TIPO: Text to Image with Text Pre-sampling for Prompt Optimization
Authors: Shih-Ying Yeh
YI LI
SangHyun Park
Giyeong Oh
Xuehai Wang
Min Song
Youngjae Yu
Shang-Hong Lai
Venue: ICLR 2026 Poster (2026)
Keywords: prompt optimization
prompt engineering
text-to-image
Primary area: generative models

Abstract: TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these prompts into richer, detailed versions. Conceptually, TIPO samples refined prompts from a targeted sub-distribution within the broader semantic space, preserving the original intent while significantly improving visual quality, coherence, and detail. Unlike resource-intensive methods based on large language models (LLMs) or reinforcement learning (RL), TIPO provides computational efficiency and scalability, opening new possibilities for effective, automated prompt engineering in T2I tasks.

We provide visual results, human preference report to investigate TIPO's effectiveness. Experimental evaluations on benchmark datasets demonstrate substantial improvements in aesthetic quality, significant reduction of visual artifacts, and enhanced alignment with target distributions along with significant human preference proficiency. These results highlight the importance of targeted prompt engineering in text-to-image tasks and indicate broader opportunities for automated prompt refinement.

Published as a conference paper at ICLR 2026
TIPO: TEXT TO IMAGE WITH TEXT PRESAMPLING FOR PROMPT OPTIMIZATION
Shih-Ying Yeh*
Li Youngjae Yu"
Sang-Hyun Park
Giyeong Oh ?
Xuehai Wang*
Min Song 90
Shang-Hong Lai*
National Tsing Hua University
Nanyang Technological University
Yonsei University"
Onoma AI
Karolinska Institute*
Scoul National University" Comfy Org Research"
Corresponding Author
Equal Contribution"
kohakuškblue .nt.
Code: github..u..... Scct///ohaauuBueeee.......
ABSTRACT
TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from sim- ple user prompts, TIPO leverages a lightweight pre-trained model to expand these prompts into richer, detailed versions. Conceptually, TIPO samples refined prompts from a targeted sub-distribution within the broader semantic space, preserving the original intent while significantly improving visual quality, coherence, and detail. Unlike resource-intensive methods based on large language models (LLMs) or re- inforcement learning (RL), TIPO provides computational efficiency and scalability, opening new possibilities for effective, automated prompt engineering in T2I tasks. Extensive experiments across multiple domains demonstrate that TIPO delivers stronger text alignment, reduced visual artifacts, and consistently higher human preference rates, while maintaining competitive aesthetic quality. These results highlight the effectiveness of distribution- prompt engineering and point toward broader opportunities for scalable, automated refinement in eext-to-image generation.
INTRODUCTION
The rapid proliferation of Iext-to-Image (T2I) generative models has revolutionized artistic creation
(Ossa et al. 2024, Betker et al., 2023 Esser et al.) 2024a Saharia et al., 2022 Ramesh et al. 2021
2022] Shi et al. 2020 Rombach a.. [002a Podell et al. 20244] /Sauer et al. [2224, Chen al.
2024b Li et al., 2024b Esser et al. 2024b black-forest labs, 2024). These models offer direct achieve precise control, modern T2I architectures often trained on lengthy, detailed text descriptions. which may consist of individual, formatted tags of objects, backgrounds, styles, or complex, integrated paragraphs outlining image content and layout. However, the increasing complexity of prompts often forces users iteratively refine them to convey intent. Moreover, most state-of-the-are T2I models are aesthetically fine-tuned (e.g., on LAION-aesthetics (Podell al. 2024)) favor nuanced artistic and stylistic cues, making high-quality T2I artwork mainly accessible to those with significant artistic expertise.
control over generative visual content via text prompts.
Extensive efforts have been made reduce the reliance human expertise through prompt opti- mization, i.c. expanding and refining a user's primitive input into a more detailed prompt to enhance generation quality. As shown in Figure [a), straightforward approach is to leverage pre-trained Large Language Models (LLMs) to rewrite prompts in a zero-shot manner (Mañas et al. 2024p. Yet, LLMS are primarily trained on general natural language, such as paragraphs and dialogues, which differ significantly from the structured prompts used for T21 models. This discrepancy often leads to additional effort in crafting LLM prompts and increased misalignment between generated images and intended prompts. Figure (bb) shows more effective approach: training LLMs directly on prompt data collected from model users (AUTOMATIC 2022; dasparthol 2022). While promising. this method is inherently constrained by the varying levels of expertise, often resulting in inconsistent or suboptimal outputs. Recent work (Hao et al., 2023) trains LLMs with reinforcement learning, where aesthetic scores of generated images serve as rewards, depicted in Figure

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However, reinforcement learning is performed specifie T2I model with high computational cost, hindering its application to a broader variety models.
Detallee
v petailled Prompt
Detailed Contantiaal Vuriely X Gotawet Gotanat-Aigred Frompe
Detailed
Contentual Variey
User Input
Contentual serddy
User Input
User Input
V Contictiull Variety
User Input
Dettorisllgieed Prompt
Deteoe-Alggeed Prompt
of Catanet-Aigned Prompt
Prompt
Original
Miclourncy prampis
Text
Caption
HL
mage
Degraded Caplian
Optimized Promot
motimieed Promps
Optimioed
Dall-E prompes
Prompt
Todinage
Iterstive Update
Oothers
Traines Detaset
Model
Model
Model
Model
(0) Direct L.M
Extend Prompt with Promptt DB
Opdimizaaiion with T21 Output
(d) Match Distribution of Dataset (Ours)
Figure 1: Comparison of prompt optimization methods using LLM. (a) uses instructions for prompting but its understanding is constrained by the LLM's knowledge base, not the T2I model. (b) relies on a curated prompt database, enhancing detail but limiting variety by not fully leveraging the T2I model's 's learned distribution.
(c) optimizes using the scorer with RL, requiring multi-turn inference with additional cost. (d) aligns prompts with the T2I model's s training distribution, ensuring detailed and diverse prompt generation that fits the target T2I model.
In contrast to prior work, we argue that good prompts should align with the large-scale text distribu- tions of T2I models' training, including those emphasized aesthetic fine-tuning, Such alignment allows models to better interpret user intent and leverage their learned priors, resulting in stronger text alignment and overall image quality. Based on this insight, we introduce TIPO (Text to Image with text pre-sampling for Prompt Optimization), framework that brings prompt optimization into the domain of large-scale multi-task pre-training. TIPO supported by a curated 30M-pair, 40B-token caption corpus, which is filtered and balanced maximize compatibility with leading T2I models and to preserve aesthetic quality. On top this corpus, we design a suite of pretext tasks that reformulate raw user inputs, including both concise natural sentences and tag-based prompts, into forms. Through this multi-task sampling pipeline, a lightweight
enriched and iisttibutiionccosiiie language model expands (rather than fully rewrites) user prompts, preserving original semantics while enriching details that are diverse, edit-friendly, and aligned with T2I training distributions. Extensive experiments on both in-domain and out-of-domain prompts show that TIPO consistently outperforms strong baselines, achieving a 62.8% win rate in human preference (validated by more than 1,400 pairwise comparisons from 221 volunteers), and providing up 29.4% runtime effi- ciency improvement. Figure iillustrraes the conceptual differences between existing methods and TIPO. To summarize, our contributions are least threefold:
1. We introduce TIPO, a prompt optimization framework that leverages the large-scale text distributions used in text-to-image (T2I) training.
2. We train lightweight multi-task language model that progressively refines both tag-based and natural language inputs into unified prompts, enhancing compatibility across a broad spectrum of T2I models.
3. Extensive experiments demonstrate that TIPO achieves superior image quality, stronger text alignment, higher human preference, competitive aesthetic quality, and improved runtime efficiency against strong baselines with SOTA T2I models, highlighting its practical value.
RELATED WORK
Prompt optimization for T21 models typically leverages language models, which can be broadly classified into two categories: (1) Model-specific strategies that tailor prompts for particular T2I model, and (2) Universal strategies that improve prompt quality across a variety of T2I models.
Model-specific Strategies T21 models generate images whose quality is often measured using metrics such as fidelity, aesthetics, and user preference. These metrics facilitate reinforcement learning approaches that optimize prompts for specific T2I model. For instance, Promptist (Hao al. 2023) fine-tunes a pre-trained language model by using CLIP relevance scores as rewards. Similarly, PAE (Mo al. 2024) extends this approach by generating dense text embeddings rather than discrete
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text tokens, with additional control vectors during online reinforcement learning. However, these methods are computationally intensive, often struggling with a larger number of training prompts. Moreover, model optimized for one specific T2I system may not generalize well to others. In contrast, our method leverages over 30 million text deseriptions to cover wide range of high-quality prompts, ensuring compatibility with broad spectrum of T21 models.
