MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis
Authors: Jun-Yan He
Zhi-Qi Cheng
Chenyang Li
Jingdong Sun
Qi He
Wangmeng Xiang
Hanyuan Chen
Jin-Peng Lan
Xianhui Lin
kang zhu
Bin Luo
Yifeng Geng
Xuansong Xie
Alexander G Hauptmann
Venue: ICLR 2025 Poster (2025)
Keywords: MetaDesigner
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: MetaDesigner introduces a transformative framework for artistic typography synthesis, powered by Large Language Models (LLMs) and grounded in a user-centric design paradigm. Its foundation is a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively orchestrate the creation of customizable WordArt, ranging from semantic enhancements to intricate textural elements. A central feedback mechanism leverages insights from both multimodal models and user evaluations, enabling iterative refinement of design parameters. Through this iterative process, MetaDesigner dynamically adjusts hyperparameters to align with user-defined stylistic and thematic preferences, consistently delivering WordArt that excels in visual quality and contextual resonance. Empirical evaluations underscore the system's versatility and effectiveness across diverse WordArt applications, yielding outputs that are both aesthetically compelling and context-sensitive.

Published as a conference paper at ICLR 2025
M MetaDesigner ADVANCING ARTISTIC TYPOGRAPHY THROUGH AI-DRIVEN, USER-CENTRIC, AND MULTI- LINGUAL WORDART SYNTHESIS
Jun-Yan He', Zhi-Qi Cheng. 2.3+ Chenyang Li', Jingdong Sun?, Qi He?,
Wangmeng Xiang', Hanyuan Chen', Jin-Peng Lan't Xianhui Lin', Kang Zhu', Bin Luo', Yifeng Geng'. Xuansong Xie', Alexander G. Hauptmann
1 Alibaba Group, Institute for Intelligent Computing (Tongyi Lab) 2 Carrneiee Mellon University, Language Technologies Institute (LTI)
3 University of Washington, Tacoma School of Engineering & Technology (SET)
Project: http://waadekooe..cuuuaaooowoaae
ABSTRACT
MetaDesigner introduces a transformative framework for artistic typography syn- thesis, powered by Large Language Models (LLMs) and grounded in user-centric design paradigm. Its foundation is a mulli-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively orchestrate the creation of customiz- able WordArt, ranging from semantic enhancements to intricate textural elements. A central feedback mechanism leverages insights from both multimodal models and user evaluations, enabling iterative refinement of design parameters. Through this iterative process, MetaDesignnr dynamically adjusts hyperparameters to align with user-defined stylistic and thematic preferences, consistently delivering WordArt that excels in visual quality and contextual resonance. Empirical evaluations under- score the system's 's versatility and effectiveness across diverse WordArt applications, yielding outputs that are both aesthetically compelling and context-sensitive
INTRODUCTION
Typography, as nexus of linguistic expression and visual design, occupies pivotal role across diverse fields such as advertising Cheng et al. (2016) 2017a Sun et al. (2018). education Vungthong al. (2017). and tourism Amar et al. (2017) By functioning both communication medium and a form of artistic expression, typography demands nuanced understanding of aesthetics and design principles. The challenge for non- non-professionals lies in navigating the multifaceted considera- tions-ranging from visual composition to emotional
to produce designs that
are simultaneously informative and striking.
In recent years, generative models have accelerated advancements in typographic design by enabling rapid adaptation to varied aesthetic preferences. Nevertheless, integrating these models to satisfy elaborate typesetting requirements remains non-trivial. Two significant obstacles are noteworthy: (I) The subjective nature of artistic typography, which varies widely based on personal and cultural contexts and complicates the development of models capable of broad appeal: and (2) The lack of comprehensive, annotated datasets of artistic typography. which restricts the capacity of generative models to accurately reflect the vast stylistic diversity demanded by real-world applications. While text-to-image synthesis models- such as denoising diffusion probabilistic models Ho et al. (2020); Ramesh al. (202I) (Song et 2202T have made notable progress, they often struggle to address these intricacies in typography.
To tackle these challenges, we present MetaDesigner, a multi-agent system purpose-built generate artistic typography guided by user preferences. As depicted in Figure MetaDesigner comprises four specialized intelligent agents: the Pipeline Designer, Glyph Designer, Texture Designer, and Q&A
"Corresponding author. Part of this work was completed while serving as Project Scientist CMU.

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Pipeline Designer
Glyph Designer
Texture Designer Tature is
Q What's the proper deiggn "Merry Chrtonai"?
The giagh gonerated
[Glyph]: Christmas tree, snow, hills, pine needles, chimney, smiley snowman....
Merry Chrismas
Merry Chri Chrismas
[Texture]: gfft, rod gift wrapping. paper, pine needles,...
Figure Overview of MetaDesigner, illustrating the interactions among the Pipeline, Glyph, and Texture agents, collectively shaping WordArt to align with user preferences.
Evaluation Agent, each contributing to personalized WordArt creation within an integrated, user- focused workflow (see Sec. The Pipeline Designer acts the system's 's orchestrator, converting user prompts into well-defined tasks for the other agents (see Sec. p. The Glyph Designer handles diverse glyph styles-ranging from conventional lettering to semantic glyph adaptations--aallose the thematic context of each design (see Sec. 3.2). Building on these glyphs, the Texture Designer applies various visual styles through LoRA model matching (see Sec. (e3.3.3) based on hierarchical tree structure. Finally, the Q&A Evaluation A Agent refines the generated WordArt by integrating user feedback into an iterative, question-an and-answer answer refinement loop (see Sec. 3.4
to
The contributions of MetaDesigner are threefold:
Multi-agent system with evaluation and optimization. MetaDesigner combines distinct de- sign modules with an evaluative feedback process to discover and realize customized artistic typography styles. Through rigorous hyperparameter tuning, the platform consistently produces outputs that align with individual aesthetic preferences. Its accessibility is further evidenced by over 1,007, 293 visits on ModelScope
Advanced glyph design through a hierarchical model tree. The Glyph Designer leverages both comprehensive font libraries and semantic translation methods to support extensive glyph transformations. Equipped with a hierarchical tree of 68 LoRA models, MetaDesigner ensures rich stylistic diversity, significantly broadening the creative possibilities in typographic design.
Comprehensive dataset fostering continued research. To further support the study of artistic typography. we introduce a carefully curated dataset containing over 5,000 multilingual images (English, Chinese, Japanese, and Korean) spanning array of artistic styles and cultural elements. This resource provides researchers and practitioners with a robust foundation for benchmarking new techniques and advancing the state of the art in artistic text generation.
2 RELATED WORK
Text-to- Image Synthesis. Recent advancements in denoising diffusion probabilistic models Ho et al.
(2020); Song et al. (2021): Dhariwal & Nichol (2021): I Nichol Dhariwal (202I); /Saharia et al.
(2022) Ramesh al. (2022) Rombach al. [2022]; Chang al. (2023) have significantly enhanced
the fidelity and flexibility of text-to-image synthesis. These models have given rise to interactive editing approaches Meng et al. (2022): Gal et al. (2023): Brooks et al. (2022) and
controllable pipelines Zhang & Agrawala (2023 : Mou et al.: (2023); Huang et al. (2023). Recent Ma et al. (2023b), and InstantBooth Shi et al. (2023) leverages CLiP-based image encoders to bridge visual information with textual embeddings, expanding the expressive range of generative models.
work such ELITE Wei al. (2023), UMM-Difius
Visual Text Generation. Producing legible text within images poses substantial challenges Rom- bach al. 2022), especially when dealing with complex or multilingual scripts. Approaches like GlyphDraw Ma et al. (2023a) and GlyphControl Yang et al. (2023) address character alignment and rendering, while TexiDiffuser Chen et al. (2023b) introduces character-level segmentation to enhance text clarity. Large-scale language models further refine text generation (Saharia et al. (2022); BBlajii al. (202)): Lab (2023), yet traditional text encoders often struggle with non-I Latin alphabets Liu al. 2023). To address such issues, GlyphDraw fine-tunes text encoders and integrates -ased
glyph embeddings Ma et al. (2023a), whereas DiffUTE uses pre-trained image encoders to extract
glyphs for editing tasks Chen et al. (2023a).
