C,X,) p(X, C,X.) p(X C, =(X. C,X.) p(X X,, X.).
Accordingly, the overall generation process decomposes into components: (i) layout generation model p(X. C, X,), and (ii) image generation model p(X X,,X,) X.).
3.2 LAYOUT GENERATION
Given an input character sequence C cc.
we define corresponding layout sequence B (b', b² .... ,."),, where each b' denotes the bounding box of character c¹. Specifically, b' is parameterized by four normalized values that capture the character's [width w, height h, horizontal offset from the previ ous character As, vertical offset from the baseline of the preceding text-line Ay], as illustrated in Figure We explore three strategies for modeling layout representations:
Autoregressive Modeling
pb (b). "..))..,('"" ",_,,,,b',..''),,,'
Masked Layout Modeling
pit M [0,, e.), & M), k EE,
(3)
Masked Modeling with Conditional Flow Matching
p i E M [( ee), & M), (0L, c M]. X.),
(4)
where M denotes the index set of masked positions and denotes the timestep.
Specifically, the autoregressive model employs several transformer decoder layers, while both the masked layout model and its CFM variant utilize several transformer encoder layers. During train- ing, the autoregressive and masked layout models optimized by minimizing the average LI distance between the predicted and ground -trtth bounding box parameters (width, height, horizon- tal offset, and vertical offset) for the relevant characters. For the CFM model, the training loss

---

Published as a conference paper at ICLR 2026
Visual Input
Training
c
Concalenatc Space
Concatrnate in Channel
Compute Conditional FlohhManchingg Less
porr
Pay lnns Trarraiin
bely
leportiig under tike
Pustuu Pectarterree srone Soid Radbed
fo dief
Nx
Insetfinc
Out Projection
VAE Eacoder
Altentin
Random Mask Gescrotion
Mad
Style Tukens
Content Tokem
Naise Takeea
Figure 3: Overview of Ipire Our model achieves unified modeling by simply concatenating X with X, and training directly on multi-line images with mask noise prediction. No additional encoders or extra designed loss needed.
is defined the average L1 distance between the predicted and ground-truth velocity of these four layout parameters at each timestep. After training, we render the content image X. based on the pre- dicted layout sequence B and a chosen standard font, thereby successfully modeling the conditional distribution p(X. C, X.).
3.3
IMAGE GENERATION
3.3.1
MULTI-LIINE MASKED INFILLING STRATEGY
Leveraging the layout generation model, the original image-text pairs ((X, C)) are transformed into image-content pairs X.)). Previous approaches typically construct paired target and style images [(X..r, X x.)). by cropping two distinct text-lines from single author's multi-line image X. Images are then resized to fixed height for training convenience. Under this setup, diffusion models are trained to learn p(Xtor.t-1 Xtar,t, X., X.), where X. X, are typically processed by separate encoders, and thus do not share the same feature space as Xtor.t- Such preprocessing suboptimal as it (i) overly shrinks characters in highly slanted lines, (ii) introduces inconsistent distortions across lines with different slants, and (iii) discards inter-line style cues, thereby limiting generative capability and hindering resolution generalization.
To enable direct training on the original image pairs (X,Xx)) we first randomly crop fixed-size patches of size x P from the original handwritten page images, where P hyperparameter controlling the patch size. On these patches, random binary mask image M of the same size applied. This mask partitions the image into two complementary components: the masked re- gion Xmis = M @ X and the observed context Xetz (1 M) & X. With this construction, there is no longer need to explicitly crop paired samples {(Xerr X,)) from X. In fact, X,is implicitly corresponds to Xtar, while Xetz serves the role of X,, Consequently, the training objec- tive is reformulated to p( ((X,iss,t) X Xatz X,), thereby eliminating the need for additional preprocessing.
Under this probabilistic modeling framework, style, content, and noise can be jointly represented within unified latent space. As illustrated in Figure 3 we begin by constructing the image I through the spatial concatenation of X and X.- random binary mask image I, is then applied to generate the masked input I, After encoding with the VAE encoder and applying patchification, we obtain the masked image tokens F, together with the mask tokens F F..- Finally, the noisy image tokens F, v concatenated with F; and F,, along the channel dimension. The overall procedure
5

---

Published as a conference paper at ICLR 2026
(0.00
(U.T)
(0.3)
(un
(B.1)
(9:)
(8,0)
Naive
Pastlion
0,.0
eLo
Tester
Bir Hot
Fnceding
12.0
0.0
(9.23
(8,3)
(0,9)
10.30
(B,3)
Aligned Pesition
11.0g
0.91
1..9
Allgaed
Scr Roo
Encediag
(2.00
12.0)
(3:2)
12.00
12:00
330
1-2
Figure 4: Comparison of different positional encoding strategies. We introduce Aligned Position Encoding (APE) to better guide the spatial layout of generated handwritten text, and further propose a variant, R-APE, tailored for long text lines.
can be formally expressed follows:
X@X.: I = I (1 - Im):
(5)
(6)
F; Patchify (VA(()))
Fm Patchify(Im);
(8)
F Finput F., F
Here, ( denotes the concatenation operation along the spatial dimension, & represents the element- wise (Hadamard) product, @ denotes the concatenation operation along the channel dimension, F; encapsulates the information from both Xetz and X... Since the same VAE encoder utilized, style, content, and noise are jointly represented within a unified feature space.
3.3.2 MASKED CONDITIONAL FLOW-MATCHING OBJECTIVE
We employ a flow-matching training objective to optimize the model. Specifically, given a clean latent code Xo, Gaussian noise sample 2₁ (O, 7), and time-dependent noise scale o generate the noisy latent input X via a convex combination:
(1 d xx + 0.21-
(10)
The model learns to estimate the velocity vector pointing from Xy to 21 and the training loss is formulated as:
Ling-CrM-m Ex,,,. ||m 3 (Vo c) - (z x
(1I)
where va denotes the model's velocity prediction, m indicates the masked regions of the latent to- kens, and e comprises X,tz and X.. To ensure concise optimization process, the training excludes additional objectives such as perceptual loss or CTC loss.
3.3.3 ROTATED ALIGNED POSITION ENCODING
Directly fine-tuning pretrained diffusion transformer models fails fully exploit their generative capacity for HTG tasks. We therefore redesign positional encodings to better support multi-line handwritten text with arbitrary line lengths. As shown in Figure naïve 2D RoPE arranges tokens row by row in the concatenated image I, where standard-font and handwritten tokens interleaved. However, since the length of text-line images varies significantly, the model struggles to distinguish whether given token should serve as style condition content condition.
To mitigate this issue, we propose Aligned Positional Encoding (APE). In APE, the token arrange- ment of I remains unchanged, while the positional encodings assigned to X, directly shared with

---

Published as a conference paper at ICLR 2026
Molt--ine Handeritten Text Synthesi
Character Word Level Handeviitenn Text Editing
tgk last
Target Teer:
Origiaal Tost
Filled livt
los dave desona fooe fil la nus.
You for thaal mas munerr
Yos sil so and ane lao
code ga see--dschc
City
fir
User and
Concosel fo lirst fevondd bud' maad ic
bodar graa
gi aersennt
Clypl Castealiiggg
behesttool
Oigt Crncol trage
Tieary
Iue Sadre
Hiaht
Sett hilatedd thes (ihy werisos
froh Sel hittaad hitt Syy iot
ifft
Mal
Droddal nos ficsedd fat's
Groad Serti
Figure 5: Inference applications of InkSpire, encompassing Multi-line Handwritten Text Synthesis and Character/Word Handwritten Text Editing. All applications are guided by the mask image M. the stylized image X, and the content image X
their counterparts in X. For cases where text-line images are wider than tall, we further introduce Rotated APE (R-APE): and X. are rotated 90º clockwise before concatenation, so that target tokens and their content-condition counterparts remain spatially close in the positional space.
3.4 APPLICATIONS OF INFERENCE
Owing to the powerful contextual generation ability of diffusion transformer models and the precise spatial control provided by X,, our framework enables versatile inference through simple adjust- ments the mask M or modifications to X., The overall procedure is illustrated in Figure 5
Multi-line Handwritten Text Synthesis: Given single style reference image from writer, previous approaches are typically constrained to generating only one or a few text lines. In contrast, our model is capable of synthesizing tancously, by placing the style reference in the first line and masking the remaining parts, conditioned the multi-line content image X
arbitrary number of text lines simul-
Character/ Word Level Handwritten Text Editing: By providing mask that specifies the regions to be edited, together with an edit-content image rendered in standard font, InkSpire can accurately modify multiple words within a handwritten text image while preserving the unmasked regions.
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
4.1.1 DATASETS
To validate the effectiveness of InkSpire in synthesizing handwritten text-line images, experiments are conducted on the IAM dataset (Marti & Bunke 2002) for English and on CASIA-HWDB2.00
2.2 (Liu et al. 2011) and ICDAR2013 datasets (Yin et al.) 2013) for Chinese. IAM comprises 13,353 English text-lime images, with 496 writers' samples for training and the remaining 161 for testing. For Chinese, CASIA-HWDB2.0-- training, while ICDAR2013 includes 3432 text-line images from 60 writers for testing. All datasets provide layout annotations at the word level for English and the character level for Chinese.
contains 52.230 text-line images from 1,019 writers for