Universal Strategies To reduce the dependency on specific T2I models, some researchers have focused on refining prompts solely using language models. For example, Cog View3 (Zheng et al.) 2024) employ GLM-4 (GLM et al. 2024), and et al. (2024) employ GPT-J and Text Style Transfer (TST) techniques, respectively, for prompt enhancement. However, both of them rely heavily on the L.M's inherent understanding of visual content descriptions, which may result in misalignment with the diverse requirements of various T21 models. Alternatively, other approaches collect high-quality prompts from T2I model users to fine-tune or train LLMs (AUTOMATIC 2022, succintly. [002] daspartho 2022). Such methods, however, are limited by the inconsistent expertise of users. More recently, He et al. (2025); Liu et al. (2024b) leverage vision language models to optimize prompts in an iterative loop: a user prompt generates images via a T2I model, the camppt-maae pairs are evaluated by the VLM suggest refinements, and the revised prompts reapplied to T2I generation for continual improvement. While such iterative refinement can improve quality, the distribution mismatch between VIMMs and T21 training data persists. Conversely, our approach constructs both tag-based and natural language prompts using large-scale dataset of image-text descriptions, thereby aligning closely with the text distributions underlying T2I models.
PRELIMINARIES
We present the formal definition of T21 models and the problem statement of this work. Text-to-Image Model. A text-to-image (72I) model defines a conditional distribution
Ip = P(x p).
x E X,
which maps a prompt p to distribution of images over the space of all possible images X.
Problem statement. Let I,, denote the user's intended distribution over X. The task of prompt optimization is to find an optimized prompt Po from the space of all possible prompts P to minimize distance d between the T21 output distribution Ip and the intended distribution I,;
arg min d(Ip, Iu), pEP
Po =
where .(.,.) is a distance between image distributions (e.g., Fréchet Inception Distance).
4 METHODOLOGY
We aim to optimize user prompts to enhance image generation quality. Instead of end-to-end optimizations tailored to single T2I model, our focus is on prompt rewriting that generalizes across a broad spectrum of models. Our core intuition that an ideal prompt should align with the texts used in T21 model training. However, rapid advances in image captioning have rendered these texts increasingly diverse and complex. To address this, we propose to (1) design clearly structured prompt schema compatible with most text descriptions, and (2) implement pre-sampling algorithm that progressively refines arbitrary, coarse user input into organized, fine-grained prompts.
4.1 TEXT SET PREPARATION
Although the text deseriptions used for T2I model training are notably diverse, most are image captions that fall into two broad categories: tag-based and natural language (NL)-based captions. Tag-based captions, such as those the Danbooru2023 dataset (nyanko202) 2023 Yehl 2024a), use comma-separated. succinct terms to describe image content. In contrast, NL-based captions, typically generated by language models with visual capabilities (Liu et al. 2023 Agrawal et al. 2024 OpenAl
2024 Li al. 2024a Bai et al. 2023 Dai et al. 2024 Xiao al. [2024] Deitke et al. 2004 may comprise multiple sentences. We represent both types using a unified text set
{t,, 12 ta t,t
where each element is an individual tag sentence.
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(a) No Pre- Pre-Sampling
(b) Add Details
(c) Add Random Words
(c) TIPO Pre-Sampling
Distribution of Pixels
Al
All Images
All
All
Indereed Images
Imngess
Whand
Inferred mages
imggss
Inferred magas
images
Distribution of All the Images
Expected Output Distribution Input Prompt Model
Expected
Expected
Dipected
Expected
Inferred Ouput Distribution of Model for Given Input Prompt
Nonsensical
Not User Indent
Prompt
astronnd rides horse Mors <75
<Te Tonefommaion farcite- UU Baxiin 1P0
Figure 2: Illustration of various pre-sampling method for generating the T2I prompt An astronaut rides horse on Mars + <T> (a) yields a basic image. (b) enhances details of images but requires manual refinement. (c) adding random words may introduce irrelevant content (red boxes), exceeding the user's s intent. (d) TIPO pre-sampling (ours) aligns outputs with expected intent, maintaining both detail and variety, <T> represents a transformation function for pre-sampling.
While the original image captions are fine-grained and detailed, which can yield high-quality images when all elements are used, they often result in prompts that are excessively lengthy or overloaded with information. Such prompts diverge from typical user input and pose alignment challenges. To mitigate this, we construct a simpler subset by removing some tags and sentences from the original prompt Iset, detailed in Section 4.2
4.2 FORMATTED PROMPT CONSTRUCTION
We aim to construct prompts in unified format compatible with existing image captions. First, we incorporate the common metadata present in these image cap- tions, typically represented as <Category>: <Content>, These metadata cat- egories primarily include style, aspect ratio, quality, and year (e.g.. quality: masterpiece, style: Impressionist) This structured metadata is intuitive for users to read and edit, while also providing strong guidance to downstream '21 models on the generation scope.
Next, we construct both tag-tasse bagebased and NL-based prompts using text T. Our design generates both simple (incomplete) and complete prompts for each image, and we train auto-regressive language model to extend the simple prompts into complete versions. For tag-based prompts, since the tags are largely order-insensitive (i.e., the order has minimal impact T21 outcomes), based dropout strategy. We first randomly shuffle the complete set tags T = (t1, f2, f3...... ... fn from given image caption. Then, construct simpler tag set T, (41 1,,,.., (m) by randomly selecting m < n tags. The prompts are constructed as:
propose a prefix-
Ps concat(T.), P. concat(T)
Here, and P., denote the simple and original prompts, respectively. By ensuring that p. is always prefix of Po. language model can readily expand simple tag-based prompts into complete versions.
For NL-bbased prompts, however, this strategy cannot be applied directly because the first sentence often contains crucial information (Godbole et al., 2024) and the order of sentences significantly influences the caption's semantics. Therefore, we preserve the first sentence and randomly drop some of the subsequent sentences without changing their order. Let:
S [sentence,, sentencez, sentence,, ..... sentence,]
represent the ordered sequence of sentences in an image caption. We derive simple subsequence S, by randomly selecting m sentences from S while ensuring that the first sentence always included and that the original order is maintained. In other words,
(sentence,, sentence senteeeee,.....sstttnne...e.
with 1 < is ... im and n. The simple and complete NL-based prompts then
constructed as:
P. conc Po concat(S.,
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This ensures that p, remains prefix of Pa- Although some sentences may be repeated in po. selecting a smaller m effectively mitigates this, and it does not empirically affect the generation quality.
4.3 TEXT PRE-SAMPLING
We aim to reformulate user input into forms that better align with the high-quality training text distri- bution Po via pre-sampling, which stands for "text sampling before image sampling." A naive strategy is plain text completion, where tokens are directly appended to the user input in an unstructured manner. Such completion often mirrors the low-quality phrasing of the input, deviates from the distribution of high-quality prompts, and produces inferior generations. On the other hand, a full rewrite risks deviating from the user's original intention. To balance these issues, TIPO preserves the original input and appends structured, distribution-conss typically paragraph-like or tag-like, making it both informative and easy to edit or remove (see Figure for an illustration and Appendix for concrete examples).
expansion. This appended segment
We propose the core technique TIPO, a flexible pre-sampling mechanism that decomposes prompt optimization into three subtasks: enriching tag sequences. extending natural language (NL) prompts, and refining NL prompts. For example, short NL prompt can be expanded into a detailed tag sequence (short to tag), as illustrated in Figure [] We further distinguish between basic tasks, which perform single transformation, and composite tasks, which chain multiple transformations within single forward pass. The latter expose the model to more holistic training signals while reducing computational overhead. Table summarizes all tasks and their input-output forms.
Task
Description
Basic tasks tag. lag_to_long
Generate a new NL prompt given tags.
long tag
Extend tag sequence given an NL prompt.
short to tag
Extend tag sequence given short/simple prompt p.. Generate a refined, detailed NL prompt given a user provided NL prompt.
short_to_long
Composite tasks
short to tag to long short_to_long long_to._aag tag www.oosssoottuooo long
From a short NL prompt or tags, produce refined NL prompt.
From a short or generated NL prompt, extend a tag sequence.
From tags or NL prompts, generate refined NL prompt.
Table Pre-sampling tasks TIPO. Basic tasks focus on one-step transformations, while composite tasks combine two basic tasks within single forward pass.