Project: https://motelscoo..m/ssaaio/oo// wttp://eeeeeettooatoooo////ettaa
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WordArt Synthesis. The synthesis of WordArt Tanveer et al. (023 ) Iluz al. (2023); Berio et al. (2022); Tendulkar et a Zhang et al.] (2017) aims to integrate semantic richness with artistic yet readable text representations. Early work Tendulkar et al. experimented with replacing letters by semantically related icons. More recent models hamess large-scale generative techniques to push typographic boundaries. For example, Word-As-Image Iluz al. (2023) proposes artistic typography for Latin scripts, while DS-Fusion Tanveer et al. (2023) explores the generation of intricately styled texts, including hieroglyphs.
Positioning of MetaDesigner. Unlike prior methods, MetaDesigner adopts a multi-agent framework featuring Pipeline, Glyph, Texture, and Q&A Evaluation agents within an interactive feedback loop. This architecture iteratively refines semantic, glyph, and texture elements based on user input, covering a broader range of aesthetic demands and typographic nuances than existing approaches.
Pipeline Designer
Glyph Designer
Texture Designer
Pipeline Designer
Glyph Designer
Texture Designer
80
Instruction:
Generate Wordort for giving text
Instruction: Render the glyph with prompt: "peace dloves branch leaves, ocean...i realistic stylo... IInstruction Form pipeline designer)
Instruction: WordAr giving
"World Praaee with leaves, dove, branches, (Iestruction pipaline designer)
Text "World Peace" (Instruction Form User)
Program:
Tag: Chinss Style"
prompt- World Peace")
TaE "Womme"
LoRAs
JD
Real
Palnting
P] 10 Whitent (THENST
d'ualttonal
twet-"Mtrr id Peace (.
World Pauce
World Peace
World Peace
glypinta Y99e, proFpt))
Chain-of Thought
Thee- Thoughh Modil Selection
P_plpd <Prompt: : What objects represents
P.aly: I<Font: "Semantic",, >,, spromt: "branchy
Teg: Tusny
Iax: [<prompt: "leaves branches, sun
"Worid Peace" prefer
leaves."
Parameter Optimization
QA Eval: User & Multimodal Model Feedback Evaluation
Q: the <prompt> extension stoat -orll Peacer with branch and leaves, doves, sunshine... in realistic style- sottifly your intent
Are the [objects] Wordfrt the original prompt: "text <World Peaces with doves branch and leaves, sun...
User
[Preterencej " cartoon is preferred Score:
[Ohject1]: branch |- -deect branch and leaves..> [0lject2]: leaves | <desc: green leaves
Pleas
eQuality
[Object3]: sun [ <desc: sun around......
cGlypho, -.] generated image.
Final Reason: The image contains: branch, leaves, and sun; ocean is absent
Consistency 5
Consistency= 77 <Quality 7; <Shyp 1
consistency
LLOVA
the following object the image and describe
Wordhrt.."
[Consistency]
[Qualit]
Reward: pUeerepreeerence: "cartoon is preferred " In-Consistency Objs: PePce Dove"".. .... Quality 7 ...)
Language Model
Prompt Engineering
Image Synthesis
: Tunable Hyper Parameter
Figure MetaDesigner Architectural Overview. The framework integrates three primary intelligent agents-Pipeling Designer, Glyph Designer, and Texture Designer to produce personalized WordArt. A Q&A Evaluation agent runs in parallel to iteratively refine the design. This diagram highlights how textual inputs are transformed into visually compelling, user-driven artistic typography through an interactive, iterative process.
3 METADESIGNER FRAMEWORK
Overview. MetaDesigner is an interactive multi-agent system for synthesizing user-tailored WordArt. Four specialized -pienlnepppeinee Designer, Glyph Designer, Texture Designer, and Q&A Evaluation Agent collabotl to generate and refine typographic art according to evolving user preferences.
Mathematical Formulation. Let be the synthesized WordArt, defined by an operator V:
i i= v (s user o. P,M),
(1)
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where sl is the user prompt (specifying style, theme, or other attributes), 6 {dpip &Blly deer ) encompasses the functionalities of the four agents, .M denotes a model library (used primarily by the Texture Designer), and P {Pri,, pely, plex, pia) represents hyperparameters tunable via feedback.
Iterative Process. To adapt and improve results across iterations, MetaDesigner leverages previously generated images. Let prev be the output of a past iteration. The synthesis process then becomes:
= v (s o, P.M) -di (ster)
@ (oot) s0x, M) o (Ipev. prt),
where 6PPP produces an extended prompt S from guver, This extended prompt divides into {aety, s guiding the Glyph and Texture agents.
The Q&A Evaluation Agent reviews both the previously generated image 1pev and hyperparameters pae, then incorporates user and automated feedback to refine system parameters. Through this loop. MetaDesigner incrementally aligns outputs with user preferences, producing visually coherent and context-aware WordArt. As shown in Figure 2 the sections below detail the functionalities of each agent, illustrating their collaborative roles in the synthesis pipeline.
Feedback and Adaptation.
3.1
PIPELINE DESIGNER AGENT
The Pipeline Designer is responsible for translating user instructions into a structured workflow, ensuring seamless coordination among the remaining agents. By combining visual programming techniques with a Chain-of- Thoughh (CoT) prompting strategy, this component simplifies the design process into manageable steps while incorporating iterative feedback to refine the final output.
Visual Programming. As depicted in Figure 2 GPT-4's in-context learning capabilities are em- ployed to generate visual programs directly from natural language instructions. These programs, assembled without explicit fine-tuning, consist of sequential "blocks" or "modules," each defined by:
A module name, identifying the function or operation.
A set of input arguments with specified names and values.
An outpul variable name that receives the module's result.
This modular structure enforces clarity in the synthesis pipeline, allowing straightforward validation and debugging of intermediate steps.
Prompt Extension. To tailor output to user preferences, the Pipeline Designer employs CoT reasoning within GPT-4. Beginning with user prompt s suser, the system generates enriched prompt that includes specialized instructions for the Glyph Designer and Texture Designer:
S = ( = s 10x
(3)
Here, (PiP represents the Pipeline Designer function. By posing clarifying questions related to style, application context, and design constraints, GPT-4 refines the initial prompt into two well-defined components: gely (for glyph design) and stxx (for texture design).
Feedback Integration. A key advantage of the Pipeline Designer lies its ability to incorporate user feedback in an iterative loop. Formally, feedback is modeled via a function F that aggregates various signals G (e.g., user evaluations, automated metries), along with an update directive & update.
PPPP
F(G updale).
(4)
where PRic denotes the updated set of hyperparameters for the pipeline. The Pipeline Designer also returns the enriched prompt S needed by downstream agents:
S. prip
piP
(s user ppip, F(G 8 update
(5)
This guarantees that subsequent design iterations progressively align more closely with user intent. Through active integration of user preferences and continuous prompt refinement, the Pipeline Designer ensures that each step of the workflow converges toward the most suitable artistic outcome.
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3.2 GLYPH DESIGNER AGENT
The Glyph Designer is central to MetaDesigner's pipeline, capable of producing three distinct glyph types-Normnn Traditional, and Semantic-to accommodate wide spectrum of stylistic needs. While Normal and Traditional glyphs are suited for formal contexts (e.g., weddings, galas), Semantic glyph transformations cater to more imaginative or humorous applications. GPT-4 automatically determines the most appropriate glyph type based on the user prompt.
Normal & Traditional Glyph Generation. In formal scenarios, the system renders conventional or culturally traditional glyphs with the Free'Type font library:
G.,, G. = (s dh (s""))
(6)
where on and o, are specialized rendering functions, and g denotes the glyph-specific prompt. This setup ensures a clean, dignified aesthetic for occasions demanding a formal tone.
Semantic Glyph Transformation. For creative and humorous contexts, the Glyph Designer supports semantic glyph trunsformations through ds.. These transformations reshape vector-based glyphs to approximate target objects or thematic concepts, leveraging differentiable rasterization and a depth-to- to-image Stable Diffusion model:
=, sem M),
(7)
where gaem is a semantic prompt capturing the desired concept, and M references the model library. During this process, vector parameters an SVG representation are iteratively optimized so that the resulting glyph visually integrates the target idea without compromising legibility.