---

Published as a conference paper at ICLR 2026
Toxt
After whassensed period repsiion
Text:
x 0:
Styte
went ou to Clsterile the Lomeery of Mue Maeton
Style Ref
$
A Apter what Secueed undu perio of reeeh home
Une UM
0s-DM
After that Weeentlee On das parind of oppliition
TGC-DDI
2
DiTPan
ABes what susrouad unduae parioch xepe lition
taksSpire
Afee what sucuee undbes peried of repebition you as had bescuee das andthe pesrial of reahtiinn Belowed, seeing thesss gidee
GT
Text:
Al Hia aebard, ahhich is dugamingg sit followed seeing how the sam chud followedd i oeeiny the Oun gildery
Style Ref
Cuc-DM
</h Je ca
UKe
TGC
a/ f 12
t
followed seeing how the swn gr kded Pollowed, seeiing how the scam golded
SpkSpiic
d9l vil
AaAA
- Tuk mbx
foloadd, ceeking limes Vhe gibled
CIT
23 14. rORe
22
baiiningts tnitheraneroeddd fattib, bapry Ofper a lorg Meul a bus appeare teginning to fed the storectyped flatiish happy
Text:
Styte
Style Reef
A
VrT
One DM
#
Sgiining to dnd the strrppo the Maadhan cm
20
leginiing to Rind sterrdlppes Plort ith happy Leginning de find the skveolped Rattish happs legioing Ne bos the skeacheed Hatleh hoppyy
takSpire
k
FLUS9
GT
Figure 6: Qualitative comparison between VaTr, One-DM, DiffPen, TGC-Diff, and our proposed InkSpire on image generation across the considered datasets. Our multilingual model demonstrates strong style imitation capabilities under both Chinese and English conditions.
4.1.2 EVALUATION METRICS
The evaluation metries encompass two key aspects: style and content. To assess style diversity and consistency, employ the Fréchet Inception Distance (FID) (Heusel et al.) (017)) Kernel Inception Distance (KID) (Binkowski et al. 20188) and the task-specific Handwriting Distance (HWD) (Pippi et al. 2023b). For content accuracy, we adopt the Correct Rate (CR) and Accuracy Rate (AR) (Yin
et al. 2013) for Chinese text, while for English, we use the Absolute Character Error Rate Difference
(ACER) (Nikolaidou al. 2024).
4.1.3 COMPARED METHODS
For English handwritten text line generation, we compare our method against representative models, including HWT (Bhunia et al.) (02I)), VATr (Vanherle et al. (2024), One-DM (Dai et al. 2024), and DiffPen Niikolaidou et al.] 2024). For Chinese handwritten text line generation, the comparison is conducted with One-DM and TGC-Diff (Wang et al. 2025).
4.1.4 IMPLEMENTATION DETAILS
For layout generation, we employ 10 transformer layers across all three modeling approaches. Dur- ing inference, the three layout modeling strategies differ in how layouts are produced: in the autore- gressive model, the reference layout is encoded as prefix tokens and the target layout is generated sequentially in token-by-toker manner; in the masked layout model, the input contains observed reference layout and a masked target layout, and all masked tokens are predicted in a single forward pass; in the Conditional Flow Matching (CFM) model, the same masked positions in the masked
8

---

Published as a conference paper at ICLR 2026
IAM Layout
ICDAR2013 Layout
Method
Arl
Ayl
Awl
Ahl
Ax
Ay4
Awd
Ahl
Autoregressive Modeling
5.60
17.04
11.42
13.67
7.28
19.25
14.29
12.88
Masked Layout Modeling
5.18
14.51
6.52
8.04
6.42
16.71
13.67
10.56
Masked Modeling with CFM
4.74
14.39
4.74
4.94
5.13
14.85
12.43
8.74
Table I:
Layout prediction results on IAM and ICDAR2013 datasets. Values in the table correspond L1 losses multiplied by 103; lower values indicate better performance (L).
FIDI
KIDI
HWD.
ACER
HWT
44.72
43.49
2.97
0.33
FID1
KID
HWD!
CR
ART
VATr
34.00
29.68
2.38
0.03
One-DM
34.36
28.37
0.80
73.19
72.33
One-DM
43.89
44.48
2.83
0.13
TGC-Diff
23.43
13.85
0.63
89.99
89.13
DiffPen 12.89
9.73
2.13
0.03
InkSpire
10.98
11.45
0.41
92.92
91.56
InkSpire 7.92
4.83
0.62
0.01
Table 3: Chinese text line generation results on ICDAR2013. The KID is multiplied by 10a. Lower is better (4), higher is better (f).
Table English text line generation results on
IAM. The KID is multiplied by 10 Lower is better (J).
layout model are used, but the masked tokens generated through a continuous denoising process solved with a 10-step ODE solver, enabling smooth and flexible layout prediction.
For image generation, our approach builds upon the pre-trained (2024), latent rectified flow transformer tailored for image editing. To adapt to multilingual gener- ation, training samples across languages are integrated within unified training pipeline. The image patch size is set to P = 1024, and the batch size is 4. The model is optimized using the Prodigy optimizer with an initial learning rate of and weight decay of 0.01. Fine-tuning is performed via Low-Rank Adaptation (LoRA) with a rank of 32 and LoRA scaling factor of 32, trained for 20,000 iterations on four A A100 GPUs (40 GB). total, the LoRA modules introduce approximately 115.9M trainable parameters, A comprehensive description of all fine-tuner LoRA parameters can be found in Appendix Table [ The fine-tuned model achieves high-quality synthesis with about 20 ODE denoising steps. For more implementation details of layout generation and image generation, please refer to Appendix A.3
L-U.I--Filldde model Labs
4.2 ADDITIONAL. ANALYSIS OF STYLIZED LAYOUT GENERATION
We compare three layout modeling tional Flow Matching (CFM)-using average losses of four layout-specifie features. For all methods, the tokens corresponding to the first line of each paragraph are provided as reference tokens, and the models are tasked with predicting the layout parameters of all remaining tokens. Under this set- ting. the masked layout model and CFM apply masking to all non-reference tokens. The evaluation metric computed by averaging the L1 deviations over the full set of tokens that require prediction. As shown in Table [] CFM consistently outperforms the others, demonstrating its effectiveness in capturing complex spatial dependencies while preserving layout coherence.
Masked, and Masked with Condi-
4.3 STYLIZED HANDWRITTEN TEXT-LINE GENERATION
Although InkSpire trained on multi-line images to capture in-context stylistic features, we follow the one-shot evaluation protocol, where only single reference text-line is provided. The goal is to generate new lines with the same style but different content. During inference, we consistently select references by using the second line to generate the first, and then the first line for the remaining ones, which mitigates intra-writer style variations across paragraphs.
Quantitative results, presented in Table 2 and Table demonstrate that InkSpire consistently out- performs sfate-of the-art methods on both English and Chinese datasets. As illustrated Figure [ model not only produces handwritten styles that more visually aligned with the reference,