We randomly select from the aforementioned tasks during training to enhance model generalization. By extensively training on these tasks, TIPO can seamlessly adapt to various input types, flexibly refining user input whether it consists of tags, short sentences, or long sentences. Figure 3 (b) illustrates a scenario where both tag captions T3 and short NL captions S, are available. In such cases, TIPO processes each input type separately to maintain clarity and coherence:
S. = [A young girl with long hair..), metadata = T. (outdoors, scenery, water, wind, landscape, ...
The generation proceeds sequentially follows:
short to tag: TIPO uses T, as the primary prompt to generate a detailed tag sequence Ta-
2. tag to long: Ta is incorporated into the metadata, and TIPO produces refined short NL prompt S, based on Tа.
3. short to tag to long: With both Ta and S, in the metadata, TIPO generates a comprehen- sive long NL prompt Sa, ensuring more detailed output.
4. TIPO aggregates Ta, S4, and any additional metadata to construct context-rich prompt Pa-
This progressive process enables TIPO to build prompts that are both detailed and contextually aligned with the user's input.
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sooeey
Proeec Oolmittioo
outdbon, scenery;
Ayoungghools long atbed ng onaiannyy
Ayounggri Mith..
User liput
User legut
TIPO
Soceess doudy meartarcee ditry siy.
outdoors sooncry: watee
weler (maah,
Ayeungg
long Mlcce
Tag Prompt
Tax Prompt
feowerks, zeral
doy mourtaioee
scrneny daadd
TIPO ogniingg
Bua mou-mainnnn the beisddge sund......
Abeauthi Irigt
black white
outdoons,
water beach, long Mac-hhai
scentry.
TIPO nggiian
Ayoungaltwith longhal onasardy boadh
grost
holaor, Stary Stany-ig-
eyes,
Naturall Frompt
Long
Nattura
Promet
Long
peoceful rightaime scone with two resl ng grass, framed
treek... mountarrs
sarewwch latuin
the moseaann meeeoggguurd.
AboudR/nignt
TIPO Justss
Alwad aodu cour
frwindo showethe
sandy beach Jarrert
red boso henheeeing stancing ona
fiweh balranda
lang dedeedyyy beach.........................
4yoangggri with
TIPO Suttsse
Ay longnar buach..
(a) Scenery tag only input
(b) Tag short NL input
Figure 3: Example prompt optimization paths in TIPO. (a) shows generation from single tag input, while (b) uses both tag and natural language input. These illustrate representative pipelines, not the full range of use cases. Blue shading indicates increasing prompt richness.
Implementation Details In implementation, TIPO adopt the LLaMA architecture (Touvron et al, 2023a by Al @ Meta | with all experiments are conducted with a 200M -arammeee model
Our training dataset is about billion tokens curated from Danbooru2023 (nyanko202) 2023 Yeh
2024b), GBC1OM (Hsieh et al. 2024), and CoyoHDI IM (CaptionEmporium) 2024).
5 EXPERIMENTS
5.1
EXPERIMENTAL SETTINGS
Baselines and T2I Models We compare against representative prompt-optimization baselines: GPT-4o-mini for zero-shot rewriting (OpenAl 2024), MagicPrompt, which fine-tunes GPT-2 on community-collecter prompts (daspartho 2022). Promptist, which applies reinforcement learning for model-preferred prompt optimization (Hao et al. 2023), and 1ammini-200-fas--mmaae. 3 which uniquely serves both as a backbones, our main experiments adopt SDXL-base-I.0 (Podell et al. 2024), Illustrious v3.5 (v- pred), Kohaku-XL-Zed and Stable Diffission 3.5 Large(Esser et al.J 2024a). To further assess generalization, additionally evaluate on four diverse backbones with undisclosed training data: FLUX. 1-dev (black-forest labs, 2024). Omnigen2 (Wu et al. (025). Lumina-2 (Qin et al. 2025), and HiDream-T1
baseline and T2I generator. For image generation
(Cai al. 2025 The details of T21 models provided in Appendix B
Evaluation Metrics
We employ four latest metrics FDD (Stein et al. 2023), Aesthetic Score (dis
cus0434 2024), AI Corrupt Score (narugo 2023). and Vendi Score (Friedman & Dieng 2022) to measure the quality of generated images. Specifically, FDD (Fréchet DINO Distance) quantifies fidelity by comparing the distribution of DINOv2 features (Oquab et al. 2023) between reference
images in the evaluation dataset and images generated from the corresponding captions, which better aligns with human perception than traditional FID (Heusel al., 2017). Aesthetic Score is computed via Aesthetic Predictor V2.5 (discus0434 2024). quantifying visual appeal, composition quality, and artistic merit. AI Corrupt Score detects technical flaws in generated images by identifying visual artifacts. Vendi Score quantifies image diversity by calculating the von Neumann entropy from normalized cosine similarity matrix using Dino V2 embeddings. Notably, Vendi is defined directly on feature-space space dispersion and is sensitive to non-semantic variations such low-level noise or artifacts. As result, it "should be used alongside a quality metric" (Friedman & Dieng 2022) and a higher value alone does not always correspond to more meaningful semantic diversity.
'The multi-task design of TIPO is compatible with many autoregressive language models (c.g., GPT, LLaMA, Qwen, etc.). Intuitively, adopting more advanced backbones could further enhance efficiency and effectiveness, but such exploration would require extra training cost, which defer future work.
"We also trained TIPO-100M and 500M variants to analyze the impact of model scales. See Appendix http:///wwooooor.oooooo/oo//oo//

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Published as a conference paper at ICLR 2026
(a) Scenery Tag Only
(b) Scenery Tag + TIPO (c) Truncated Long Prompt
(d) Truncated + TIPO
Figure 4: Generated images from 4 types of prompts: (a) simple scenery tag. (b) scenery tag enhanced
by TIPO, (c) truncated (< 40 words) long prompt, (d) TIPO-enhanced truncated prompt. TIPO adds detail and maintains variety, yielding coherent images from simple prompts.
Evaluation Protocols Our proposed TIPO leverages large-scale image caption datasets for training, which overlap with the training text distributions of many T21 models. Following Promptist (Hao al., 2023), we divide our experiments into two settings: (1) In-domain, where the T2I model's training texts overlap with those used by TIPO, and (2) Out-of-domain, where no overlap exists.
5.2 EXPERIMENTAL RESULTS
In-domain Tag-based Prompt Optimization To assess prompt optimization performance tag-based prompts, we generate scenery images, as they contain abundant descriptive tags for objects and backgrounds. We randomly sample 32,768 tag-based prompts from Danbooru2023 (Yeh) 2024b nya 2023), shuffle and concatenate the scenery tags into new prompts (thereby preventing data leakage of the original captions), and generate one image per prompt using Kohaku -e--et--eeeee -eta. This evaluation tests the in-domain capabilities, as Danbooru2023 is used during both TIPO and Kohaku-XI-2eta training. The results in Table 2 reveal two key insights. First, MagicPrompt and Promptist, which rely on user prompts or reinforcement learning, underperform in Aesthetic and Al Corrupted Scores due to the quality or quantity limitations of their collected samples (e.g.. Promptist uses 90K samples, limited by the high reinforcement learning cost). In contrast, GPT and TIPO benefit from large-scale training corpora (>30M samples), yielding higher-quality outputs. Second, TIPO achieves the best FDD by a substantial margin over GPT, which can be attributed to its superior distribution alignment with T2I models.
In-domain NL-based Prompt Optimization We evaluate the prompt optimization performance on NL-based prompts by selecting 10,000 short prompts and 10,000 long prompts from CaptionEm- porium (CaptionEmporium (024) and GBC (Hsich al. 2024) as test prompts. In particular, since the long prompts are much longer than typical user input, we truncate them to two sentences (< 40 words) to simulate real-worke applications. We use SDXL-1 0-base as the T2I model, whose training text data largely overlap with TIPO. Table 2 demonstrates that TIPO achieves either the best or second-best scores in Aesthetic and AI Corrupt Score by effectively enriching the original prompt with appropriate textual elements while rarely introducing extraneous noise. While all methods com- promise fidelity and diversity, as reflected in the FDD and Vendi Score, TIPO remains competitive because it maintains small semantic deviation from the original sentences via progressive refinement.
Out-of-domain Performance Some recent T21 models are trained on proprietary images and captions. As a representative example, 5-aaa.--rrgr is trained on private images captioned with Vog (Zheng al. 2024), which differ markedly from the texts used to train TIPO. To evaluate model performance in this out-of-domain scenario, we generate 8, 89192 original tag- and NL-based prompts using the baseline GPT-4o-mini rather than relying existing prompt datasets. We apply the remaining methods to these prompts and assess their performance.