Optimization Mechanism. To achieve smooth, context-aware deformations, the system begins by creating glyph image Tsem from trainable parameters 0 using DifVVG The character segment is then cropped and augmented into batches Xaug- Coupled with semantic concept S, these batches are fed into vision-language model to calculate the training pyss-paticcllrt)) the SDS loss in the latent space code z. This iterative procedure refines 0, producing a final glyph design that balances aesthetic appeal with textual clarity.
Glyph Style Selection. Finally, the glyph style determined by context-derived cues, enabling both aesthetic and functional considerations. Formally:
(s pe( ds (stem ,M),
if formal context,
G = ((set pely MM) =
if traditional context,
(8)
if creative context.
Where G is the final glyph, and pely represents glyph-specific hyperparameters. By choosing the appropriate rendering technique and iteratively refining vector shapes, the Glyph Designer produces glyphs that are visually appealing while fulfilling functional needs.
General
Realistic
SCI-FI
Cartoon
Anything
Realistic object
Future SCI
European
Pisel style
cyberpunk
Painting
Simple
Realistic Scene
Van Gogh
peometry pover pereannees..
fractal geometry
Chinese Style
Ol painting children
Landscape Painting
Freehand painting
Figure 3: A hierarchical model tree with multiple sub-categories for fine-grained ToT model selection.
3.3 TEXTURE DESIGNER AGENT
The Texture Designer enriches glyphs by integrating controllable image synthesis with Tree- of of-Thought (ToT) strategy, ensuring both creative diversity and alignment with user preferences. Formally, can be represented as follows:
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1Ax 10x0 (Layy F F. TexR [gly. 8 C, ToTSel( F F,
(9)
where Lex is the final textured output, TexR denotes the controllable synthesis mechanism, ToTSel encapsulates the ToT-based model selection function, ggly the input glyph image, sexx is the texture prompt, C' represents control conditions (e.g., edge maps, depth), J guides the ToT process, and M the model library. The remainder of this subsection provides high-level overview; additional details can be found in the supplementary materials.
Controllable Synthesis. Building on ControlNet, the Texture Designer manipulates stylization parameters such edges, depth, scribbles to realize varied and adaptive texture styles:
Txx TexR(1aht s0x C, W).
(10)
where W denotes the selected model weights. By tweaking these parameters, the system can fine-tune visual details, catering to a range of aesthetic or thematic requirements.
Free-of-Thought Selection. To ensure artistic originality and coherence with user preferences, the Texture Designer adopts a Tree-of-Thought approach (see Figure B). First, the prompt giex is decomposed into conceptual pathways {21, 22, 2n under the guidance of F(s F(stx).. Each pathway's suitability is then evaluated by a heuristic V(z;,, leading to a search for the model M that maximizes the aggregate score:
Mbest
arg Max
Σ (2: MM) F=1
(11)
MEMM
This approach identifies the model that best matches each conceptual path, thereby enhancing the creative alignment between the chosen style and the user's vision.
Model Library Integration. Within MetaDesigner, curated library of 68 LoRA models (see Figure [T] spans categories like "Reaistii "SCI-FI," "Art, "Design," and "Cartoon." To maximize flexibility, these models can be fused through weighted combinations:
Wiusion Σ ai Wi,
(12)
where a denotes the blending coefficient for each LoRA model W,. By adapting the Tree-of- Tree-of-Thougee Thought output to this model library, the Texture Designer deftly transforms plain glyph images into textural "masterpieces," balancing user-defined constraints with artistic flair.
3.4 Q&& EVALUATION AGENT
MetaDesigner incorporates feedback mechanism designed to fine-tune hyperparameters based on four core criteria: -ik--to-mmage consistency, image quality, glyph feedback, and user preference. The system employs the LLaVA model to quantitatively assess consistency and overall image quality, while user studies contribute qualitative feedback via a Q&A format.
Feedback Metrics. For each newly synthesized image, GPT-4 generates evaluation prompts that encapsulate the project's goals (e.g., artistic style, thematic adherence). ILaVV analyzes these prompts against the generated images and outputs metric set:
Gm = {
cos
qua L. }:
where ttth denotes text-to- text-to-image consistency, gi represents image quality, and L. is a loss-like term used in optimizing system parameters. GPT-4 then summarizes these findings, producing coherent feedback and suggesting rationales for potential hyperparameter updates.

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User Feedback. In parallel, MetaDesigner gathers optional user responses through GPT-4-initiated Q&A sessions, capturing preferences (gtt)) and perceptions of glyph style (oa*), Formally:
G2 {9u cas
Pu
qua
Fe
pref )-
Although not mandatory, these user evaluations provide crucial insight into subjective factors like aesthetic appeal or contextual appropriateness. MetaDesigner integrates all feedback via
G = Merge (m,; Gu);
giving precedence to user input if any conflicts arise.
Optimization Strategy. Drawing on both model-based and user-derived inputs, MetaDesigner frames the overall optimization objective as L L.n + Lu. Specifically,
L.
argmax (s I).
(13)
(PPy (pia)
where goall is the evaluative prompt and H the LLaVA-based heuristic measuring how well the generated image i satisfies the evaluation criteria. Accordingly, the system adjusts glyph design parameters pely and texture design parameters pie maximize this composite objective.
Iterative Hyperparameter Tuning. As illustrated in Algorithm I and Figure 4 the system dy- namically refines its entire hyperparameter set P {Ppip, pely, piex Puu) by applying the feedback function P F(G (pddat)). Generally, user preferences and glyph-specife cues influence pely and pipeline adjustments, while text-to-image alignment and quality assessments inform pis, This closed-loop process ensures continual convergence toward outcomes that balance objective visual performance with the subjective nuances of user-defined style.
User Pronitt: "Comente "World Fowed Werdlet
Promett Orgineerng
Onggi Parteiter
Peramate Update
Peace Wortiess with cartoon
Ootann Parmiden Heastlype: "Serarri"
Chyph Style: Inturesting chiddee
qusiles Puurentrr
dov""
Updrts Frange: "Geeacee image
Origh
Seeeth and
kove
Quality Booword withtwws kranch and, lypting
fvetbee::
joone dovel
Figure 4: Feedback Loop for Hyperparameter Tuning: An overview of how user preferences, glyph quality, text-to-image consistency, and image quality assessments iteratively guide hyperparameter updates.
EXPERIMENTS
To evaluate the effectiveness of MetaDesigner, we curated 150 prompts spanning five themes-cartoon design, reality, sci-fi, and traditional culure--nccopassing artistic styles and cultural motifs. From these, we selected 20 prompts (in English, Chinese, Japanese, and Korean) for user studies to investigate the framework's multilingual performance.
wide range of
4.1 COMPARISON WITH STATE-OF-THE-ART METHODS
We compared MetaDesigner against several contemporary state-of-the-e art (SOTA) models, including Stable Diffusion XL (SD-XL) Podell al. (023), TextDiffuser Frans al. (2022), TextDiffuser- 2 CChen et al. (2023c), Anytext Tuo et al. (2023). and DALL-E. 3. The models were chosen to represent a spectrum approaches in text-to-image synthesis. Representative results are illustrated in Figure and evaluated the following criteria:
WordArt Synthesis Success. Figure shows that SD-XL often fails to accurately depict text, per- forming inconsistently even in English. Both versions of TextDiffuser produce acceptable English WordArt but encounter difficulties with Chinese, Korean, and Japanese. Anytext improves perfor- mance English yet struggles with Korean and Japanese, and DALL-E 3 is similarly restricted to English. By contrast, MetaDesigner adeptly handles all tested languages, demonstrating robust, high-quality WordArt generation. (See supplementary materials for additional examples.)
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lh WEl & "ES
"World Peace"
"World Chinese
"World Peace" Korcan Japanese
HARLEACCE PEACE PEACCE
XL
World Peace
Word Peace
TextDiffuser
Only
Del Engiss
Supeers
Ony
#
+
WORLD PEACE
WORLD PEACE
BEtH
Poocs Dove
TextDiffuser-2
#
W oorld
World Preas DF
Anytost
Peace
WIBACEE
DALLE
or Pexice pecce
Ours
Figure 5: WordArt Synthesis Comparison: Columns I and 2 illustrate "World Peace" in English, columns 3 and 4 present the Chinese rendition, and columns 5 and 6 show the Korean and Japanese versions, respectively. The leftmost column corresponds to the baseline prompt ("Create stylish word 'World Peace" representing its meaning"), "", while subsequent columns include additional keywords such as "Sun, Peace Dove, leaves, cloud."