---

Published as a conference paper at ICLR 2026
FID4
KID,
HWD
ACER|
FID.
KIDI
HWD.
CR
AR*
baseline
15.12
19.27
0.97
0.11
baseline
17.57
18.61
0.53
89.72
88.91
+APE
9.31
7.21
0.58
0.05
+APE
11.75
12.35
0.42
91.87
90.63
+R-APE
7.92
4.83
0.62
0.01
+R-APE
10.98
11.45
0.41
92.92
91.56
Table 4: Ablation study of positional encod- ing on the IAM Lines dataset. The KID is multiplied by 10 Lower is better (4).
Table 5: Ablation study of positional encod-
ing on the ICDAR2013 Lines dataset. The KID multiplied by 10 Lower is better (4),
higher is better (t).
FID4
KIDI
HWD4
ACER]
FID.
KIDI
HIWI
CR
ART
F-TopMask R-Mask
8.73
6.13
0.78
0.07
F- F-Top.ask
11.57
13.41
0.48
92.48
91.34
7.92
4.83
0.62
0.01
R-Mask
10.98
11.45
0.41
92.92
91.56
Table 6: Ablation study of masking strate- gieson the IAM Lines dataset. The KID is multiplied by 103. Lower is better (L).
Table 7: Ablation study of masking strategies the ICDAR2013 Lines dataset. The KID is multiplied by 10 Lower is better ()), higher is better (t).
but also exhibits significantly improved character structure accuracy. These findings provide strong evidence of the superiority of approach and are well-aligned with the quantitative metries.
4.4
ABLATION STUDY
4.4.1 ABLATION STUDY ON POSITION ENCODING
observe that Aligned Positional Encoding (APE) markedly enhances style diversity and content accuracy by helping the model better distinguish style from content to- kens. While naïve positional encoding can handle single-line generation, often copies input images in multi-line settings and is sensitive to resolution. Moreover, the rotated variant (R-APE) yields fur- ther gains both English and Chinese datasets, better localizes tokens in long text lines under the one-shot setting.
From Table and Table
4.4.2 ABLATION STUDY ON MULTI-LINE MASKED INFILLING STRATEGY
In addition to the Random-Size Multi-Region Masking (R-Mask) strategy descrihed in Section 3.3. introduce Fixed Top-Region Unmasked Masking (F-TopMask) scheme Since masking is performed on 1024x 1024 image patch, the fixed-mask design keeps only the top 128x1024 region visible while masking all remaining lower areas. This setup resembles the inference scenario where only the first text line is provided as style reference and the model must generate all subsequent lines. The quantitative results Table and Table / demonstrate that our R-Mask strategy yields moderate improvements over the fixed-region alternative.
ablation variant.
5 CONCLUSION
We introduced InkSpire, a diffusion transformer that unifies style, content, and noise for handwritten text generation. By removing explicit encoders and adopting multi-line masked infilling with revised positional encoding, it enables efficient training, arbitrary-length synthesis, and fine-grained editing. Trained on English and Chinese corpora, InkSpire outperforms prior methods in fidelity and stylistic diversity. Future work will extend to more languages and datasets to enhance generalization.
10

---

Published as a conference paper at ICLR 2026
Acknowledgment: This work is supported by the Natural Science Foundation of China (NSFC) Grant No. 62276258 and No. U23B2029.
REFERENCES
Eloi Alonso, Bastien Moysset, and Ronaldo Messina. Adversarial generation of handwritten text images conditioned on sequences. In International Conference on Document Analysis and Recog- nition, pp. 386.486, 2019.
Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, and Mubarak Shah. Handwriting transformers. In Proceedings of the IEEF/CVF interna- tional conference on computer vision, pp. 1086-1094, 2021.
Mikolaj Binkowski, Danica Sutherland, Michael Arbel, and Arthur Gretton. Demystifying mmn gans. arXiv preprint arXiv: .1.001444 401, 2018.
Frederic Boesel and Robin Rombach. Improving image editing models with generative data refine- ment. In The Second Tiny Papers Truck at ICLR 2024, 2024.
Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18392-18402, 2023.
Qi Cai, Jingwen Chen, Yang Chen, Yehao Li, Fuchen Long, Yingwei Pan, Zhaofan Qiu, Yiheng Zhang, Fengbin Gao, Peihan Xu, et al. Hidream-il: A high-efficient image generative foundation model with sparse diffusion transformer. arXiv preprint arXiv:2505.222 22705, 2025.
Haoxing Chen, Zhuoer Xu, Zhangxuan Gu, Yaohui Li, Changhua Meng, Huijia Zhu, Weiqiang Wang, et al. Diffute: Universal text editing diffusion model. Advances in Neural Information Processing Systems, 02663062-630744 2023.
Gang Dai, Yifan Zhang, Qingfeng Wang, Qing Du, Zhuliang Yu, Zhuoman Liu, and Shuangping Huang. Disentangling writer and character styles for handwriting generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5977-5986, 2023.
Gang Dai, Yifan Zhang. Quhui Ke, Qiangya Guo, and Shuangping Huang. One-dm: One-shot diffusion mimicker for handwritten text generation. European Conference on Computer Vision, 2024.
Haisong Ding, Bozhi Luan, Dongnan Gui, Kai Chen, and Qiang Huo. Improving handwritten ocr with training samples generated by glyph conditional denoising diffusion probabilistic model. In International Conference on Document Analysis and Recognition, pp. 20-37, 2023.
Gan and Weiqiang Wang. HiGAN: Handwriting imitation conditioned on ribitrary-lenghe texts and disentangled styles. In Proceedings of the AAAI Conference on Artificial Intelligence, vol- ume 35, pp. 7484-7492, 2021.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Lianghua Huang, Wei Wang. Zhi-Fan Wu. Yupeng Shi, Huanzhang Dou, Chen Liang, Yutong Feng, Yu Liu, and Jingren Zhou. In-context lora for diffusion transformers. arXiv preprint wrXiv:2410.2377. 2024.
Yuxin Kong, Canjie Luo, Weihong Ma, Qiyuan Zhu, Shenggao Zhu, Nicholas Yuan, and Lianwen Jin. Look closer supervise better: one-shot font generation via component-based discriminator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp.
13482-13491, 2022.
Atsunobu Kotani, Stefanie Tellex, and James Tompkin. Generating handwriting via decoupled style descriptors. In European Conference Computer Vision, pp. 764-780. Springer, 2020.

---

Published as a conference paper at ICLR 2026
Black Forest Labs. Flux: Official inference repository for flux.1 models, 2024. URL https: //github. how.bbbbooocbaaccoeooeeeeee Latss flux Accessed: 2024-11-12.
Cheng-Lin Liu, Fei Yin, Da-Han Wang, and Qiu-Feng Wang. Casia online and offline chinese handwriting databases. In International Conference on Document Analysis and Recognition, pp. 37-41, 2011.
Wei Liu, Fangyue Liu, Fei Ding, Qian He, and Zili Yi. Xmp-font: self-supervised cross-modality pre-training for few-shot font generation. In Proceedings of the IEEE/CVF Conference Com- puter Vision and Pattern Recognition, pp. 7905-7914, 2022.
Yancheng Long, Yankai Yang, Hongyang Wei, Wei Chen, Tianke Zhang, Changyi Liu, Kaiyu Jiang, Jiankang Chen, Kaiyu Tang, Bin Wen, et al. Spatialreward: Bridging the perception gap in online rl for image editing via explicit spatial reasoning. arXiv preprint arXiv:2602.07458 2026.
Troy Luhman and Eric Luhman. Diffusion models for handwriting generation. arXiv preprint
arXiv:2011.0670% 2020.
U-V Marti and Horst Bunke. The IAM-database: an English sentence database for offline handwrit- ing recognition. pp. 39-46, 2002.
Konstantina Nikolaidou, George Retsinas, Vincent Christlein, Mathias Seuret, Giorgos Sfikas, Elisa Barney Smith, Hamam Mokayed, and Marcus Liwicki. Wordstylist: Styled verbatim hand- written text generation with latent diffusion models. In International Conference on Document Analysis and Recognition, pp. 384-401, 2023.
Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas, and Marcus Liwicki. Diffusionpen: To- wards controlling the style of handwritten text generation. In European Conference on Computer Vision, pp. 417-434. Springer, 2024.
Vittorio Pippi. Silvia Cascianelli, and Rita Cucchiara. Handwritten text generation from visual archetypes. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition, pp. 22458 22467,, 2023a.
Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, and Rita Cucchiara. Hwd: A novel evaluation score for styled handwritten text generation. arXiv preprint arXiv:2310.203166 2023b.
Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, Alessio Tonioni, and Rita Cucchiara. Zero-shot styled text image generation, but make it autoregressive. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 7910-7919, 2025.
Min-Si Ren, Yan-Ming Zhang, Qiu-Feng Wang. Fei Yin, and Cheng-Lin Liu. Diff-writer: A dif- fusion model-based stylized online handwritten chinese character generator. In International Conference Neural Information Processing, pp. 86-100. Springer, 2023.
Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, and Yaniv Taigman. Emu edit: Precise image editing via recognition and generation tasks. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8871- 8879, 2024.
Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, and Mohit Bansal. Unifying vision, text, and layout for universal document processing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp- 19254-19264 2023.
Bram Vanherle, Vittorio Pippi, Silvia Cascianelli, Nick Michiels, Frank Van Reeth, and Rita Cuc- chiara. Vatr++: Choose your words wisely for handwritten text generation. IEEE Transactions on Patlern Analysis and Machine Intelligence, 2024.
Honglie Wang, Minsi Ren, Yan-Ming Zhang, Fei Yin, and Cheng-Lin Liu. camplate-guidee cas- caded diffusion for stylized handwritten chinese text-line generation. Document Analysis and Recognition ICDAR 2025, pp. 149-166. Cham, 2025. Springer Nature Switzerland.
12