As shown in Table 2. SD-3.5-Large faithfully generates images that align well with GPT-produced prompts. Consequently, additional optimizations tend to reduce fidelity and introduce more artifacts. Nevertheless, GPT-generated prompts are accurate and lack diversity. TIPO optimization enriches
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Table 2: Comprehensive performance comparison TIPO against baselines across different in-domain prompt types. Metrics are marked with T (higher is better) or L (lower better). For OOD tests, the 'Original' baseline and the FDD metric was not applicable. In the table, Aesthetic refers to Aesthetic Score, Corrupt to Al Corrupt Score, and Vendi Vendi Score. TIPO demonstrates significant improvements, achieving the highest average rank among all baselines. Best results in bold and second-hest underlined.
Prompt Type/ Task
Metric
Original
GPT
MagicPrompt
Promptist
TIPO
FDD Aesthetic f
0.3558
0.5414
0.3247
0.2350
0.2282
In-domain
5,0569
6.3676
6.1609
5.9468
6.2571
Tag-based Prompts
Corrupt +
0.5743
0.2510
0.4976
0,4331
0.0805
Vendi
16.814
8,663
11.901
14.327
13.307
FDD
0.0957
0.1668
0.0980
0.1783
0.1168
In-domain
Aesthetic T
5.8370
6.0589
5.8213
5.7963
5.8531
NL-based (Short)
Corrupt L
0,2887
0.3015
0.2936
0,3686
0.2870
Vendi
38.172
34.714
38.155
34.127
37.065
FDD Aesthetic f
0.0955
0.1683
0.1247
0.2096
0.1210
In-domain
5.7497
6.0168
5,8191
5,7759
5.8364
NL-based (Truncated Long)
Corrupt L
0,3132
0.3288
0,3259
0,4075
0,2870
Vendi
38.253
34.811
37.841
33.527
37.090
Aesthetic
N/A
6.7125
6,4507
6,3924
6,0536
Out-of- Domain (OOD) Test
Corrupt +
N/A
0.0518
0,1423
0.0947
0.0720
Vendi
N/A
8.9718
15.872
16.489
21.571
Overall
Average Rank |
2.58
3.00
3.00
3.87
2.07
Table 3: TIPO on T2I models with undisclosed training data. Significant improvements are in bold.
Model
Variant
Aesthetic
AI Corrupt
FDD
Vendi f
Original
5,2029
0.1202
0.1185
36.2597
FLUX.I-dev
TIPO
5.2746
0.0938
0.1202
35,6489
Original
5.2629
0.1110
0.1373
33.7724
Omnigen2
TIPO
5,0661
0.1187
0.1253
34.2681
Original
5.2588
0.0981
0.1318
34,7599
Lumina-2
TIPO
5,4105
0.0916
0.1286
34,4655
Original
5.7768
0.1055
0.1444
34.6318
HiDreamm-I1
TIPO
5.8143
0.1019
0.1379
34.3544
Self-refined
5.2584
0.0928
0.8084
32.7422
Gemini-2.0--lssh-Imaag
TIPO
5.2649
0.0700
0.7964
32.1136
the prompts with additional details that harmonize with the original themes, significantly enhancing the diversity of the generated images.
Compatibility with other T2I Models In addition the above baselines with publicly known training data, we further evaluate TIPO on recent models with undisclosed training sources:
FLUX, 1-dev (black-fores forest labs 2024). Omnigen2 (Wu al. 2025). Lumina-2 (Qin al. 2025), HiDream- I1 (Cai et al. 2025 and waw.inii2.f-faaal 1o-flash-image (Googlc), using 1,000 prompts sampled from the COYO/GBC datasets. Notably, (emmin--.00ffash-iiee
can perform its own prompt optimization, making it strong closed-source baseline. As shown in Table TIPO gen- erally improves image quality and alignment relative to the baselines. For example, Lumina-2 and HiDream I1 achieve higher aesthetic scores and lower corruption rates after TIPO optimization. Despite Gemini's 's integrated optimization, TIPO still yields measurable improvements, highlighting the practical value of our lightweight, task-specific strategy. Overall, these results demonstrate that TIPO maintains robust compatibility with heterogeneous T2I models, even when their training data unavailable and potentially mismatched with TIPO's optimization corpus.
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Efficiency A key concern is whether TIPO's iterative pre-sampling strategy introduces noticeable latency. Hence, we benchmark latency with improvement up to 29.4%. Details of training and inference are in Appendix H.4
latency in Table 4 where TIPO reduces per-proepn
Table 4: Prompt generation latency and relative speedup
TIPO
Promptist
PromptExtend
MagicPrompt
Avg. Time (8)
1.03
1.46 +29.4% 4%
1.38 +25.6%
1.14
TIPO speedup vs. each
49.6%
Human Preference Evaluation Quantitative metrics may not fully align with human preference. Therefore, we conducted a user study based on pairwise image comparisons between the original prompt, MagicPrompt, Promptist, and TIPO over 1,400 images, gathering preferences from 221 volunteers. As illustrated in Figure 5a TIPO achieved the highest overall win rate at 51.3%, significantly outperforming competitors. out-of-domain scenarios, TIPO's ' win rate increased to 52.5%, demonstrating consistently strong user preference across different contexts. For further results and statistics, please refer Appendix G
All Data
Out of Domain
100
80
32.19
43.9% AS Sh 45,47
Tic
AR 4C.E%O
Aestheti
Image Quality
Percentage (96)
Ovaral
Rating
601
11.29
11.5%
21.29
37.5% 18.0%
40
1000
20
S1.5%
35.394
AL 0%
D4S%
12.9%
380
902
TIPO
Promgory
TIPO
Prompiis
prompeod
orpiiial
TIPO
Pronpetie
(a) Pairwise comparison results showing win-tie-lose percentages for overall user preference. Evaluations cover All Data' and Diotaf--Dmain' sets for TIPO and baseline methods.
(b) ratings comparing TIPO and baseline methods across four criteria: Prompt Adherence, Image Quality, Aesthetic, and Overall. The dashed line indicates the initial ELO rating.
Figure 5: Human preference evaluation demonstrates that TIPO consistently achieves higher user preference compared to baseline prompt optimization methods (Promptist, prompttext, magicprompt, and original prompts). All evaluated images were generated using SD-3. 3.5 5-Medium
Prompt Distribution Alignment While TIPO consistently achieves the best or competitive results in image quality, it remains unclear whether such gains arise from closer distributional alignment between its optimized prompts and the T2I models' training text corpora. To verify this hypothesis, sample from two representative T2I training sets, obtaining 1,000 natural- -lnguage captions from COYO and 1,000 tagebased captions from Danbooru2023. We then encode ground-truth captions and the outputs of all compared optimization methods using widely adopted text encoders T5- XXL (Raffel et al., (2020a) (used in SD3, Flux, and PixArt) and jina--mbedee embeddings -3 (Sturua al. 2024) popular recent text-embedding model). Finally, we measure the embedding-space alignment between the ground-trumg truth captions and optimized prompts with Fréchet Distance (FD) and Maximum Mean Discrepancy (MMD with RBF kernel). As shown Table baseline methods show varying extents of alignment across different prompt types, text encoders, and metrics, while TIPO maintains consistently better alignment under all settings. This indicates that TIPO aligns well with manner.
the T21 training-text distribution in prompt-compatible and
Prompt-Image Alignment To further assess the semantic consistency between optimized prompts and their generated images, compute CLIPScore between each prompt and its corresponding SDI.5-generatee image using wttpaa//piiiiiiiiaapeeeo sets in the Prompt Distribution Alignment experiment. As shown in Table 6 TIPO achieves
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Published as a conference paper at ICLR 2026
Table Embeddingg-spee spacn distances (FD and MMD) between optimized prompts and T2I training corpora. Lower is better. Best results are in bold and second-best are underlined.
Prompt
Encoder
Metric
TIPO
Promptist
MagicPrompt
GPT-4o-mini
FD
0.0322
0.1003
0.0385
0.1064
Jina
MMD
0.0320
0.1501
0.0624
0.1699
NL-short
FD
0.0704
0.2072
0.1441
0.1252
T5
MMD
0.1438
0.2914
0.1972
0.2297
FD
0.0309
0.1192
0.0493
0.0963
Jina
MMD
0.0359
0.1700
0.0772
0.1642
NL-trunc
FD
0.0674
0.2312
0.1884
0.1276
T5
MMD
0.1404
0.3147
0,2270
0,2323
FD
0.1094
0.1891
0.1958
0,2479
Jina
MMD
0.1539
0.2473
0,2415
0.3050
Tag-based
FD
0.0524
0.2080 0.3573
0.2578 0.3948
0.0728
T5
MMD
0.1846
0.2194
Table 6: CLIPScore between optimized prompts and T21 generated images. Higher is better. Best results are bold and second-best are underlined.