Quality and Diversity. Owing to its reliance on the Stable Diffusion 1.5 backbone and limited training data, the TextDiffuser family tends to yield visually similar results across prompts. While Anytext exhibits greater stylistic variety, it occasionally sacrifices image fidelity. DALL-E achieves higher- quality outputs but tends to default to cinematic 3D aesthetic. As shown in Figure MetaDDsignnr excels in both quality and stylistic breadth, consistently producing diverse outputs ranging from realistic cartoon 3h-we preserving overall visual quality.
Creativity and Semantic Alignment. Although SD-XL produces visually appealing text, it often strays from the prompt's meaning. The TextDiffuser series includes thematic elements (e.g., leaves and peace dove for "World Peace") but lacks cultural or linguistic adaptability. Anytext sometimes introduces unrelated logos that miss the prompt's essence, and DALL-E while visually refined, can be thematically ambiguous. In contrast, MetaDesigner consistently generates creative, semantically coherent WordArt (e.g., adding dove and foliage for "World Peace"), demonstrating a clear grasp of both context and theme (see Figure 5 and Figure
Quantitative Analysis. Because OCR methods often struggle with stylized text, we conducted a user study to evaluate MetaDesigner alongside competing approaches on two dimensions: Text Accuracy and Aesthetics Creativity. Eleven participants provided feedback, ensuring varied perspectives. As seen in Table [ MetaDesigner clearly outperforms other methods in both criteria. striking strong balance between textual fidelity and visual appeal. We further assessed each method using SSIM (Structural Similarity) and LPIPS (Learned Perceptual Image Patch Similarity). Table 2 shows MetaDesigner consistently achieving superior results on both metrics, underscoring its capacity to produce readable, visually coherent WordArt.
Letter-Level Comparison. We further evaluated MetaDesigner at a more granular level by comparing its single-letter WordArt outputs against Google search results, Stable Diffusion Rombach al.
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Table I: User Study Results. Higher scores are better; denotes MetaDexigner without feedback.
Dimension
SDXL
SDXL-Aug
TextDiff
TextDiff-2
Anytext
DALLE
Ours'
Ours
Text Accuracy
44.1
42.7
82.3
33.2
88.2
96.8
Aesthetics
0.5
5,9
0.9
0.5
0.9
10.9
5.0
75.4
Creativity
0.9
1.8
0.5
0.9
0.9
7,3
10.5
77.2
Table 2: Quantitative Comparison. We measure SSIM and LPPPS on ground-truths from Promeai (P) and design websites (D), Bold and underlined are best and second-hest. denotes MetaDesigner w/o feedback.
Metric
Text
SDXL
SDXL.-Aug.
TDiff-2
Anytext
DALLE3
Ours
Eng.
0.1254
0.1381
0.1860
0.1641
0.1324
0.0834
0.1730
0.2397
SSIM
CJK
0,1853
0.2092
0.1747
0.2037
0.1021
0.1401
0.2269
0.2643
P
All
0.1553
0.1736
0.1803
0.1839
0.1172
0.1117
0,2000
0.2520
Eng.
0.7491
0.7652
0.7453
0.7633
0.6910
LPTPS.
CJK
0,7712
0.7307
0.7970
0.7687
0.7601
0.7693
0 0.6937
0.6846
0,7602
0.7496
0.7811
0.7564
0.7527
0.7673
0.6949
0.6878
Eng. 0.1802
0.2439
0.2342
0.2036
0. 0.1669
0.1413
0.1913
0.3119
SSIMT
CJK
0.2093
0.1846
0.1987
0.1073
0.1542
0.2539
All
0.1877
0.2266
0.2094
0,2012
0.1371
0.1478
0.2048
0.2829
Eng. 0.7995
0.8157
0,8312
0.8306
0.7650
0.8 181
0.7950
LPIPS
CJK 0.8025
0. 0.7964
0.8249
0.8429
0.8437
0,7872
0.7912
0.7880
AlI 0.8008
0.8060
0.8280
0.8397
0.8466
0.7761
0.8040
0.7915
(202), DALL-E 3, and DS- DS-Fusion Tanveer et al. (023). As shown in Figure & Stable Diffusion struggles with coherent letter shapes, while DS-Fusion produces cleaner forms but offers limited style variation. DALL-E 3 demonstrates high textural quality yet does not always capture thematic nuances. In contrast, MetaDesigner consistently merges artistic creativity with precise detailing, achieving visually appealing, thematically aligned letters. More examples are in the supplementary material.
4.2 EFFECT OF TREB-OP-THOUGHT
Belavanoe
Quality
Stple
To verify the effectiveness of the Tree-of- Thought (ToT) scheme, we conducted quan- titative analysis comparing the ControlNet and approaches. GPT-4, state-of- sfate-of-the-art language model, used to measure the "Relevance," "Quality," and "Style" of the synthesized WordArt. The results, illus- trated Figure [6] show significant improve- ments achieved by the ToT-LLRA in all metrics, highlighting its ability to generate WordArt that highly relevant the given prompts, visually appealing, and stylistically diverse.
Eeiign
Tata
Deiigs
the
Tradhiime
Trafiieed
Sartty
ControlNet
CoT-iomini
TOT-GPT4
Figure 6: The evaluation of the synthesis WordArt. From left right the "Relevance", "Quality". and "Style" scores generated by ChatGPT-4 the sub- categories. (The performance of ToT+Gemini and ToT+GPT4 are very closc)
case study, presented in Figure demon- strates that the ToT-LoRA scheme significantly outperforms ControlNet in WordArt style and text-to-image consistency. Specifically, ToT- LoRA+ControlNet excels in rendering com- plex textures and maintaining coherence with the given prompts, such as "ethnic customs, New Year, dumplings, steamed bread, kitchen, mother, little boy" and "Renaissance style, Cas- tle." " The results show clearer thematic representation and enhanced visual appeal, further validating the effectiveness of our approach. More examples are the supplementary material.
ToTHooRlvContraNeet
"uthnic customs, dumplings, steamed bread, Michen, mother, little
World Peace
"Renaissance Costle"
Figure WordArt Texture rendering.
4.3 EFFECT OF OPTIMIZATION
To illustrate the impact of the optimization process, we conducted a detailed case study, presented in Figure IO The LLaVA system is employed identify objects mentioned in the prompt but absent in the generated WordArt. This information is then used to update the generation process, incorporating the omitted elements. Techniques such symbol enhancement, word sequencing adjustments, and

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Dragon
PLANT
Dragon
PLANT
Stable Diffusion
S
N
N
N
DS-fusion
R
R
NNN
R
N
N
Figure 8: Single-Letter WordArt Comparison: We compare Google, Stable Diffusion, DALL-E 3, DS- Fusion, and MetaDesigner. The prompts here "Dragon" (left) and "Plant" (right). Partial results from
DS-Fusion Tanveer 2023) are reproduced for reference.
Hete
World
Designer
ALNOWAY
ANYWAY
SE X 0ス0
J
Figure 9: Examples showcasing multi-langun multi-language support (Chinese, English, Japanese, Korcan, Arabic numerals, etc.) and varied word counts, with applications ranging from e-commerce to personalized social media.
keyword repetition employed to augment the WordArt generation process, ensuring that the final output accurately reflects the input prompt. As shown in Figure 10, the optimization process proceeds through several steps to achieve better alignment with the prompts. In Step the initial generation may miss key elements such as "little girl" in the prompt "old man, cake, candles, little girl." In Step 2. iterative refinement introduces missing elements, enhancing text-to-image consistency. Similarly, for the prompt "kitchen, girl, steamed bread, a plate of fruit," the optimization process adds missing objects and refines their depiction over multiple steps. This optimization process plays crucial role in enhancing the semantic consistency and visual quality of the generated WordArt.
Step-1] old man cake, Candles attle gli
2lid man, cake,
DRop 3] man, little gitls, cake, Candles Listl girl
[Dass 1]: Kitchhnn gir, steamed [Btpp2]:: Kitchen, girl steamed get
braad, A place
World
World Peads
Pooce
Figure 10: The examples are the optimization of the text-to-image consistency.