---

Published as a conference paper at ICLR 2026
Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xingrun Xing, Ruiran Yan, Chaofan Li, Shuting Wang. Tiejun Huang, and Zheng Liu. Omnigen: Unified image generation. Proceed- ings of the Computer Vision and Pattern Recognition Conference, pp. 11994-133044 2025.
Yangchen Xie, Xinyuan Chen, Li Sun, and Yue Lu. DG-Font: Deformable generative networks for unsupervised font generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5130-5140, 2021.
Yankai Yang, Yancheng Long, Hongyang Wei, Wei Chen, Tianke Zhang, Kaiyu Jiang, Haonan Fan, Changyi Liu, Jiankang Chen, Kaiyu Tang, et al. Joint reward modeling: Internalizing chain-of- thought for efficient visual reward models. arXiv preprint wxxv:26022.0533, 2026.
Gang Yao, Liangrui Peng, Zhiyu Li, Tianqi zhao, Kemeng Zhao, Ning Ding, and Yao Tao. Multi- level style control for chinese handwriting generation: G. yao et al. International Journal Document Analysis and Recognition (IJDAR), pp. 1-12, 2025.
Fei Yin, Qiu-Feng Wang, Xu-Yao Zhang, and Cheng-Lin Liu. ICDAR 2013 chinese handwriting recognition competition. In International Conference on Document Analysis and Recognition, pp. 1444414770 2013.
Yao Zhang. Fei Yin, Yan-Ming Zhang, Cheng-Lin Lin Liu, and Yoshua Bengio. Drawing and recog- nizing chinese characters with recurrent neural network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):849 842-862, 2017.
Zechuan Zhang, Ji Xie, Yu Lu, Zongxin Yang, and Yi Yang. In-context edit: Enabling instructional image editing with in-context generation in large scale diffusion transformer. arXiv preprint DXXiv:550422690, 2025.
Bocheng Zhao, Jianhua Tao, Minghao Yang, Zhengkun Tian, Cunhang Fan, and Ye Bai. Deep imitator: Handwriting calligraphy imitation via deep attention networks. Pattern Recognition, 104:007080, 2020.
Yuanzhi Zhu, Zhaohai Li, Tianwei Wang. Mengchao He, and Cong Yao. Conditional text image generation with diffusion models. In Proceedings of the IEEF/CVF Conference Computer Vision and Pattern Recognition, pp. 14235-14245, 2023.
13

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 INFORMATION ABOUT USE OF AI ASSISTANTS
The authors used Al-assisted tools solely for language polishing, including grammar, spelling, and expression refinement. All conceptual contributions, technical innovations, experimental design, and analyses presented in this work were independently performed by the authors without reliance on Al for content generation or scientific reasoning.
A.2
USER STUDIES
A.2.1 USER PREFERENCE, STUDY
We conduct human evaluation to assess the perceptual quality of synthesized Chinese handwritten text-line images, focusing on style fidelity and content correctness. Participants, all of whom hold postgraduate-level education, compare the outputs of our method with two state-of-the-art baselines, One-DM and TGC-Diff. In each trial, a writer is randomly sampled from the ICDAR2013 dataset, and one of their handwritten text-line images is provided as a style reference, together with an identical content prompt for all methods. Participants are shown the reference text-line alongside multiple candidate images generated by the three models and are asked select the sample with the highest overall generation quality. Figure illustrates an example of the questionnaire instructions and its corresponding question items. The evaluation consists of rounds, yielding 900 valid responses from 30 volunteers. As shown in the Figure a)), our method receives the highest number of user selections, indicating its superior perceptual quality in handwritten text generation.
Instructions
This survey contains 30 evaluation questions. For each question. you will be given target text and style reference image. Options A, B, and show images generated by three different models (in randomized order).
Your task is to select the image with the highest generation quality.
"Generation quality" primarily includes two uspects:
1. Style: How similar the generated handwriting style is to the reference
(e.g., stroke thickness, curvature, connections, and other local stylistic details).
2. Content: The correctness and clarity of the generated characters.
Note:
If the reference wriling style is exaggerated, eusily distorted, blurry, pleuse prioritize style consistency content accuracy. Pleuse ignore occasional hallucinated content that exceeds the given larget text.
In short, choose image that closely resembles being written by the same person style reference image.Fur For best results, please complete the survey computer click zoom on each image before making selection.
A 14
X8
A.
B.
C.
#2
Figure 7: User Preference Study Instructions.
14

---

Published as a conference paper at ICLR 2026
preference Analysis
User plausibility Analysis
One DM
17.4%
Ink: Spire
53.0%
25.2%
37.3%
Inksspire
TGC-aif
(a) User preference
(b) User plausibility
Figure 8: Overall caption describing both images.
A.2.2 USER PLAUSIBILITY STUDY
We conduct user plausibility study to evaluate whether text-line images generated by InkSpire are perceptually indistinguishable from real handwriting. Participants are first presented with 30 authentic handwritten text-line samples, which serve style reference images. In each question, they are then shown two candidate images: one genuine sample written by the same author and one generated by model. Their task is to determine which image appears more likely be written by the same writer as the reference. Figure@iillusrrate illustrates an example the questionnaire instructions and its corresponding question items. total of 23 participants provide 690 valid responses. As shown in Figure B[b) the selection accuracy converges to approximately, indicating performance at chance level. This suggests that the text-line images produced by InkSpire are nearly indistinguishable from real handwriting.
Instructions
This survey contains evaluation questions. For each question, you will be given target text and style reference image. In this study, only two candidate images are presented. Please select the image that most closely resembles the style of the reference image (i.e., appears to be written by the same author). The order of the two options has been randomized.
Please ignore occasional hallucinated content that exceeds the specified text. For best results, complete the survey on computer and click zoom in on each image before making your selection.
P # 12 P4
A
A
1
A.
9
18
B.
9
2888
Figure 9: User Plausibility Study Instructions.
15

---

Published as a conference paper at ICLR 2026
Denoising Loup: iapet time step!
Transformer Decoder
Transformer Encoder
Diffusion Transformer
Boisy tokens
predicted
reference tokens
masked tokens
Character Embeddings
Character Embeddings
Chrratter Embeddingg
Position Emboddings
Position Emheddings
Position Embeddings
(c) Masked Modeling with Conditional Flow Matching
(n) Autoregressive Modeling
(b) Masked Layout Modeling
Figure 10: Layout Inference Architectures.
A.3 MORE IMPLEMENTATION DETAILS
A.3.1 LAYOUT GENERATION DETAILS
We provide detailed description of the three layout generation strategies discussed in the main text:
Autoregressive Modeling: This approach predicts the layout of each target token using an autoregressive Transformer architecture. The model input consists of the embedding of the current character (or, for English words, the sum of the individual character embeddings to form word-level embedding). positional embeddings, and the layout information of preceding tokens. We employ Transformer decoder layers and optimize the model using an L1 regression loss.
In this strategy, the input consists character embeddings, positional embeddings, and partially masked layout representation of tokens. We adopt two masking strategies: (1) randomly masking a contiguous segment of arbitrary length, and (2) masking each token independently with a 20% probability. Each masking strategy is applied with equal probability (50%). The model trained to predict the layout of the masked tokens. We employ 10 Transformer encoder layers and optimize the model using an regression loss computed over the masked tokens.
Masked Layout Modeling:
Masked Modeling with Conditional Flow Matching: This variant adopts the same masking strategies Masked Layout Modeling, but extends the masked modeling framework by incorporating a time-dependent denoising condition inspired by flow dow-matching objectives. addition to character embeddings, positional embeddings, and conditional tokens, all masked tokens are progressively denoised from noisy tokens over multiple timesteps. We employ a 10-layer diffusion Transformer to model this process and optimize using an LI loss over the predicted layout values (v (v-rrediction) for the masked tokens.
During inference, take the tokens of the first sentence in paragraph reference tokens and ask the models to predict the remaining tokens. The detailed model structures are illustrated in Figure I0 All Transformer blocks have hidden dimension of 512 and employ 8 attention heads. The models are trained with a batch size of 160 using the Adam optimizer with base learning rate of x 10-1
16