Prompt Type
TIPO
MagicPrompt
GPT-4o-mini
Promptist
Tag-based
0.2217
0.1782
0.1774
0.1642
NL-short
0.2413
0.2834
0.2378
0.2347
NL-trunc 0.2310
0.2517
0.2275
0.2063
the strongest alignment for tag-based prompts. While MagicPrompt shows clear advantages NL-based prompts, this is likely due to its 1.8M+ SD1.5 community training corpus, where prompts typically explicit and stylistically strong, effectively eliciting the CLIP encoder produce high alignment scores. However, such stylistic bias is often less preferred by mainstream users. In contrast, TIPO attains competitive performance on NL-based prompts without such bias, as reflected by the win rate in Table where MagicPrompt vs. TIPO = 11:48.
CONCLUSION
We introduced TIPO, a lightweight prompt pre-sampling framework designed for efficient real-world Text-to-Image (T2I) applications. By aligning user prompts with the intrinsic distributions T2I training datasets, TIPO enhances semantic coherence, image fidelity, and diversity with minimal inference overhead. Experimental results show that TIPO consistently outperforms existing prompt optimization methods across multiple evaluation metrics, while extensive user studies confirm its strong alignment with human preferences. Despite these promising results, several aspects remain open for future exploration, such generalization to tittof--ditributionn user input, model-specific adaptation, personalization, discuss these limitations and potential extensions in Appendix To encourage wider adoption and facilitate reproducibility, we release our trained models and source code. We hope TIPO will inspire further advancements in efficient, scalable, and robust generative frameworks for creative systems.
sange-feedbackeeee
refinement, and large-scale model scaling. We
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ETHICS STATEMENT
Our work democratizes prompt optimization for text-to-image models, but automatic prompt enrich- ment can inherit biases from training data or misused produce misleading or harmful content. We therefore emphasize responsible use, including bias mitigation, transparency, and appropriate user controls. No human subjects or sensitive personal data were involved, and we avoided including identifiers metadata that could raise copyright- or attribution-rellted concerns.
REPRODUCIBILITY STATEMENT
An anonymized repository with code and configuration files provided the supplementary materi- als, enabling reproduction of all reported results. The main paper and appendix reference the exact experimental settings, hyperparameters. random seeds, and evaluation scripts; data preprocessing steps and any additional resources needed to re-create the experiments are also documented there. For hardware, we report that all experiments were conducted on NVIDIA RTX 3090 and/or A6000 GPUs, with full details described in the appendix and repository.
ACKNOWLEDGMENTS
This work supported by Institute of Information & communications Technology Planning & Evaluation (IIP)) grant funded by the Korea government (MSIT) (No. RS-2021-112 Artificial Intelligence Graduate School Program (Seoul National University)). This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. RS- 2034--24003)4 This work was supported by the Technology Innovation Program(RS- 2025-25456760, Development of a humanoid robot specialized in chemical processes based AI foundation model) funded By the Ministry Trade, Industry and Resources(MOTIR, Korea)
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Appendix
TABLE OF CONTENTS
A
Dataset/Resource
20
Danbooru2023
20
A.2
GBCIOM
20
A.3
Coyo HD 11M
20
B
Baselines/T2 models
20
B.1
Prompt-Optimization Baselines
20
B.2
T2I Models
21
C
TIPO
Implementation Details
22
C.1
TIPO Training Data Construction
22
C,2
TIPO Training Settings and Model Configurations
..........
23
C,3
TIPO Inference Settings
................................
24
C,4
Impact of Model Size on Performance
25
C.5
Impact of Model Size on Inference Speed
25
D
Evaluation Statistics
26
D.1
In-domain test regarding scenery tag
26
D.2
In-domain prompt generation test
27
D.3
Out-of-domain evaluation
29
D.4
Ablation Test
32
E
TIPO example
33
F
Image Examples
36
F.
In-domain test regard to scenery tag
36
F2
In-domain prompt generation test
36
G
Human Preference
37
G.1
User Interface for Human Preference Evaluation
40
G.2
Extended Human Evaluation.
40
G.3
ELO Ratings,
40
G.4
Human Preference ELO Method
40
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G.5
Statistical Significance
41
G.6
Survey Response Examples
42
G.7
Conclusion
45
H
Ablation Study TIPO
46
H.1
Experimental Setup
................................
46
H.2
Evaluation Metries
46
H.3
Results & Discussion
47
H.4
Speed Test and Overhead Analysis
47
H.5
Conclusion of Ablation
48
Topic Distribution Visualization
48
Discussion and Future Work
51
K
Disclosure of LLM Usage
51
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A DATASET/RESOURCE
A.1 DANBOORU2023
The Danbooru2023 dataset (Yeh) 2024b a. nyanko202 2023 is extensive collection of images and their corresponding tags, compiled from the Danbooru image board. This dataset includes images annotated with particular and detailed tags, providing rich resource for training both the Text-to-lmage (T2I) and Large Language Models (LLMs) involved the TIPO framework. The dataset contains data up image ID 7.349,999, encompassing various visual content with granular annotations. These annotations allow for creating nuanced and precise prompts, ensuring that longer, more detailed prompts can indicate subsets of shorter prompts.
Key Characteristics:
Rich Annotations: Detailed tags differentiate subtle variations, crucial for specific image generation.
Large Volume: Extensive dataset size ensures diverse training examples.
Tag-Based Prompting: Refined prompts from detailed tags enhance image generation accuracy.
A.2 GBCIOM
The GBC10M dataset (Hiihh et al. 2024) is large-scale collection of 10 million images sourced from CC12M (Changpinyo et al.) 22021) annotated using the Graph-Base Captioning (GBC) approach. Each image is represented by a graph where nodes correspond to object regions, compositions, and relations, and edges define their hierarchical relationships. Annotations are generated automatically through pipeline leveraging pretrained multimodal large language models (MLLM) and object detection tools. The GBC structure enhances traditional image captions by providing detailed descriptions and structural information. Data is provided in JSON lines format, including image URLs, bounding boxes, and captions.
In TIPO, only the root node captions from GBC10M are utilized for concise yet descriptive prompts.
A.3 COYO HD 11M
The Coyo HD I 11M dataset EmporionEmporium 2024) consists of 11.4 million high-resolution, high-concept-densityy images paired with 22.8 million synthetic captions generated from the Coyo- 700M dataset. Images maintain minimum of 512 pixels on the shortest edge to ensure high visual quality. Captions, generated with the LLaVA VA-Neet Next-8B model (Liu et al. 2024a) based on LLaMA 3 (AT@ Meta 2024), undergo post-processing for conciseness and clarity.
TIPO uses short and long captions, booru tags, and open image tags from this dataset.
B BASELINES/T21 MODELS
B. PROMPI-OPTIIIZZATION BASELINES
GPT-4o-mini. GPT-4o-mini(OpenA 2024) a multimodal model introduced by OpenAl in July 2024 as a cost-efficient variant of GPT-4o. It supports a 128k-token context window and up 16k output tokens, trained on text and vision data. this paper, it serves as a zero-shot rewriting baseline for prompt refinement.
MagicPrompt.
(asperrrmmmddppartti 2022) fine-tunes GPT-2 models on large-scale,
community colleette prompts (e.g., from Lexica.art and the Stable Diffusion Prompts dataset). learss to generate extended prompts with richer descriptive content, originally designed to improve prompt quality for Stable Diffusion.
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Promptist. Promptist(Hao al. 2023) is reinforcement learning framework for prompt optimiza- tion. Starting from a seed prompt, generates refined prompts using supervised pre-training and reinforcement learning, guided by aesthetic and semantic alignment rewards. It consistently improves model-preferred prompt quality when paired with diffusion backbones.
variant of Google's Gemini 2.0 Flash family with native image generation support. It provides both prompt-refinemet and text-to-image generation within single model, supporting high-fidelity text rendering, compositional control, and iterative editing. Unlike other baselines, it functions both optimizer and generator.
B.2 T21 MODELS
SDXL Stable Diffusion XL (SDXL) (Podell et al.
2024) improves upon earlier models (Rombach
al. 2022b) with a more considerable UNet backbone and dual text encoders (CLIP ViT-L (Radford et al. 2021) and OpenCLIP ViT-bigG (Ilharco et al. (02T)), enhancing text conditioning. Supporting resolutions up 1024x1024, SDXL accepts natural language prompts and tags, suitable for diverse
image generation.