CONCLUSION
We introduced MetaDesigner, LLM--driven, multi-agent framework that streamlines user-centric artistic typography synthesis. Its three core agents-Pipelliee, Glyph, and Texture--oolabbraiivvly translate user preferences into visually compelling, context-aware WordArt. By integrating generative AI with typographic rendering, MetaDesigner enables both professionals and hobbyists to efficiently produce high-quality, customizable, and aesthetically diverse designs. Future work includes expand- ing the WordArt Dataset, extending language coverage, refining the multi-agent system, and exploring broader applications in design, branding, digital media, and visual communication.
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A APPENDIX: ADDITIONAL DETAILS
This appendix provides further information that could not be included in the main paper. The sections organized as follows:
1. ToT Model Selection (appendix B
(a) Top Model Selection Prompt (appendix B.1
(b) ToT Model Selection Case Study (appendix B.2
2. Image Evaluation (appendix C
(a) ToT-LoRA Evaluation Prompl (appendix C.I
(b) LLaVA Evaluation Prompt (appendix C.2
3. Dataset Details (appendix D)
4. Additional Comparisons with SOTA Methods (fig. 17
5. Mixing vs. Separating the Glyph and Texture Designers (fig.
18
6. Effect of GPT-4/GPT-4V on ToT Selection (appendix B
B MORE DETAILS OF ToT MODEL SELECTION
B. TOT MODEL SELECTION TEMPLATE
You possess knowledge of various cultural backgrounds and artistic styles. For each prompt, identify and analyze its vocabulary, theme, content, implied culture, and potential reader perception. From this unalysis, choose the most appropriate element from the given inpul list. Document the reasoning steps, then determine a final consensus best-guess match. The final output must adhere to the following template:
TEMPLATE:
Selected: [the selected word] Input list: (search_list)
Input prompt: (input)
B.2 TOT MODEL SELECTION CASE
Input Prompt: A girl, a boy, in church
Thougghs-Ist (Keywords: "General", "Traditional Art", "Design", "Cartoon", "SCI-FI"' "Realistic")
VVoaabular: The prompt uses common words (girl, boy, church) that do not point a specific art style or culture.
Theme: Featuring girl and a boy in church, suggesting spiritual or religious setting often associated with traditional art.
Content: Minimal details; however, the church context is frequently linked to traditional or classical depictions.
Implicit Culture: Likely Western or Christian influence, common in traditional art.
Reader Perception: A straightforward scene that leans toward traditional aesthetics in many cultural contexts.
Summarization: "Traditional Art" best fits this simple yet possibly religious scene.
Selected: Traditional Art
Thoughhs-2nd (Keywords: "European Style", "Chinese Style"))
Vooabularr: The prompt mentions girl, boy, and church, but does specify distinet culture or art form.
Theme: Two individuals in religious setting, with no direct mention of cultural influence.
Content: Minimal context, though church often implies Western European setting.
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General
General
Anything
ReVAnimated
EveryOne
Art
European
Painting
Oil Painting
Van Gogh
Monet
RealisticOO/Paintiig
gold
Chinese
Wash painting
ShuiMo
Mural
Chinesepaainting
CuteinkPainting
Comics
XIADRENSHU
Chinese jillsttations
Chinese_illutrationn
InkComicStrip
piper
Paper cutting
BillaminatedPage daperCut
Design
Logo
LOGO
Linearicon
Financelcon
Poster
Old school
MinimalistPoster
TextCover
Cover
Cartoon cover
RedEnvelope cover
Indoor design
Indoor
InteriorDesign
Style
design
Cute
IP P_Animal
Cartoon
Pixel style
Pixel
PixelArt
SimplePixe!
Simple style
Simple drawing
SimpleFashion
painting
FarkFairyTalee
Line style
LineArt
Comictine
Colorful style
ExplodeEffect
DreamyWatercolor
ClassicalDazzle
BlindBox
Cute3D
SCI-FI
Cyberpunk
Cyberspace
CyberGirl
CyberCat
CyberCity
Transportation
Ship
Futuristic Cities
Advanced Cities
Wasteland
Underwater City
FantacyWorld
Science Object
Mecha
Alien
Robots
Bionic Robot
Realistic
Realistiic Scone
School
Park
Street
RealisticUrbanMli
Realistic Object
ChilloutMix
majicmiiiRellistic
KryptonRRallsticc
MoonFilm
Figure 11: The complete hierarchical model tree, featuring multi-level subcategories in-depth ToT model selection and enhanced stylistic diversity.
Implicit Culture:
Likely European, given the mention of a church.
Reader Perception: Many viewers may perceive Western influence, though this remains subjective.
Summarization: "European Style" appears most compatible with the church setting.
Selected: European style
Thoughts-3rd (Keywords: "Painting", "Engrave")
Vocabulary: Plain language referencing girl, boy. and church, making the setting easily visualized.
Theme: Suggests peaceful spiritual scenario with no explicit action, often depicted in paintings.
Content: Minimalistic, focusing on two characters within a church.
Implicit Culture: The mention of church indicates likely Christian or Western environment.
Reader Perception: Evokes serenity or spirituality; could be rendered effectively as painting. Summarization: "Painting" suits the subtle details of the scene better than "Engrave."
Selected: Painting
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Algorithm I Hyperparameter Tuning
1: Input: Prompt s " initial hyperparameters P, max iteration threshold score threshold 6, model library
M. MetaDesigner V/:
2: Output: WordArt image f:
3: while i < and L < O do
4: I = (ss &.P.
Eq.
5:
Gm H(ssail) 1)
G. You 9h 1 9u Pu )
User Feedback
7:
G = Mergo(Gm. Gu)
F(G (aplare)
9:
L Lm + Lui i+1
10: end while
11: return i, {pmi, per plo):
Prompt: man, vase;
Text Content: Word;
Base model: AnythingVS(Prt-RE
LoRA Model: Chinese, bronzes, LORA, Sketch_Style Style_ LORA
Word
Word
Word
Word
Wor
Word
(0.0) x.a
[0.2,
004,0000
(0.6, 0.4)
(o.s, 0.2)
(1.0,00 0.0)
Werd
Word
Word
Word
Word
0..1
(0.3.0.7)
(0.5, 0.5)
10.7, 0.3)
[0.9,0.1]
0.0
Chinese_brre bropobrrrressseeeeee
1.0
Wketch_Style LoRA
0.0
Prompt: girl, vase;
Content: Word;
model: AnythinggV((Pr-REE]
LoRA,
Style
Word
World
Word
Word
MOLO
(0.2, 0.s)
(0.4, 0.6)
10.5, 0.4)
(0.8, 0.2)
(1.0 0.0)
Word
Word
Word
JRO
00 Comic 0.91
(0.3,
(0.5,
(0.7,
[0.5,
10
Croissant Style LoRA 0.0
Figure 12: Effect of merging LoRAs with different weights. The top example fuses Chinese Bronzes and Sketch Style LoRAs; the bottom fuses Comic and Croissant Style LoRAs.
Thoughts-4th (Keywords: "Oil Painting", "Van Gogh", "Monet", "RealisticOilPaintnnn""
Vocabulary: Straightforward scene without explicit ties to any single artist or style.
Theme: A possibly everyday spiritual moment suitable for an oil painting approach.
Content: Two figures in a church-a scene often depicted in realist classical oil painting tradition.
-mppicii Culture: Western context, aligning well with artists like Van Gogh or Monet, although the prompt is not artist-specific.
Could be viewed as an ordinary, contemplative moment in a church, commonly illustrated in oil paintings.
Reader Perception:
Summarization: "Oil Painting" is broad enough to encompass the scene without restricting it to specific artist or ultra-realister style.