---

Published as a conference paper at ICLR 2026
Autoregressive Modeling
(b) Masked Layout Modeling
(c) Masked Modeling with Conditional Flow Matching
Figure 11: Visualization results of Chinese text-line generation. Black rectangles indicate the target layout, while red rectangles denote the predicted bounding boxes.
(a) Autoregressive Modeling
(b) Masked Layout Modeling
(c) Masked Modeling with Conditional Flow Matching
Figure 12:
Visualization results of English text-line generation. Black rectangles indicate the target layout, while red rectangles denote the predicted bounding boxes.
A.3.2 HANDWRITTEN TEXT IMAGE GENERATION DETAILS
We list all LoRA fine-tuning parameters in Table With LoRA rank of 32 and a scaling factor of 32, the LoRA modules collectively introduce approximately 115.9M trainable parameters.
A.4 ADDITIONAL ANALYSIS OF STYLIZED LAYOUT GENERATION
We provide two visualization examples for Chinese and English layout generation in Figure TT and Figure 12 respectively. In each figure, black rectangles denote the target layout, while red rectangles represent the predicted character bounding boxes produced by each method.
In Figure the Chinese example illustrates that our masked modeling with CFM successfully captures the sopward-slanting trajectory of the text line-ffre tie-from the lower left toward the upper right, In contrast, the slant is much less pronounced in the layouts generated by the Autoregressive Model and the Masked Layout Modeling bascline.
In Figure 12 the English example shows that masked modeling with CFM provides noticeably better control over word spacing and word size compared with the other two strategies, leading to a more coherent and visually consistent layout.
Overall, the layouts generated by masked modeling with CFM demonstrate superior visual fidelity, aligning well with the quantitative improvements reported in Table
17

---

Published as a conference paper at ICLR 2026
Parameter Name
#Params (M)
Xemmeeder
0.1106
Model
Style Score (%)
001..00000000000 10-18.noon 18.orml linnar
13.072
mansformer.blocke. 1rrckks.--8... 18.att..t
3.7355
English Models
htttffommmrrbbocss.oo (rtocks.--1..att..... tt.ttt..
3.7355
HWT
39,62
3.7355
VATr
45.19
transformer_bie Sunsffrrerr.boocks0--1... -l.t.k..........ttt.ttt..
3.7355
One-DM
50.73
transformer blocks.0-18.ff.met.22
9.3389
DiffPen
61.26
single transformer blocks.0033..norm mnne
14.942
InkSpire
78.58
single transformer Blocks.0-37.pp. mlp
18.678
Chinese Models
22.413
One-DM
51.34
7.4711
TGC-Diff
60.46
single tiglle.transfomme.. Stw.eneerrhoocoO--- mootkks..-7..atmm
single transformer 3-.....tt.ttt..kkkkkkkk
7.4711
InkSpire
86.28
7,4711
Total
115.91
Table 9: Style Scores (%) of different models for English and Chinese text-line generation.
Table 8: List of all LoRA parameter names.
A.5 ADDITIONAL ANALYSIS OF STYLIZED HANDWRITTEN TEXT-LINE GENERATION
A.5.1 VISUALIZATIOON ANALYSIS OF POSITION ENCODING ABLATION STUDY
We provide visualization results for the ablation study on positional encoding. Figure 13 examples of both Chinese and English text-line generation. The red bounding boxes highlight stances where the model produces structurally incorrect handwriting.
presents
From the visualizations, we observe that using the original positional encoding of the Flux-fill back- bone leads to inaccurate spatial localization, causing the model to generate handwriting fragments incorrect positions within the image. In contrast, APE improves positional consistency, and the proposed R-APE further enhances structural accuracy. The superiority of R-APE over APE is par- ticularly evident in the correctness of character shapes and alignment, which is consistent with the quantitative improvements reported in Table and and Table
A.5.2 VISUAL.IZATION ANALYSIS MASKED INFILLING STRATEGY ABLATION STUDY
We provide visualization results of the ablation study on masked infilling strategy. Figure presents examples of both Chinese and English text-line generation. In both figures, the red bounding boxes highlight cases where the model produces structurally incorrect handwriting.
Overall, the stylistic appearance produced by both the random masking (R-Mask) and the fixed lop-region masking (F-TopMask) strategies remains comparable and closely aligned with the target style. However, subtle differences emerge at the fine-grained character-struucture level: the R-Mask strategy leads to fewer structural errors, likely because its randomly sampled masks expose the model wider range local character patterns during training. These qualitative observations are consistent with the quantitative improvements reported in Table 6 and Table 7
A.5.3 STYLE SCORE EVALUATION
evaluate style fidelity using text-line style classifiers built upon ImageNet-pretraine Swin Trans- formers (about 86.8M parameters), trained separately for English and Chinese. All text-line images are normalized to 64x 64x1024 resolution by proportional resizing to height 64 followed by width-wist wise cropping white padding.
The resulting classifiers reach 94% accuracy on the 60-way Chinese validation set and 91% % on the 161-way English set. Their prediction accuracy generated text lines is reported the style score, indicating how closely a model replicates the target handwriting style.
As shown in Table InkSpire consistently attains the highest style scores across both English and Chinese settings, outperforming all baselines and aligning with trends observed the user study and earlier style-consistency analyses.
18

---

Published as a conference paper at ICLR 2026
Template:
Style Ref
Bascline
+APE
-R- APE
Template:
# 30
Style
F Ki
-
byj مجم 2x uo 2/3
Baseline
13
(61 No of.
+APE
PR.
424
Ap
2
Kn
+R- APE
88-2
50
GT
2 $
3 R
15
de
them out. Just what these orders wi b we don't
Template:
FBa Peotans, he raid, "ara prosimebly here to
Style Ref
the witl aaey. ousliee, then out. Jusst
Baseline
them out Klugtt what thers dipers will la, we donnt 4
Ieam out Juet what there orders will be, we don't
+R- APE
Iemn out Juot what theoe orders wall be, me dan it
GT
night exceptioaalillllll ly bri lint and strange, Something's up said tond Undartone zine Ozdgit bricnally kennight, exceptionallnn = night, exceptionally brilliant and shrang,, night, exceptionally brilliant and strange night exceptinnall brillient and otrange
Template:
Style Ref
Baseline
APE
GT
Figure 13: Visualization results of Chinese text-line generation for the positional encoding ablation. Red rectangles highlight cases where incorrect character structures are produced.
19

---

Published as a conference paper at ICLR 2026
Template:
$ A | 5 54
* 
Style Ref
13 k
33 14, - An'
F-oopMakk
5/3 54
R-Mask
i26
* 5
46 4
GT
4
16
Template:
09
1 R
in
Style Ref
2x ko1
p. k6
F-TopMusk
et P
& & 3A
30
R-Mask
2 to AG
GT
te te 49
AM
8
&
Template:
buzzing sound. had just finisted
flying saucer tilting and dipping
Style Ref
Deadly eiileea,, clacccly peotent
Gonsury amauel the goudt uam of the Chatena
P-TopMask
sounci He Food junt Priched
Aring aucyr filling Migfen oven
R-Mask
buzzzin sourch
Gleat finehed
Pijing rewait was tillinn and dipping over
GT
buazing
souncl
He
neal
just Finished
[iging Sauces wao Wiiting and happing ooea
Figure 14: Visualization results of Chinese and English text-line generation for the masked infilling strategy ablation. Red rectangles highlight cases where incorrect character structures are produced.
A.5.4 ANALYSIS OF INTER-LINE STYLE CONSISTENCY
We define inter-line style consistency as the stylistic coherence between multiple generated text lines that share the same textual content but use different style reference lines from the same handwriting passage. As discussed in Section 3.3.1 compressing reference lines with different slant angles to fixed height introduces mismatched character scales, which leads to noticeable style discrepancies among the generated lines. Moreover, the reduced resolution further weakens the model's ability to capture fine-grained stylistic cues.
when different style-reference lines are used to condition the generation of the same text, visible stylistic variations emerge. Highly slanted reference lines, once height- normalized, become excessively small, making it difficult for the model to extract meaningful style information and resulting in outputs resembling printed fonts. In contrast, when the original-size reference is used to generate multiple lines simultaneously, the resulting handwriting demonstrates substantially improved inter-line style coherence. A similar phenomenon is observed for English text-line generation, as illustrated Figure [6]
As shown in Figure [5|
In summary, maintaining the native resolution in training and inference markedly strengthens the model's 's stylistic imitation capability and inter-line style consistency, while reducing the style insta- bility caused by slope-induced compression artifacts.
20