In this paper, three SDXL models are used without the refiner model SDXL- Illustrious v3.5 - vpred and Kohaku-Zeta 8
Illustrious Illustrious a series of fine-tuned Stable Diffusion XL models primarily trained on the Danbooru2023 dataset. In this study, we specifically employ the v3.5 version variant with parameterization (Salimans & Ho 2022), which is notable for its extensive incorporation of natural language prompts. The inclusion of both tag-based and natural language formats allows Illustrious to leverage a broad range of semantic knowledge for image generation.
Within TIPO, perform ablation study to analyze the effectiveness of different prompting dtrategies-nann extended tags versus natural language prompts- to identify which approach contributes most significantly enhanced image generation performance.
Stable Diffusion 3.5
Stable Diffusion 3.5 (SD-3.5) incorporates the MMDiT architecture [Esser al. 2024a) and the Rectified Flow formulation (Liu al. 2022 Albergo Vanden Vanden-Eijnden
2022 Lipman et al. 2022) for improved text-to-image generation. Utiiizing triple text encoders (CLIP/VIT-L, CIPICLIP/ T5-XXL (Raffel al., (220b)). SD-3.5 supports resolutions up to 1024x1024 and uses 50/50 mix of original and Vo MM-generated captions. Figures confirm the capability to process both natural language prompts and tags.
This study employs SD-3.5-Large (8B parameters) with FP8 inference on RTX 3090 or RTX 4090
GPUs.
FLUX.1-dev FLUX.I-dew is open-weights text-to-image model released by Black Forest Labs as a guidance-distillee variant their FLUX.1 family, targeting research and non-commercial (black-forest labs 2024). Architecturally. FLUX adopts transformer- rectified-flo formu- lation (Lipman et al. 2022) with a hybrid stack of multmmoda/pparalle diffusion transformer blocks and rotary position encodings, scaled to 12B parameters, emphasizing prompt adherence, typog- raphy, and aspect-ratio flexibility (black-forest labs 2024). We use the FLUX. 1 1-dev checkpoint for T2I without additional refiners; it accepts both natural-language prompts and tag- like inputs and supports resolutions in the 0.1-2.0 MP range [T0]
OmniGen2 OmniGen2 is a unified multimodal generator that decouples autoregressive text mod- eling from diffusion-based image generation via two distinct pathways with unshared parameters
https:
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(Wu et al. 2025). The diffusion side conditions on hidden states from the MLLM while exclusively feeding VAE features into the diffusion decoder to preserve low-level fidelity; 3D rotary scheme (Omni-RoPE) disentangles sequence ID and 2D spatial coordinates stabilize editing and in-context generation (Wu et al. 2025). Beyond standard T21, OmniGen2 natively supports image editing and subject-driven in-context generation, and introduces reflection mechanism/dataset to iteratively refine outputs.
Lumina-Image 2.0 Lumina-Image 2.0 proposes unified and efficient T21 framework built Unified Next-DiT-a single-stream DiT that performs joint self-attention over text and image to- kens kens-paired with task-tailored Unified Captioner (UniCap) that produces gaaltiigaanuarrtty.. multi- lingual captions for training (Qin et al. 2025). The model employs Multimodal RoPE, progressive resolution training (256-> and effiiintt inference (CFG-Renorm/TTumm (Lin et al. 2024] Yi
et al. 2024), Now-DDMM-ollve (Xie et al.) 2024), TeaCache (Liu et al., 2025 to improve prompt- following and speed at only 2266 parameters (Qin et al. 2025).
HiDream-11 HiDream-11 is a 17B-parameter image foundation model based on sparse Diffusion Transformer with dynamic Mixture-of- Ef-erts (MoE) (Cai ct al. 2025). It employs a dual-stream (text/image) sparse DiT for separate encoding followed by single-stream sparse DiT to fuse modalities efficiently; hybrid text encoders (c.g., CLIP-L/14, CLIP-G/I4, T5-XXL) and an LLM aggregator provide robust conditioning (Cai al. II-Dev (guidance-disse -iittilled,,,, 28 steps), and Ii-Fast (14 steps), with a GAN-poweree diffusion distillation retain sharpness at low step counts.
2025) The suite includes I1-Full (50+ steps),
C TIPO IMPLEMENTATION DETAILS
In this appendix, we provide all the necessary details including our dataset construction process. model configurations, inference pipeline, and the model's 's properties not mentioned Section 4.2
and
C.I TIPO TRAINING DATA CONSTRUCTION
This section details our methodology for constructing and preprocessing training data to ensure robust model performance across various input scenarios.
Length Control To systematically control output prompt length, we implement a structured length categorization system using unique length tags. These tags enforce specific constraints on tag counts and natural language sentence lengths. For instance, the <1 <long> tag specifies that the corresponding prompt must contain between 36 and 52 tags (inclusive), accompanied by 4 to sentences of natural language description. We define four distinct length categories, each with strict bounds for tag count and sentence length.
Type
Very Short
Short
Long
Very Long
Tags (count)
18
36
48
72
NL (sentences)
2
4
8
18
Table 7: Maximum length specifications for each category and caption type. For each category, the actual count/length must not exceed these values.
To enhance input diversity and better simulate real-world usage patterns, we implement several data augmentation strategies:
Random Augmentation
Metadata Tags:
For tags representing image metadata (e.g., artist, character, aspect ratio),
we employ two randomization techniques:
Random removal of metadata tags
- Random repositioning of metadata tags to the end of the prompt, after all content-related descriptions
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TIPO-100M
TIPO-200M stagel
TIPO-200M stage2
TIPO-S00M
Architecture
LLaMA
Type
Pretrain
Pretrain
Finetune
Pretrain
Vocab Size
32013
Hidden Dim
768
1280
Attention Heads
12
20
MI.P Dim
2240
2304
3840
Hidden Layers
10
20
20
Model Parameters
100M
203M
508M
Max Learning Rate
5c-4
2c-4
5c-5
2c-4
Optimizer
Adam Cosine Annealing LR
LR scheduler
betas
0.9, 0.98
weight decay
0.01
Dataset
Coyo, GBC, Dan
GBC, Dan 5
Coyo, GBC, Dan
Coyo, GBC, Dan
Epoch
max context length
512
512
1024
1024
global batch size
1024
2048
2048
3584
Token Seen
6. 6.0240B
22.625B
18.339B
31.274B
Hardware
4 RTX3090
4 x RTX3090
4 x RTX3090
8 x H100
Training Time (wall)
22.5 hour
150 hour
270 hour
100 hour
Table Training settings for TIPO models. The datasets include CoyoHDIIM (Coyo). GBC1OM
(GBC), and Danbooru2023 (Dan). Stage 2 additionally incorporates Pixtral (Agrawal et al. 2024 to generate NI. captions from Danbooru2023 dataset.
This approach encourages the model to handle varying metadata positions and availability, while maintaining the ability to infer metadata relationships from content descriptions.
Content Tags: For tags describing image content (e.g., objects, actions, attributes), implement:
Random shuffling of tag order within the content section
Length-based truncation to meet target length constraints while preserving key content information
Natural Language: For natural language descriptions exceeding length limitations, we employ selective sentence removal, targeting middle sentences to preserve context-setting opening sentences and concluding details. This maintains coherent narrative flow while meeting target length requirements.
These augmentation strategies create more diverse training dataset that better reflects real-world prompt variations, improving the model's robustness and adaptability to different input styles and formats.
C.2 TIPO TRAINING SETTINGS AND MODEL CONFIGURATIONS
Tokenizer and Task Tokens TIPO employs vocabulary derived from L.LaMA2 (Touvron et al. 2023b) consisting of 32,000 tokens, with additional tokens (13 tokens) specifically designated for task and length control placeholders. This extended vocabulary includes task identifiers and length modifiers to ensure flexibility across different prompt types:
Placeholder Token (1 token):
</empty
>
Task Tokens (8 tokens):
< <Igen_metal [|tag_to_lonae < s|shrtt_ phoor_to_tagl>>
<I [llong_to_tagl// sooott__ooooog >>, < sttag_to_short_to__onng
Length Tokens (4 tokens):
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Tags
Tags
<LENGTHP
<LENGTH
Extended
Tags
Tags
Extended
4LENGTH <ang lo
Tags
LHENGTHH po_ooge
Finel Prompt
Extended
Extended
Final Prompt
NL
Prompt
NL
NL
sohort to_.ooooooooor
estort 30 20p long
Refined
Refined
NL
NL
Input Tags Only
Input NL Only
Input Both Tags and NL
Figure 6: TIPO inference workflow, with solid arrows denoting the primary generation steps and dashed arrows indicating alternative generation paths within the same cycle. <TOKEN> represents special tokens, with all tokens detailed in Section
< </rry_short/> < //short 
 >, < verry_ongg
Optimizer and Learning Schedule Training is performed using the AdamW optimizer (Loshchiloy 2017). with cosine annealing learning rate scheduler (Loshchilov & Hutter 2017). The optimizer parameters include 31 0.9, B2 = 0.98, and weight decay of 0.01. Maximum learning rates are adjusted per model size, outlined in Table 8
Training Configurations TIPO models are trained in multiple stages. Table 8 summarizes the configurations for pretraining and fine-tuning TIPO-100M TIPO-200M, and TIPO-500M. Both pretraining and fine-tuning was conducted on datasets like Danbooru2023 (nyanko202 2023).