Selected: Oil Painting
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ControlNet
ToT CoTiieiiioo
ControlNet
ToT:LoBA&ControNee
"ethnic customs, New Year, dumplings steamed bread, kttchen, mother, little
Wordd
Worle
"Sculpt represemation Dynasty bronse artifacts'
World
Peace
"Chinese traditional style, lovely little
World Peace
aoorld
sWiorld
"Renalssance style, Castle
polapenimmon
Peace
"no huwans,,eetallennn seepsimple background,flwwee flower,trai tional media, white
World Peace
World
World
shape
background.brandce
anchoc, compass, nautical cap"
Pea
Peace
Figure 13: Comparison of WordArt texture rendering on the glyph "World Peace" (ControlNet vs. ToT-GPTT4LORA-Commonn
Relevance
Quality
Style
Gemini
SO
SO4
Son
Color:
Calon
Calon
DARDI
Claude
Caroet
Tooal
Qwen
Figure 14: radar-chart comparison of WordArt rendering on the glyph "World Peace" (ControlNet
vs. TOTTGGTTA-LORA-CCCtroNNne,,
C MORE DETAILS OF IMAGE EVALUATION
C.I TOT-LORA EVALUATION PROMPT
You scoring judge for generative model. I will show you images and prompts produced by the model. Please assess how well each image's 's content aligns with the corresponding prompt and rate its overall image quality scale of 1-10. Provide three numerical scores: (1) Prompt
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Relevance, (2) Image Quality, and (3) Style-Prompt Match. Return only these numbers without any further explanations.
C.2 LLAVA EVALUATION PROMPT
Given an input sentence, break it down into real-world, visually identifiable "Targets." " Use the template below to list these targets:
Ta rrgett::on (target, target, ...) <Input sentence>: (input)
Subsequently, for each listed target, query LLaVA to check whether it appears in the image by asking: "Is ( (target) present in the photo? Please answer Yes No."
D MORE DETAILS OF WORDART DATASET
To thoroughly evaluate the MetaDesigner framework, assembled 150 prompts spanning five themes-carree design, reality, sci-fi, and traditional culture. These themes encompass a broad array of artistic styles and thematic elements, challenging MetaDesigner to produce aesthetically and contextually diverse WordArt.
Linguistic Diversity and Selection Criteria. From this larger set, selected 20 prompts for user study, specifically chosen to represent English, Chinese, Japanese, and Korean. This subset was curated to ensure linguistic variety, thematic breadth, and real-world applicability (e.g., e-commerce, education, and digital content creation).
Statistics and Analysis. Each of the five themes contains 30 prompts, ensuring balanced coverage of different styles and subject matter. We considered cultural relevance, popularity in digital art, and commercial or educational applicability when selecting these prompts. Figure 9 highlights diverse use cases such as cartoon text, Chinese sumames, and solar terms, illustrating the dataset's capacity accommodate varied artistic and linguistic expressions.
Applicability and Use Cases. The dataset demonstrates utility for numerous domains. In e-commerce, for instance, customized WordArt can enhance user engagement; educational contexts, multilingual text designs can enrich language learning and cultural instruction. By testing MetaDesigner in these settings, we validate both its ability to generate contextually and culturally relevant WordArt and the dataset's suitability for studying the intersection of AI, language, and art.
WordArt Dataset Analysis. Figure 15 illustrates numerical data from our "Image Plaza," where over two million images have been generated. These images feature range of aspect ratios, word counts, and thematic content, and are retrievable in our WordArt space
preference analytics, revealing that over million images have been produced by users. The majority favor 16:9 aspect ratio, typically using up to five characters text. Moreover, alpha-channel images especially popular.
Figure 16 depicts
Table 3: VLM evaluation in two dimensions: Aesthetics and Creativity, scored from 0-10 (higher is better). Ours" indicates a version without agents.
SDXL-Auu
Anylext
DALLE
Ours'
Aesthetics
79
8.1
6.1
6.4
7.0
8.5
8.7
Creativity
75
7.7
5.8
6.0
6.1
7.6
7.7
7,9
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Number of Images
Aspect Ratio
2500
20
2000
1500
Total: 2,226,285
1000
178.29
Textt+Texture Texture
163
9:16
Number of Characters
Text Topic
00
1000
1000
203.55AA88.988
136:35
103.68
94.83
138.952
100
30.285
5.345
10
7.69
2.03
Sumaness
Celebiity
Iilom
Tourns Attration
Emotie Vocalbulury
Constellation
Chinese Zediac
0.1
0,027
Figure 15: Numerical analyses of our WordArt dataset. The horizontal axis denotes the number of images (in thousands), while the vertical axis represents category labels.
Number of Images
Aspect Ratio
1400
1000
800
50h
Totak: 149007766
400
200.58
Glxph+Texture Texture
Glyph
Texture
16:9
9:16
1:1
Number of Characters
Alpha Channel Image
1000
1000
533.77 526
44.79
100
6436
28.36
30,00
10
22/00
10
3
5
>=10
Yes
No
Figure 16: User preference analytics. The horizontal axis shows the number of images (in thousands), and the vertical axis displays specific categories. "0" in the "Number of Characters" panel indicates of mask image rather than textual input.
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"Desert"
"Sky"
"S
"Iron" Chinese "Spring" in Chinese "utumn"' in Chinese "Jewelry" Chinese
SDXL
1 a
SDXL Augmented
Desert
Sky
TextDiffuser
Qument Only
Guncet Verson Only
Curree Version Only
Cumer Verson Only Supports Englan
Dupports Eaghe
Sky
*
Desera
*
TextDiffuser-2
Sky Ccibuss
Anytext
Desert
ララ
S
SIS
DESECT
Ours
Desent
(w/o agents)
Desert Sky
Figure 17: Additional examples comparing different methods.
"World Peace"
"Fruits"
#t 3o 50.12" Chinree
Mix Glyph Texture
World Peace
world Peaie
Separate Glyph & Texture
Fraits
Figure 18: Visual comparison of merging vs. separating the Glyph and Texture Designers.
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LLavA response
GPT-4V response
Consistency: : "scor"::: 8, "reason": "The image effecively incor porater seanl elements from the prompe induding flowrs and leaves, and shawcases senie of warmth and stallty.
Consistency "Vcare" "rease"": "The image cobbin the requined elenerts ruch flowen, young leww, sunight warmth surves, petal patterm, gass toxtare, ripples, warm lones, viailty, elements properly poitiooed contribunee
Howceet
representaion
suntight and contral regresertation gross"
"sassor" "the
aeshetic sligns with the flora creating
featrees
articlc mtyle The cuelight wornth elements and the oveaal aomoughere, creating sense of vitiiity and life. The use of curves and petal pattoms adds visal interest and contribbb to the artiste insowston."
stmasghere. The
minimalist background oehances the focus the fora elements contrbbttng to the intonded style.
Quality: "score': 9: "resson" "The image exhiits high darily with conddeeedd compostion. The lightnng affectivell highilatts the textures colors the
Quality: "score" 9 "reasson" "Theimage is clear and uelicomppoedd with good lighting eflects othance oveull appeal. The tochnécal leaes, tosture which
adding to photograph.
exeuned"
Creathvity. "score" 8 "reason": urique artislic interprecation, using elemems
Creatvily:
High antisic Immouation through the elements, such sunlghts,
lagenuity
warmth, and testure. The combination these eloments creates urique and viuually aspcaling composttion that ttands out from more conventional foorl Images"
The insovatian presentation commendable, though remaiss within cstablisedd creative houndaries.
Consistancy: scoee:: 8 "reasn"" i: "The image contains all the resuied clenenss such as flowers, young leave, surlight warmth curves, petalputterns gao tectume, rigoles, sam tones, and vhallyy The elements are well pouitioncd and contribune to the overal compostionn the image""
Consistency: "score" 9: "reason": "The image voricty flwees and young lowws
arraged way that visuilly
interretable. elements from prompt, auchas
wellimtgrated creating compaetion."
Style "score" 9: "reason" "The matches the requested artsti styl, color tore, and atroophere. The warn tones, sunlight, and warmth awate a sence diality and whil curees pabei patterns add artieic tauch. The grss teature and ripbles contribute the ovenal vasaa Interset imago."
Style:
The "reason"
matches
atmosphere feels vibrant and lively, algning well with the
warthh vitility although the artbiic style may not fully resonate with tradttonal fioea represenbation"
Quality "score" 9, "reasee") "The image os high qualis, with claar compoctionn lighting aftects are eeneaeedddddd the tocheical sspects Creathvitt "score": 8) "reason") "Theimage quie creative with unique combinatoon of dlmments creats visually internttnng and engaging composition. warn tonos sunlight, while
composdd
Qualitty "score 9; "reasnn" "the image dear, with sharp detals that highlghht the soctores of the flowen and leave. The lighting soft and onhances the ovent woreth of the composttion, contributing to visually apocaling and tochnically cecollene. photo."
image are excollent") "I
Creathvity: "ccoee" 9: "reason"" "The
imagh,
arnnn emangement of the floral etements in croative and
artistic way demonstrates a high level innovation. The use ofpeaals and loowes to form an artistic rpprocrtations shows uniqueness and strong cruatr visen, settingit it spart from bypical Horal inagery."
ardsti
Figure 19: Differences between LLaVA- and GPT-4V-based evaluations.