---

Published as a conference paper at ICLR 2026
Original Handwritee Pasage Image
A (9 M.R
Eiu
Roof
44 20
My p-
et m) Pra
:-
di ft & PS-4
B-N4 7
Se S0 12
2 (6
As the 36 48% of R8
& 2018
Templlare
Terplire
10e E Py tee M R xhaach Et Atd
axe B y tee 90 32 w *hwech Etts 3id
Style Reibennce Irange cosccaed beight plucls
Style Redeence Imagge corigital resofation)
the aj & Au u.. & Fj 54 Generate Handweiting
M... AL 1..6 & 6:
Generaee Handbriingg Image
Mac 5
di
y
à. s
Refienne Iesaae Gescaled begin
Reiaense Iasage coriuizal
2. 8 8.
Genernted Handerting Irange
Genernee Haadoetinng Image
sa 8.86
#2 B) 05
@ 12-223 68
Figure 15: Visualization results of Chinese text-line generation for evaluating inter-line style con- sistency. Red lines indicate the style-reference text line, and blue lines indicate the target text line.
Oriyial Dosrage beage
de did wilh. téght i Teuch with the Avtman tht was pauing of as hie snother Stormouthh maanage to lobes hir tonfuducct. led simne shat ake was an honut evrrggh coruare prlyy hi reaeed unaen as eleer as it woild have hew Mee skoved
Texelak
Tamplac
w nher confidence- tseems that
winher confiidence dence- tseems that
Styte Reiernoe buage coadaadd beight pincia)
Style Retronee traug congrut rouiitico
smilllr Neeemistt qiarigge to
AelMirr Mloromouth matiagge to
Ocvenned Haniwwiing
Generiet Handectina trane
Sui Ae copridlence. a semns Mhat
Rdite for Chaftelene to sevms that
Styte Reiorese bease trovaded beigh pincal note teotugle centonyy Canamned
Style Retronee tragy urggra restenee) Ahl N858 ahe Anruit enough sottanyg biffes Mes coofideece. sceur Ahat
I fer coole devcce secues that
Figure 16: Visualization results of English text-line generation for evaluating inter-line style consis- tency. Red lines indicate the style-reference text line, and blue lines indicate the target text line.
21

---

Published as a conference paper at ICLR 2026
V Visual Input
Cuncolante Space
Parunckr
Froza Parmmeter
1. Freze VAE Lacoders
Freom Enceder
Trainable Content Encader
Syft
Origin Iraege Patek
Carienl Tegul
Origie Tenag.
Curient lapul
lapst
and
Monders
Auadin
Mancie
Nhedee air chatf
Dreeless of
nove mus
Content Rocoder
Styk Escoder
Style Coedition
Splee Cunditias
Candition
Masied Rojas
Centcal
Madied Ragian
Castcal
Madked Ragian
C
Condition
Madued Candition topai
Masked Canditiie
Madied Canditio Iopat
Figure 17: Overview of the visual input processing under different encoder configurations.
FID
KIDI
HWDJ
ACER
FIDJ
KID.
HWD.
CR
ART
Fixed-VAE
7.92
4.83
0.62
0.01
FAXed-VAE
10.98
11.45
0.41
92.92
91.56
C-Ene
20.48
10.31
1.58
0.05
C-Ene
17.46
19.52
0.91
59.32
55.58
C+S-Enc
17.39
8.97
0.71
0.02
C+S-Ene
16.53
14.37
0.59
94.74
93.48
Table 10: Ablation of encoder configurations on the IAM Lines dataset. C-Enc: only Con- tent Encoder trained from scratch; C+S-Enc: both Content and Style Encoders trained from scratch. KID scaled by 10
Table 11: Ablation of encoder configurations the ICDAR2013 Lines dataset. C-Ene: only Content Encoder trained from scratch; C+S-Enc: both Content and Style Encoders trained from scratch. KID is scaled by 10a,
A.5.5 IMPACT OF ENCODER DESIGN CHOICES
We conduct an ablation study to analyze the impact of different encoder configurations our unified encoder-less framework. As illustrated in Figure 17. design three experimental settings:
Pretrained- VAE Encoding (Default Setting). In the original setup, pretrained VAE is used to encode both the origin image patch and the content image.
Learned Content Encoder Only (C-Enc), In this variant, we train content encoder from scratch to extract content features, while the origin image patch continues to be encoded using the frozen pretrained VAE.
Jointly Learned Content and Style Encoders (C+S-Enc). In the third configuration, both the content encoder and the style encoder are jointly learned from scratch.
Following Dai et al. 2024), both the content and style encoders adopt a CNN+Transfore archi- tecture. The CNN backbone ResNet-50 pretrained on ImageNet, followed by Transformer encoder with layers, a hidden dimension of 2048, and 16 attention heads. To adapt the repre- sentation the DiT input format, we append linear projection layer that maps 2048-dimensional features to 64 dimensions. The proposed encoder contains approximately 124.4M parameters, while the frozen VAE encoder has about 34.3M parameters. Quantitative results for the three configura- tions are reported in Table [o] and Table
Figure presents visualization results for Chinese and English text-lines. We observe that when training encoders from scratch (both C-Enc and C+S-Enc) the generated strokes tend to appear no- ticeably lighter. This is likely caused by a distributional bias between the latent space learned from scratch and the latent space of the pretrained VAE. Furthermore, for the C-Ene setting, the model more easily captures complex cursive styles but struggles to maintain accurate character structures. In contrast, the C+S-Enc setting produces outputs that resemble cleaner, more printed-like like handwrit- ing, but with reduced stylistic diversity. This suggests that jointly learning content and style encoders may shift the model toward different balance between content fidelity and stylistic variation. No-
22

---

Published as a conference paper at ICLR 2026
Template:
Style Ref
,
Ne
Re
VAE
C-Enc
CIS-- Enc
GT
Template:
Style Ref
# 64
Fixed
& 1 <t 26 14644
C-Ene
T4448
/4. d0
C'ISEEne
td d J. 1500 ale @ M A Nudl s8 Brof HE d' & LBA
k t 13 2 1 & 2/2 ( 1 fa leos aatt 26
Template: to keep an appointment with Heather
atan intrrestion for 25| lighh caae Athis
Style Ref
Mpsr hast Rescuedd ascchir period of
vaperflwwo peecuska aa for then
Fixed VAF
kep ay appointrand with Huather
af nu inborccionn for He gettt to daag At
C.Enc
keep au apperanicnss with Mutther
leberssion for te Rig de dagge Aitee
C'S-Eni Ene
keep apprintmend with Hea ther.
idkrraaion for tedlyy to daagge Att his
GT
to due ap apporaliiced wath Yeather.
ihrenda for th Eflt to daye lix
Figure 18: Handwritten Chinese and English text-line generation results under different encoder configurations. Red rectangles indicate cases where incorrect character structures are produced.
tably, Table I0 shows that C+S-Ene achieves higher CR and AR scores than the frozen- Prozen-VAE-barr VAE-based setup. This is because although C+S-Enc often generates characters with multiple extra strokes, these artifacts rarely mislead the downstream text-line recognition model, resulting in higher recog- nition metrics despite localized structural emrors.
Compared with Chinese, English exhibits fewer character-level errors and generally acceptable styles, although the overall faint-stroke phenomenon still exists. This further indicates that Chi- nese handwriting is more challenging to model than English. Similarly, the C+S-Enc configuration yields more neatly shaped English words.
Overall, for both Chinese and English, the C-Ene and C+S-En settings underperform the frozen- VAE baseline. This may be partly attributed to the inherent alignment between the pretrained VAE's latent space and the subsequent Diffusion Transformer, which provides a stronger and more stable representation than training the encoders from scratch.
23