GBCIOM (Hsich et al. 2024). and CoyoHD! 1M (CaptionEmporium] 2024).
Augmented Task Representation Each dataset entry undergoes random task assignment and splitting to simulate a wide range of input-output mappings, effectively increasing the dataset size. For example, single entry may contribute tasks like short to tag or tag to long, with length modifiers dynamically controlling the output verbosity. This approach ensures the model can handle diverse tasks while maintaining robust generalization.
Hardware and Time Requirements Training conducted on NVIDIA RTX3090 GPUs for smaller models and H100 GPUs for TIPO-500M. Total wall-clock training times ranged from 22.5 hours for TIPO-10OM to 270 hours for fine-tuning TIPO-200M.
Token Seen and Effective Training Non Non-padding tokens are used to measure the effective token count during training, ensuring efficiency given the short and variable data lengths. Table X details the total tokens seen per model and training stage, illustrating the comprehensive exposure diverse data entries.
C.3 TIPO INFERENCE SETTINGS
Sampling Strategy We employ a hybrid stochastic decoding strategy combining nucleus sampling (top-p = 0.95) and top-k 60 filtering. This follows standard practice in open-ended text generation, as adopted in the official Hugging Face generation examples []]] This hybrid approach maintains diversity while preserving coherence, preventing both overly deterministic and excessively noisy generations.
Inference Pipeline The TIPO inference pipeline is designed to handle various input types and scenarios, combining different tasks to refine expand both tag-based and natural language prompts. Figure T illustrates this comprehensive workflow. Our framework processes tags and natural language
Hugging Face. "Usage transformers 2.11 documentation"" 2.b.uummaai........mamccaaaaii Example text generation top-p= = 0.95 and top-k 60. o
geneetttioooninniii with XT.Net uses html
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inputs separately, allowing for specialized handling of each imput type. This flexible pipeline allows TIPO to adapt to various input scenarios, whether the user provides tags, natural language descriptions, or both. By leveraging different task combinations, TIPO ensures that tag-based and natural language prompts are optimized, resulting in more detailed and effective input for text-to-image models.
C.4 IMPACT OF MODEL SIZE ON PERFORMANCE
To analyze the impact of model scales on prompt-optimization performance, we compare TIPO-200M and TIPO-500M using a 1,000-image subsample from the COYO and GBC datasets. Results are shown in Table
Table 9: Prompt optimization performance of TIPO-200M and TIPO-500M on a Ik subsample.
Metrie
Task
TIPO-500M TIPO-200M
FDD (1)
NL-short
0.1529
0.1356
NL-long
0.1650
0.1398
Aesthetic (1)
NL-short
5.8531 = 0.7501
5,8943 = 0.7064
NL-long
5,8364 = 0.7501
5.9030 = 0.7015
Corrupt (L)
NL-short
0,2870 0.4167
0.2891 0.4189
NI.-long
0.2870 0.4150
0.2862 + 0.4151
Overall, TIPO-500M shows consistent gains in FDD and Aesthetic scores, while performance on AI Corrupt remains comparable. However, the larger 500M variant entails substantially higher computational cost without delivering proportionally greater benefits, which limits its practicality for community use; hence, all main experiments are conducted with TIPO-200M.
C.5 IMPACT OF MODEL SIZE ON INFERENCE SPEED
We conducted comprehensive speed tests of our 100M, 200M and 500M parameter models using inference pipeline described in Section 5.2 Each prompt requires two sequential generation steps. Our primary metric is average tokens generated per second, which reflects real-world task performance rather than thcoretical maximum throughput.
The evaluation was performed using Ilama.cpp (Gerganov) 2023 efficient C++ implementation that provides optimized support for various hardware accelerators, including CUDA, HIP, and Apple Metal.
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TIPO-100M
TIPO-200M
TIPO-500N
Hardware Platform
tok/sec
gen
tok/sec
gen time
tokísec
gen
MI Max (32 GPU cores)
339.4
0.66
190.0
1.23
119.4
2.02
RTX 3090
0.42
341.4
0.69
289.8
0.81
RTX 4090
742.9
0.29
454.5
0.51
359.7
0.63
Table 10: Model performance comparison across different hardware platforms. Tokens per second (tok/sec) represents the average generation speed, while generation time (gen time) shows the average time in seconds required for a complete two-step prompt optimization process.
D EVALUATION STATISTICS
In this appendix, provide statisties for the result obtained in Section
D.1 IN-DOMAIN TEST REGARDING SCENERY TAG
G7740-111
Proppiit
TPO
1.0
Aolinck Scar (ighiel Bdtial
(a) The box plot the Aesthetic Score result scenery tag test.
Comgui Scom Bocplet by (rompt
TP0
0.02
325
0.50
8.5
1.00
Compot Score lfighel Botter
(h) The box plot for the AT Corrupt Score result of scenery tag
1
The KDE plot for Aesthetic Score re- sult scenery tag test.
(d) The KDE plot for the Corrupt Score result of scenery tag test.
Figure 7: The distribution of aesthetic and Al corrupt score for scenery tag test.
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The box plot and Kernel Density Estimation (KDE) plot displayed in Figure illustrate the aesthetic scores and Al corruption scores from the scenery tag test described in Section 5.2 The analysis shows that TIPO significantly outperforms all other methods, demonstrating considerable margin of improvement.
D.2 IN-DOMAIN PROMPT GENERATION TEST
Acsthetic
GPMio-mas-soor
Proteet
Pompptittanott
TiFO-short
5.0
5,5
6.0
6.5
7.0
Amnthetic 0Migher
(a) The box plot for the Aesthetic Score result of short prompt input. Prompt
Punes
Promptstishort
stort
8.00
0.5
0.50
1.00
Compptt eligheri Better
(b) The plot for Corrupt score result short prompt input.
"olal
theas
02
(c) The KDE plot for the Aesthetic Score result of short prompt input.
(d) The KDE plot for the AI Corrupt Score result of short prompt input.
Figure 8: The distribution of aesthetic and AI corrupt score for short prompt input.
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Boxplet Prompt
nag to
Bonet
Proppitttiion
3.0
6.U
65
7.2
Andhetk (higher Betteri
(a) The box plot for Aesthetic Score result truncated long prompt input.
Grignwi-ting
GPTGo tirtton
Punkk
Promppatttiin
5.00
025
0.50
6.75
1.00
Comrup Score 0ligheris Better)
(b) The box plot for the Al Corrupt score result of truncated long prompt input.
Diib mutorr Huad
loang dihi
(c) The KDE plot for the Aesthetic Score result of truncated long prompt input.
(d) The KDE plot for the AT Corrupt Score result of truncated long prompt input.
Figure 9: The distribution of aesthetic and Al corrupt score for truncated long prompt input.
Figures 8 and display the box plots and KDE plots of aesthetic scores and Al corruption scores obtained from the In-domain prompt generation test detailed in Section F2 While the box plots reveal subtle differences in performance between various methods, the Al corruption scores provide valuable insights. Specifically, these scores indicate that implementations supported by TIPO produce more stable output images than other methods.
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D.3 OUT-OF-DOMAIN EVALUATION
Aesthatic Score Roxplor Prompt
Magicnompt
hromt
710
CON COOO
5.5
6.
b.
Aesther Score (Higher Better
(a) The box plot for the Aesthetic Score result of out-of-focus test.
GPP
Magichonpt
Pumptist
3.50
Camustt Score (Higher is Better)
(b) The box plot for the AI Corrupt Score result of out-of-focus test.
Dask hnd
Deerredenn of de
(c) The KDE plot the Aesthetic Score result out-of-focus test.
(d) KDE plot the Corrupt Score result of out-of-foce test.
Figure 10: The distribution of aesthetic and Al corrupt score for out-of-focus. test.
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GITAc-m
(a) Original Caption
(h) Prompt by GPT4o-mini
(c) Prompt by MagicPrompt
(d) Prompt by Promtist
1PF
(e) Prompt by TIPO
Figure 11: The similarity matrix for the 100 best aesthetic results generated in SD3.5-Large experiments. Off-diagonal elements of the matrix indicate the similarity between different images. A lower value for an off-diagonal element indicates greater diversity among the generated images.