20

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Published as a conference paper at ICLR 2025
User
want peace WordArt with text "
Glyph Designer
Prompt:
Pipeline Designer
Glyph Prompt: teapot, tea, ripple, willow, water drop.
Texture Designer
Texture Prompt :spring, green tea, mist, ripples, young leaves teapot, tea, ripple, willow, water dpop
ToT LoRA Selection
Vocabulary: prompt indicatss possible combination of
portraying
blooaming green
the slate road, the mist, ripples, young naves, teapot, tea, willow, and water drop. There isalso hints of some abstract co ncapts that explored through
ripples technique cal peaceful atmosphere typically associated with traditional tea ceremony Theme: In terms theme, the
scones. This sugrests leaning towards symboliam related to tranquility, tradition, and nature.
Content:
prompt sugrest fresh, outdoorsy, towards specific color palette that can be found within nature-orienas ententedd artwork.
gr cen and misty
Implicit Culture: The elements mentioned in the prompt are assodated with Asian or Eastem cult ures, espacially due to the
emphasis ceremony symbolism.
Reader Perception: Given that the being painted invites thorough exploration of different textures like the softness of the tea leaves, the hardness str erength of the slate road, the delicate nature of the mist, etc., it clearly emphasizes the use of realistic randering techniques.
Summarization: Considering ing all the above analysis,, the prominent el dements from the prompt appear to be 'traditional culture' and 'realistic. The choices.
scone, implied cuttural context, necessity for realistic texture render ing contribu
Selected: realistic
cabaularr:: The promptcontains vocabulary Indicative of re allstic scene. Words Like 'spring,, 'gree tea', 'slate roa 'miet' 'ripples', younglesvess' and water drop' contributesto setting particular atmosphere that portrays concrete mo ment in time, much kknn reaßstic ene.
Theme: e: The theme here is serone springt ime moment expe rienced white havingtea. Green tea, willows, and young leaves beginnings, Content: color plette inferred
sym
tranquility presented
scene.
refreshing, soothing maaeggmoddd mood; spring greens, grays, mist w witt or saft blues, and the warmth of tea. The Mood peaceful and calm ofte associated with rea listic
prompt portrays aye
Implicit Cutture: The cuttural context is dra from coomtes-tenn sea-drinkingeee kiikiinngggueeesse where
reflec East Asian
tra like Chinese or Japanese, which pro for doing it natural eneries, aligning it more wawrd realisti
The artistic techniques the through the obje e.g. b ommin tea, road with mist, mist,youn young leaves, water drops, are mo neture-centered, to ther cre creating coherent, relatable, and rea realistic scene.
Reader Perception:
Summarization: summation the described scenario in the inp mo ment in nature note the spring blo,mm, the i the sla ro the tea, hinting to wowarss aeisticaceee ra ther than for gon rallstticabject.
be painting picture partic
Selected: realisticscene
Evaluation
Consistency "score": 8, "reason": "The image contains teapot, tea, ripple, willow, water drop, and young leaves, which are all elements of the prompt. The arrangement of these elements is visually appealing and well-organized,""
Style: "score": 9, "reason": "The image has green tea theme, which is consistent with the prompt. The color tone is predominantly green, and the atmosphere calm and serene, which ali gns with the requested artistic style."
Quality: "score": 9, "reason": "The image of high quality, with clear details and a well- composed rrrnggeeent of the elements. The lighting effects are subtle and effective, enhanding the overall appeal of the image."
Creativity: "score": 8, "reason": "The image is creative in its presentation of the elements, with the apot and tea leaves in visually interesting manner. The use of ripples, willow, and wat er drop adds touch of artistic flair to the composition."
Figure 20: Overview of the full MetaDesigner pipeline.
21

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Published as a conference paper at ICLR 2025
Enter the creative to be genersted
Typography Output
Pease
Characters Entry
Input creative effectakllick the Al magic wand generate
WESEEE CFFF,
REE
Design Instructions
Creative Assistance
Change the shape words
Select the fo to usekčlick the up ood the font
Selection
specifications.
Aspect
Variant Selection
10a
9:6
Ratio
Figure 21: User interface of the MetaDesiggne system.
Formal
Traditional
Elegant
Classic
#
Figure 22: Impact of font type on glyph transformations.
tean
ROW'ss
YON'S
Figure 23: Further examples generated by the Glyph Designer.
22

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Published as a conference paper at ICLR 2025
Table Comparison of ToT-Selection using different models.
Evaluation Theme
GPT-
GPT-4V
traditional culture oil painting (x1)
design >> poster (x3)
general -> general (x2)
World Peace
traditional culture >> oil painting (x2)
realistic >> realisticscene (x1)
traditional culture -> papercut (xl)
sci-fi future world (x1)
sci-fi >> future world (x2)
traditional culture >> oil painting (x1)
Colorful World
traditional culture -> oil painting (x1)
traditional culture -> papercut (x1)
traditional culture -> inkpainting (x2)
realistic realisticscene (x1)
sci-fi cyber (x1)
traditional culture -> oil painting (x3)
design - poster (x2)
Artistic Style Fusion
traditional culture -> engrave (xl)
traditional culture >> inkpainting (x1)
design poster (x1)
traditional culture -> chinesccomic (x1)
Future Technology
sci-fi cyber (x5)
sci-fi cyber (x5)
cartoon -> 3dcartoon (x4)
cartoon -> 3dcartoon (x4)
Children's Fun
design -> poster (x1)
cartoon -> forchildren (x1)
EFFECT OF GPT-4/GPT-4V ON TOT-SELECTION
We evaluated the GPT-4 and GPT-4V models on 25 test prompts, divided into five thematic categories. Thble the ToT-Selection results. Compared to the more constrained Future Technology theme, World Peace and Colorful World allow broader interpretations. On these open-ended themes, GPT-4V produces more varied outcomes, indicating nuanced grasp of user intent. However, for the more focused Future Technology theme, GPT-4V tends to be more consistent than GPT-4. Details of the test prompts within each theme outlined below:
World Peace Theme
"watercolor dove, olive branch, rainbow blend, peace symbol"
"multicultural children, circle dance, traditional patterns, vibrant clothing"
"hands joining together, global unity, soft pastels, harmony symbols" "peace garden, blooming flowers, meditation space, tranquil atmosphere" "world flags blending, peaceful doves flying, sunrise hope, gentle clouds"
Colorful World Theme
"earth view, kaleidoscope, abstract continents, space art"
"butterfly swarm, gradient sky, cultural patterns, colorful wings"
"rainbow coral reef, tropical fish, underwater prism, vibrant marine life"
"northern lights aurora, starry night, color spectrum, celestial dance"
"spring festival lanterns, color explosion, festive celebration, dynamic lights"
Artistic Style Fusion Theme
"art nouveau, digital wireframe, modern fusion geometric patterns"
"ukiyo-e waves, pixel art, japanese digital, style blend"
"renaissance painting, neon aesthetics, classical meets cyberpunk" "baroque architecture, minimalist overlay, historical modern mix"
"tribal patterns, glitch art fusion, ancient digital blend"
Future Technology Theme
"cybernetic cityscape, holographic interface, neon circuits, quantum particles" "robotic augmentation, bioluminescent tech, neural networks visualization"
"quantum computing visualization, data crystals, energy flows"
"cyborg nature fusion, techno- ooganic growth, synthetic biology"
"artificial intelligence mindscape, digital consciousness, virtual reality portals"
Children's s Fun Theme
"playful teddy bears, rainbow playground, bubble floating magical toybox"
23

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Published as a conference paper at ICLR 2025
"cartoon dinosaurs, candy colored clouds, children's storybook, whimsical adventure"
"magical treehouse, flying paper planes, lights, childhood dreams"
"circus animals parade, balloon animals, cotton candy skies, cheerful carousel"
"fantasy schoolyard, animated crayons, floating books, imagination sparkles"
24


Reviews:
[{"review_id": "kqCuR3zWCV", "reviewer": "Reviewer_t5qA", "summary": "This paper introduces a novel framework for artistic typography synthesis that leverages Large Language Models (LLMs) to drive an interactive and user-centered design process. The system is built on a multi-agent architecture, including Pipeline, Glyph, and Texture agents, which enable the generation of highly customizable WordArt. \nThe authors introduce a comprehensive feedback mechanism that integrates insights from multimodal models and user evaluations to iteratively refine hyperparameters, ensuring that outputs align with specific stylistic and thematic preferences.", "questions": "**Glyph and Texture** Will separating Glyph and Texture limit the diversity of generated results? Could you provide additional ablation studies to demonstrate the necessity of decoupling Glyph and Texture?\n\n**User Interface** Can you provide more details about the user interface and how users interact with the system? How intuitive is the user experience?\n\n**Failure Case** It would be best to explain the existing artifacts in the current system and suggest potential areas for improvement in future work.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "**Innovative Framework** The use of a multi-agent system (Pipeline Designer,\nGlyph Designer, Texture Designer, and Q&A Evaluation Agent) and the integration of large model to drive the design process is a novel approach that addresses the challenges of creating high-quality WordArt.\n\n**User-Centric Design** The comprehensive feedback mechanism, which includes insights from multimodal models and user evaluations, ensures that the generated WordArt aligns closely with user preferences and expectations.\n\n**Multilingual Capabilitie** The system demonstrates robust performance across English, Chinese, Japanese, and Korean, making it versatile and suitable for a wide range of applications.\n\n**Empirical Validation** The paper provides detailed comparisons with state-of-the-art methods, showing that MetaDesigner outperforms existing techniques in terms of WordArt synthesis success, quality, diversity, and creativity.", "weaknesses": "**Complexity and Usability** The multi-agent system and the iterative optimization process may be complex for non-technical users, potentially limiting the system's accessibility.\n\n**Computational Resources** The paper does not provide a detailed analysis of the computational resources required to run MetaDesigner, which is important for practical deployment.", "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": "dcfPw7ERMT", "reviewer": "Reviewer_zqVY", "summary": "This paper introduces a multi-agent system capable of generating WordArt based on user instructions, with flexibility in style and texture. The system primarily consists of a Pipeline Designer, a Glyph Designer, and a Texture Designer, each responsible for overall process design, generating font images, and applying background and text textures, respectively. A feedback mechanism is integrated into the system, where prompt refinement is achieved through interactions between GPT and LLava or users, improving prompt to produce higher-quality images that better align with user expectations.", "questions": "I'd like to pose questions that can address the weaknesses discussed.\n1.  **More Detailed Examples of the Full Pipeline**: While Fig. 10 presents the final results, it would be beneficial to show the output at each step of the system, such as the glyph images generated by the “Glyph Designer Agent.” Subfigures (like the picture with dark green tones in the lower left corner) may be good choices for illustrating these intermediate steps.\n\n2.  **Details on the Construction of the Style LoRAs Library in the “Texture Designer”**: Additional information is expected regarding the number of trainable parameters, the volume of collected data, and a comparison of data size with previous WordArt synthesis works, among other relevant details.\n\n3. **Information about the inference efficiency**: A comparison of image generation time and computational resources required, relative to other models\n\n4. **Impact of Replacing with GPT4-V**: a) In both the “Texture Designer” and “Parameter Optimization” stages, image input is utilized. Would replacing the previous GPT-4 with GPT4-V be a more effective choice? Quantitative and sample-based analyses? b) What differences would arise if using GPT4-V instead of LLava in the “Feedback” stage?\n\n5. **Potential Common Problems in Generated Images**: a) Although the images in Fig. 5, Fig. 8, and Fig. 9 may possess artistic qualities, the text color often resembles the background, making it less distinguishable compared to results from other models.  b) In the lower-left of Fig. 8, style information, such as “Renaissance,” is generated as part of the text—what causes this, and is this phenomenon common?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The WordArt examples generated by the model in this paper demonstrate higher visual quality, more accurate text representation, and greater consistency in overall style compared to previous methods.\n\n2. The system proposed in this paper can generate WordArt in diverse styles, closely resembling designs created by human designers, owing to the use of a style LoRA library. Additionally, it demonstrates the effect of merging LoRAs with varying weights.\n\n3. The paper utilizes advanced prompt-LM techniques, such as Chain-of-Thought, Tree-of-Thought, and Feedback Refinement, to enhance the accuracy of sub-prompts for each agent, resulting in generated content that better aligns with user preferences.", "weaknesses": "1. **Lack of novelty**: The improvements in “Accuracy and Creativity” claimed in the paper primarily depend on the construction of the style LoRAs library in “Texture Designer”, which aligns with previous experience. The impact and novelty of other modules are also insufficiently highlighted. The paper adopts a style closer to a technical report.\n\n2. **Insufficient Experimental Design**: For example, (a) the user study includes only 11 participants, and it may benefit from a fair comparison using publicly available VLMs (e.g., GPT4-V); (b) the ablation study only analyzes the ToT module within the “Texture Designer,” without examining the analyses of other modules; (c) the comparison of generated results with other models is limited to 6 samples in Fig. 5, and more samples would be expected.", "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": "gjkSjXqRbI", "reviewer": "Reviewer_9F4p", "summary": "The paper studies the problem of generating stylistic text (with stylized glyph and texture) from input text prompts.\n\nThe core contribution is a multi-agent system that factors text generation into multiple sub-tasks, and addresses them with specialized LLM, LMM and stable diffusion agents, including: 1)  a pipeline designer for generating design programs to instruct other agents; 2) a glyph designer for glyph generation; 3) a texture designer for texture generation;  4) a Q&A evaluator that gathers user and LMM feedback for hyperparameter tuning of other agents to iteratively improve output quality.", "questions": "1. Should Equation (13) use max instead of argmax?\n\n2. For the hyperparameter optimization in Section 3.4, it seems that the parameters are updated with GPT (as illustrated in Figure 4). What are the detailed prompts used to instruct GPT to output the updated prompts/parameters for each case in Figure 4?\n\n3. For the user study, what does “Text accuracy” mean? Was the input text exposed to the participants during the study? What is the meaning of the percentages in Table 1?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. Artistic text generation is an important problem, whose solutions can be useful in many practical scenarios.\n\n2. The amount of the contribution is non-trivial since a lot of effort has gone into adapting and connecting different large models together to form a complete, large working system. \n\n3. The whole system is well motivated and designed.", "weaknesses": "1. Some technical details are missing. For example, in “Normal & Traditional Glyph”, the rendering details of normal and traditional glyphs based on $s^{gly}$ are unclear. Also, it is not clear how vector graphics optimization in “Semantic Glyph Transformation” is done. Where do the “target objects” come from and what is the loss function exactly? In Section 3.4, what is the meaning of $L_m$ and $L_u$ ?\n\n2. Some experimental settings are questionable. First, comparison to SDXL is unfair. Traditional text-to-image models like SDXL often require detailed prompts and may not work well with brief instructions (e.g., “Create a stylish word World Peace representing its meaning”) that the proposed system is supposed to handle. The proposed system utilizes GPT for input prompt extension and, thus, it should be compared against a GPT-augmented SDXL where GPT is used to extend the input prompt into a more detailed description relevant for text generation.  Second, in the user study, “Aesthetics & Creativity” should be divided into two independent dimensions, instead of being merged as a single one. Third, the claim “Table 2 presents quantitative results using SSIM and LPIPS metrics, further reaffirming the excellence of our approach in terms of readability and aesthetics.” is inadequate. SSIM and LPIPS mainly measure image similarity and, therefore, are not suitable for evaluating readability. To compute quantitative scores for readability and legibility, a more reasonable way may be to apply a model to detect and recognize text on the generated images and then compare the outputs with the ground truth text. Last, the “quantitative” evaluation of the Q&A evaluation agent is lacking. The results of the proposed method, without this agent, should be added to Table 1 and Table 2. \n\n3. Some results are not satisfactory. As shown in Figure 5, the outputs by the proposed method, although being visually appealing, suffer from low text legibility and readability: some letters and words are either hard to recognize due to some of their strokes being excessively deformed or stylized (e.g., as leaves), or hard to read because of low color contrast between them and the background.", "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)