---

Published as a conference paper at ICLR 2026
(a) Single Line Generation
(b) Multi-Line Generation
ART
j $
#
1 ppp
Figure 19: Visualization results of failure-case analysis. The red boxes highlight the erased marks mistakenly generated by the model.
A.5.6 ANALYSIS OF FAILURE CASES
common failure mode we observed is that the model sometimes fills in the spaces of a text line with artifacts that resemble crossed-out erased characters. This behavior stems from the training data: some authors habitually strike out a mistaken character and then write the correction beside it. When the generation strictly conditioned by the content template, the correct action would be to preserve an empty region (i.e., gap) for the removed character; instead, the model often produces residual "erasure" marks that region. The problem is particularly pronounced at paragraph-Ievel generation and near image boundaries. Representative examples are shown in Figure 19
A.5.7 ANALYSIS OF STYLE-CONTENT CONTROLLABILITY AND DISENTANGLEMENT
We conduct an explicit style-content disentanglement experiment to verify that InkSpire maintains independent controllability over style and content, despite embedding style, content, and noise into a unified latent space. Given a fixed content template, we generate handwriting samples condi- tioned on multiple distinct style-reference lines sourced from different writers. This setup evaluates whether the model can vary stylistic attributes (e.g., stroke width, slant angle, character curvature) while keeping the textual content unchanged.
As shown in the visualization results (Figures 20
Pink boxes highlight the handwritten style of the letter g, and purple boxes highlight the style of the character sequence "th". We observe that within each sample (e.g., sample b), all occurrences of g and th exhibit highly consistent writing patterns that closely match their respective style-reference samples. Meanwhile, samples and show strong stylistic divergence from each other, reflecting the differences in their reference styles.
The red arrows indicate the slant direction of characters in samples e and d. Sample c shows uniformly left-leaning slant across almost all words, matching its reference style, while sample d shows a consistently right-leaning slant at nearly uniform angles-again mirroring its style reference.
These observations confirm that the unified latent representation does not collapse style and content. Instead, InkSpire successfully preserves the content while expressing distinct stylistic attributes, demonstrating robust and independent controllability over both factors.
A.6 MORE VISUALIZATION EXAMPLES FOR MULTI-LINE TEXT GENERATION AND EDITING
We provide additional visualization results of InkSpire for multi-line handwritten text generation and character-level handwritten text editing. Figure presents examples of English multi-line text generation, while Figure 22 shows the corresponding results for Chinese. Moreover, Figure 23 illustrates character-level editing results on English handwritten passages, and Figure 24 displays the editing results on Chinese passages.
24

---

you Published as a conference paper at ICLR 2026
Content Condition
than three thousand three fifty at the most. Now you could get suite nice little seni-detached house in Grinstesd for three thousand, that's stere line, just before the green belt, lovely and madern, you know. forgot you, don't usually lend any house ear lir than 1918
Style Reference
Style Reference
"Good del Bau for Pies
ohill joill rome. dlvwiing and
And filon wa 02 for rhail along Av Generation
Generation
Heau Hoves Hasusanea Hures folty at the most you
Houcardi AUEo PiPky
could iick quite wice little sami de locked homse in 1211
could Fgh quie a nice willlo comi- dbooood vave in Grinddaci for TTuls (ilooard Thlt's waee liv, jus loofa ixttan bolk, loocy and mackrn, you know dan'a voudly land any ucuoz build antior Thón 19r3
for (tiges Phheessed Filfat's 's were like gust befove the lifiem bell, lovely and moderss you konow. tofjet tell you, we don't usually any Inwse built sarlly Phaee A3
Guimsteed
fotaot 2
No kel
Style Reference
Style Reference
Bunc wwond it satn poooerree iits my slang
lett, Gal Ratio Buy day ? tis maper anre
Generation
Generation
Mone Rosweadd Moma litty at the most you could get quite a vies Wide sami datachal' house in brimateed for thna threaad Meats war is live gisst inforss grann kall, and modern, you Yeuaas lorreet no you, we don't unullyy any heus bilh santies then SSSS (c)
tinee theeiand Myyy and op quit as nie lilll urn deteched nove in Griniled foi tree thawndd Hal's lipor igen bet, lodty modion you know. Apdll ce you den ueolly lest ar noue bett water than m1s.
you
(d)
Figure 20: Visualization results Style-Content Content Controllability and Disentanglement. Pink boxes mark stylistic variations of "g". purple boxes highlight "th", and red arrows indicate the global slant direction.
25

---

Published as a conference paper at ICLR 2026
Ground Truth G
Predict
"Bod dal Oue for the guit-rame rone dlariing and kell Guoge buing in the borte du jau." Bauley was man of his word the hept away from Save mathers, was au exceldent host and sprendió varontuur The waming
"Good del Gue for the guitt guil-romm come dediing and lett Gonge biing the Gearte du jour any Bowr tow man of his word He kept awayy from Service matters, was excellent beot and a splendid raconteur The evervry
Solo Toun
rell Coerge the divette poas"" drves Service matien, romong up
wond
Bud waadd it wat portectedd wn's sing fou juit gave leach to wa dindt vaus potiing your lowane 3 patting, i hing you showtdonn I jeda aboun surions thanng late engaguone
and weatt is satn protectedd le's my vinin
You just gour baoh me "I vamut clich it, was johinn you howts was nams Soring, bigel far shouth about sar onms thing fime engeing engeremeent
Tarpet
"Bat eontis" 11 the pesteteed "iriing ring""
didn't was jabing You Lieas
felle hull Ret Boy day to puleer mnne ill the ball or be gpenad foet te radied dritidy aef, tunng ti back to has cmbtirad to taly the beaf. aaze into Wliding blocan the de it d-yondff apleard and the pelited foo foolan poieds dell yoor
sheat vrtion
Belli; hal Rad Bay day He cokeer mone olle ball as he openel the forst deer de naded iribalty
an,, loning back le be mostedd to tade biif- aal into aidding behonn he do youd tarpppnd
and te polthel bealt laulern pourde Hell and ad
Sude Tea
Torger Tane.
heeppeed
curning bock
shis bettl.
And luon uoo to go for chall doogg tho kentt, waon Cie luve wustm'l be weeket ue goshueed locards luo bay where so pes uao wao just risig war to roús, lloon Rou we uond on lao Goy auildd al Wiw
Anci wo @o for a rhall adlnng Ar bouss. A moon like Mio musin' a be wastoa No gosurad Acuaards Ao boy unsrz the Full mocn was gust rising ooor Ar rocks, Mloo nor Soid his hand on lars Goy smilad at him xn
Style "Aedd then will for aordl sileg
beaach like
Target Tean
warted"
ramd was just civing the ruclo, this
Good bye Saode paiat with deadly finally Wakel him go, unalle to speak, the fell that part of hes was lraiing him She cocldd 4 brake bim
good bye faodra, LL said with dea My h fimality Maehing bin yo, unohle to speaks, ahre bell Hhal part of hee was leaving Ahimn She cosetolej hake him
Birget Tase
him use sprnk the part karing
Figure 21: More examples of multi-line English paragraph generation.
26

---

Published as a conference paper at ICLR 2026
Ground Truth
Predict P
Tongst Stec
Soll Trat N.EMEE 1
9,b E
R
till giiat
Ao Big 1e B34
te ae
(8+ (e-hi 2o. 4ift
7549 f8... R. Eiff
Bi +
a $5.
ark
PASA M6
1
4
67
#
Theger
A SE R...0900.0000 EUK 51 AAW. P4
Figure 22: More examples of multi-line Chinese paragraph generation.
27

---

Published as a conference paper at ICLR 2026
Original &
Edited P
"Good del :ouifor the gull-room darwing and Well Guoge to bniggingtte barte du jau. Bandey was man of has word iee lhigh away from Save masting was an excelaunt heat and splandid d racenteur The taaeiing
"ood dal: ".... twolfor the grill rome dariing and kell Groge to buing minglooftee infthe bare du jaur Baully was man of his word tee FFeedd awayy from Seara reassons was an endllonn hast and splandid racouteur The morving
Tanget Best:
Tanget lust:
-Gosd-akt One for aril-rrom, darting, and sell George in bring in the tearte du jour." flawkey was mon of word. lis kept awaa from Service motters, was cueelleat bost and spicodid recontear. The evening
-ood Two the grill darfingg and sell George lering the du jour." avay Service
man
keep
as splendld
morning
Belte bal Red boy day du mafeer ann ill hult to gpeeed the for te radied writaly litt ......
Helle, Gull Red Kay tay ? He paker ann inl Mell la grece lhd pack dlor lt radied arielll thd .....
aef, turing ti back to bas antiral to thty the benff- inte 4lt biding bllwee the do it yourdff az foard and the pelited toow paolen ponder sell yeee
and, tarring he back to hor, oetired to taty the beaf aove into biidling belccer the to i yourdlf oy fowrd and the pedoled boood foeslen poodds- shell
Target Test
Target Testi
'HELO, CECR. HAD hany day?' His mother come into the bill as spened the frant dooe. He nodded irritally and, turaing bi back to bet, contrived sidde the beidf- into bisting betwres de-ll- sossstllll cuphaard pullched brom buander dirlecuu
"HELLO. CECIL T.. HAD bany day His mother come into the hell to opened the back door He nedied irritably and, turaing his bock to her, contrived is sids the beicf- one into bitting berween the do-l-surrll caphaard and poliched bustrrenguander chellcune
Bedd waad i wat proterted we the 'our garigaur ist leach to imai II dish Hi was poiin Youloworrs was
Bnd wand it wat prodended wy You just graned it leach to II
diatnn tha i was poiiig pou-hagaa lotinng Acdran? you shouldnn joto anoun
potirn, Auige? you showldyy joty torouet aboort seriorss things dite engagummtt
things libe enagagmmetn
Taraet
Target
want grotested
slng."
gave
juking jeking, Sigel"" shoslda'
dida't Natan. "Yoa shouldn' around wernos things empiemman."
things like
Figure 23: More examples English paragraph editing.
28

---

Published as a conference paper at ICLR 2026
Original G
Edited P
Torget Teat: S 11
+
A Jug
A A
# i BRA
10 of
tr
2578.85-1
#6
Figure 24: More examples of Chinese paragraph editing.
29


Reviews:
[{"review_id": "3726TjXORz", "reviewer": "Reviewer_iDHx", "summary": "This paper introduces InkSpire, a diffusion transformer model for stylized handwritten text generation, which unifies style, content, and noise within a shared latent space, thereby eliminating the need for auxiliary encoders. It utilizes a multi-line masked infilling strategy to train directly on raw text-line images and a revised positional encoding to support arbitrary-length multi-line synthesis and fine-grained character editing. Experiments conducted on bilingual Chinese-English datasets demonstrate that InkSpire achieves superior structural accuracy and stylistic diversity compared to prior state-of-the-art methods.", "questions": "Please refer to Weaknesses 1-3.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. This paper introduces InkSpire, a new framework that reframes stylized handwritten text generation as a multi-line masked infilling task. This approach enables the model to unify style, content, and noise within a single shared latent space.\n\n2. To support this unified framework, the paper proposes a crucial revised positional encoding scheme, Rotated Aligned Position Encoding (R-APE). This technique effectively solves the challenge of aligning tokens between the spatially concatenated style image and the standard-font content image.\n\n3. The paper conducts extensive experiments on both English and Chinese datasets, demonstrating the effectiveness of the proposed method.", "weaknesses": "1. The paper rightly points out that prior methods for resizing single text lines to a fixed height introduce distortion. However, it is unclear how the proposed Multi-line Masked Infilling Strategy avoids a similar limitation when processing entire multi-line images of varying sizes.\n\n2. This paper lacks a direct ablation study for the core architectural contribution—the unified, encoder-less framework itself.\n\n3. The LoRA training details are incomplete, which hurts reproducibility. While the paper specifies the rank (32), it omits other critical hyperparameters, such as the LoRA alpha value, and fails to report the total number of trainable LoRA parameters.\n\n4. The evaluation relies entirely on automated metrics. We suggest adding a user study to better validate the perceptual quality and stylistic fidelity of the proposed method. \n\n5. We suggest that authors provide visual examples of ablation study results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "G0nh2ReGa9", "reviewer": "Reviewer_ejPb", "summary": "This paper presents InkSpire, a unified diffusion–transformer framework for generating personalized, multilingual handwriting. The model integrates layout prediction, glyph rendering, and image diffusion into one pipeline. Unlike previous methods that use separate encoders for style and content, InkSpire represents both in a shared latent space and trains on full-page, multi-line handwriting data. A conditional flow-matching (CFM) approach is introduced for layout generation, and a modified absolute positional encoding (APE / R-APE) is proposed to stabilize feature alignment. Experiments on IAM (English) and ICDAR2013/CASIA (Chinese) show improvements over prior handwriting synthesis systems in FID, KID, and content recognition metrics.", "questions": "* Table 1 reports layout-prediction L1 errors, but the paper never specifies how masking was performed during test-time prediction (e.g., partial vs. full masking, sampling procedure).\n* It is unclear whether the reported errors correspond to full-layout generation (M = all) or masked reconstruction.This omission makes the results non-reproducible and the comparison between auto regressive, masked, and CFM models ambiguous.\n* The layout generation could be the single source of failure in case of handwriting generation and transparency in its complete methodology is of significant importance for paper’s reproducibility.\n* No details are provided on the masking ratio or noise schedule used during training for masked modeling or CFM. Without this, one cannot replicate the training regime or interpret robustness to partial conditioning\n* By embedding style, content, and noise into a single latent, InkSpire sacrifices controllability: the ability to vary style independently of text content.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Integrates layout prediction, style conditioning, and handwriting generation in a single diffusion transformer\n* The editing and generation by same model is a Novel contribution in field of handwriting generation, thou such masking and generative techniques have been explored before in printed text documents like UDOP (Tang et al., 2023) and DiffUTE (Chen et al., 2023) and should be properly cited.\n* Demonstrates bilingual (English–Chinese) generation using one model\n• Qualitative results show realistic handwriting style imitation and fine-grained local editing capabilities.\n• R-APE and LoRA fine-tuning on a frozen FLUX.1-Fill backbone allow good performance with modest compute", "weaknesses": "* The paper claims “strong style imitation” but provides no quantitative metric for writer identity preservation e.g., writer style identification accuracy as presented in WordStylist (Nikolaidou et al., 2023) and StylusAI (Riaz et al. 2024).\n* All “style fidelity” evidence is qualitative and there is no test of whether generated handwriting is still identifiable as belonging to the same writer. This is important since during inference time specifically when using the model as a handwriting generator and not editor, the style sample present is just a single line style handwriting image and model is inferring style based on that.\n* Smoke testing for out of distribution handwriting styles e.g. handwriting style of someone not in the IAM db database would also provide usefull insights.\n\n* The lack of explicit writer-style metrics, undefined masking and noise procedures, and dataset-specific ambiguities weaken its reliability. The conceptual ideas (CFM for layout, unified latent fusion) are valuable, yet the paper would need a more transparent experimental protocol and stronger evaluation of stylistic consistency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0U23HWmKbn", "reviewer": "Reviewer_c7ya", "summary": "The paper introduces inkSpire for the handwritten text generation task, which encodes style, content, and noise into a unified latent space. It aims to enable the efficiency of information interaction within the common latent representation and improve generative performance. Experiments evaluate the proposed method.", "questions": "1. What are the benefits of encoding style, content and noise into the same latent space? Please provide more explanations and proofs. Why does a unified representation achieve better performance than complex handcrafted losses?\n2. How is the implementation of the comparison methods carried out? Is their comparison fair?\n3. Please provide more necessary experimental analyses. For details, please refer to the weaknesses 3 and 5.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The proposed Aligned Positional Encoding(APE) effectively distinguishes the style and content information in the visual input.\n2. The proposed inSpire can simultaneously achieve paragraph generation and editing.", "weaknesses": "1. In lines 075-076, this paper claims that a diffusion model that uniformly handles style, content, and noise can achieve more effective information interactions in a common latent representation. This claim lacks some references or proofs , please provide a more detailed analysis and experimental verification.\n2. Please provide more implementation details, such as which part of the FlUX parameters was fine-tuned by lora, the image size and batchsize used?\n3. The ablation study of the proposed multi-line masked infilling strategy is lacking.\n4. In the English comparison in Figure 6, \"One-DM\" and \"diffusionPen\" appear to be concatenations of words rather than direct generation of text-lines. I want to know how they are implemented. Is the comparison fair? Moreover, why is there no visualization of VATr++ shown?\n5. There is a lack of failure case analysis and user study.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1QXmBUtpnP", "reviewer": "Reviewer_RH1H", "summary": "This paper designs a unified diffusion model for processing style, content, and noise for the task of handwritten text generation, eliminating the need for additional style or content encoders and achieving performance improvements.", "questions": "1. What is the rationality of encoding different types of information such as content and style into the same latent space?\n2. It is recommended to provide more details about the layout generation method in inkSpire.\n3. What are the advantages of the proposed multi-line masked infilling strategy compared with text-line resize preprocessing?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The proposed InkSpire is trained on a Chinese-English mixed dataset and achieves bilingual handwriting generation under a single model.\n2. Figure 2 clearly show the representation process of layout information.", "weaknesses": "1. Content and style references represent different types of information; I question the rationale behind encoding different information into the same latent space.\n\n2. The description of the layout generation method is overly simplistic. I would like more information, such as model structure, loss functions, and training details.\n\n3. In lines 242-244, the paper claims that the preprocessing operation of resizing text-line images is suboptimal, raising my concerns:\n\n&emsp;&emsp; The authors argue that resizing text-line images overly shrinks characters in highly slanted lines. How does the proposed multi-line masked infilling strategy avoid this issue? When the proposed strategy faces multi-line images of inconsistent sizes, is resizing also necessary? Could this also lead to the same issues?\n\n&emsp;&emsp; The authors believe that resizing text-line images introduces inconsistent distortions. Would this issue still arise if the resizing is proportional?\n\n&emsp;&emsp; The authors mention that text-line images lack inter-line style. How is the inter-line style defined? In Figures 7, 8, the style references are single-line, and they may also lack inter-line style, yet the proposed InkSpire still achieves nice results. Please provide an explanation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)