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GITAC-m
(a) Original Caption
(h) Prompt by GPT4o-mini
11
(c) Prompt by MagicPrompt
(d) Prompt by Promtist
(e) Prompt by TIPO
Figure 12: The similarity matrix between 100 images worst aesthetic generated results of SD3.5- Large experiments.
Figures 1 and 12 present similarity matrices for different prompt generation methods and their corresponding aesthetic outputs on SD3.5-Large (Esser et al. 2024b). A matrix with predominantly lower similarity values (brighter appearance) indicates high diversity among generated images, while higher values (darker appearance) suggest consistent but less diverse outputs. Please refer to Table 2 in Section
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D.4 ABLATION TEST
Assthetic Score Boxplot Prompt
Rleatend
4.110
5.0
3.3
6.
7.0
Aestherk Score Hrigher s Better)
(a) The box plot for the Aesthetic Score result of NL ablation test.
2% 1999
PAR CAUS
extend
0.00
0.25
0.50
0.75
1.20
Comupt Score trigher Bettar)
(b) The box plot for the AT Corrupt Score result of NI. ablation test.
Coldideer
(c) The KDE plot for the Aesthetic Score result of NL ablation test.
(d) The KDE plot for the Al Corrupt Score result of NL ablation test.
Figure 13: The distribution of aesthetic and Al corrupt score for NL ablation test.
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Booplst by
laga-1112
2011 2013
Acsthetic (ligher
(a) The box plot for the Aesthetic Score result of tags ablation test.
Corrupt Score Bosplot by Prompt
CACC
aig estend
0.00
2
Compgt lMihhe
(b) The box plot for Corrupt Score result of tags ablation test.
Detittatio becharrs
(c) The KDE plot for the Aesthetic Score result of lags ablation test.
(d) The KDE plot for the Al Corrupt Score result tags ablation test.
Figure 14: The distribution of aesthetic and Al corrupt score for tags ablation test.
Figures 14 present the tag ablation test in the TIPO effect on the aesthetic score and AI Corrupt Score among the original tag, tag-extend and the tags TIPO. The box plot reveals that the tag TIPO is better than the original tag and the tag extend is the best. In detail, KDE plot reveals that the tag TIPO has similar performance compared with the tag extend. Both of them are better than the original tag, which indicates that the tag TIPO aspect helps control corruption and promotes the aesthetic score.
E TIPO EXAMPLE
In this section, we provide some text example TIPO's input and output.
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TIPO Format Template
User Input:
lgirl, cilorenko, naccha (mochsncc), absurdres, newest, safe
ask (askzy), solo, masterpiece,
A girl sits in cozy cafe, cradling a cup of coffee in her hand
Formatted TIPO Input for Expand Tags:
absurdres
roting: safe
style: anime-style iLlustration, digital art, character deaign, fantasy concept art quality: masterpiece, newast aspect_rotio:
target: 
 <|short_to_tagl>
short: girl sits cozy cafe, cradling cup coffee in her hand tag: 1girl, solo
Formatted TIPO Output after Expand Tags and Expand Natural Prompt:
meta: absurdzes
zating: safe
style: anime-style ilattrationn digital art, character destgn, fantasy concept quality: maaterpiece, neweat
aspect_catio: 1.0
target: <| <ltag_ta_lengl>
tag: lgirl, solo, sit: closed mouth, jewelry, long hair, looking at viewer, crossed lega, plast, table, couch, bracelet, cup, smile, tescup, indoors, blue eyes, blande hair, holding
long: A girl sita in cosy cafe, crad ing of coffee in her hand. The cate has large windows with green plants the walls and woodan table in front of her. Sehind her is stairease leading to ancther roon. The overall atmosphera of the image serene inviting.
Formatted Output for Immae 1girl, ciloranko, naccha (mochance), ningen mame, ask (askey),
solo, sitting, closed mouth, jeweiry, long hair, looking at viewer, crossed Legs, plant, table, couch, bracelet, cup, smile, Leacup, Indoore, blue eyes, hlonda hair, holding,
A girl sits in cozy cafe, cradling à cup coffee
staircase leading another The cafe has large windows with green plants on the walls and wooden table In front of har, The overall atmosphere of the image serene inviting.
User Input
TIPO Output
mastespiece, absurdres, safe
Figure 15: An example of formatted content used for training and inference in TIPO.
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TIPO Format template
User Input:
scenery humans. masterpiece, absurdres, newest.
Formatted TIPO Input For Expand Tags:
meta: absurdres
rating: safe
quality: masterpiece, nexest aapect_raatoo:
target: 

tag: scenery, na humans
Formatted TIPO Output after Expand Tags and lag_to_long task:
absurdzes
zating: safe
quality: maaterpiece, newest
aspect_catior 1.0
Lazget: <|longl> <ltgg_to_longi>
tag: scenery, nо humans, storefront, motor vehiale, road sign, power lires, plant, railing,
pot, vanishing point, outdoors, sign, potted plant, aidewalk, awning, tree, bicycle, window, railroad crossing, bush, building, utility pole, lamppost, shop, track, traffic 1ight, fence, chinese text, stairs, door, blayale baaket, town, day, streetcar (cate), lamp, road
long: A small town with variety of buildings and hauses. the sky is blue and there left
are trees
the image, background, "chinese restaurant*. the right side, there and restaurants. in front
orange
buiiding
sign
several other buildings with different types of the buildings, there appears be street with cars parked along tha road.
in the center of the illustration, we can see train cressing sigmal with two red blue sky abova it. of the traffic light pole,
lights
alse a yellow building with
and green roof. conditiening unit. the street is lined with trees and bushes, and there graffiti on the ground.
air
Formatted Output For Text-to-Image
scenecy, no humane, storefront, motor vehicle, sign, power lines, plant, railing, flower pat, vantahing point, outdoors sign, potted plant, aidewalk, awning, tree, bicycle, window, railroad crossing, bush, building, utility pole, lamppost, shop, truck, traffic light, fence, chinese text, stairs, door, blaycle basket, town, day, streecear (oafeli, lamp, road,
User Input
A small town with varfety of bu" Idings and houses. the sky is blue and there are trees in the beckground. en the left side
of the image, there is an orange building with sign that "chinase "eataurant", on the right aide, there other buildings with different types shops and restourants. in front of the buildings, there appears be street with cars parked along the road. in the center of the illustration, se can see trein cressing signal with twe red lights and a blve sky above it. there is also green roof.
yellow building
TIPO Output
masterpiecs, absurdres, safe
Figure 16: An example formatted content we used for training and inference TIPO.
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F IMAGE EXAMPLES
In this section, we present sample images from the experiments described Section 5 to visually demonstrate the improvements achieved by TIPO.
F.1 IN-DOMAIN TEST REGARD TO SCENERY TAG
Figure 17: Comparison of generated images using simple input (left) vs. TIPO-enhanee input (right) for the scenery tag
Figure 17 demonstrates the difference in output diversity between simple input and TIPO-enhanced input for the scenery tag. As observed, TIPO significantly expands the range of generated sceneries, better reflecting the variety present in the Danbooru2023 dataset (Yeh) 2024b). The left column shows results from simple input (scenery tag only), while the right column illustrates the enhanced diversity achieved with TIPO-enhanced input.
F.2 IN-DOMAIN PROMPT GENERATION TEST
(a) Short Caption
(b) TIPO-Generated Caption
Figure 18: Comparison of generated images using original input (left) vs. TIPO-enhanced input (right)
Figure 18 illustrates the differences between short captions, truncated long captions, TIPO-generated captions, and TIPO-extended captions. The "short prompt" and "truncated long prompt" used in this experiment typically consist of 1-2 sentences, resulting in reasonably good quality outputs. However, the use of TIPO to refine or extend these prompts still yields noticeable improvements in aesthetics and overall quality.
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G HUMAN PREFERENCE
X40
300
Remociue Hapooc
(a) ELO rating Illustrious
(b) Win rate matrix Illustrious
Theletical LMO Frinnt can Mit PiBlic 504 Nedicr
Avaa Comper son Mabris ad335m
(c) ELO rating on SD3.5-medium
(d) Win rate matrix on SD3.5-medium
Figure 19: ELO ratings and win rate matrices across different experimental settings comparing five prompting methods (TIPO, Promptist, Promptextend, MagicPrompt, and Original) on three evaluation dimensions
Comparison
Win Ratio (A:B)
Proportion
p-value
Significant?
Original vs. PromptExtend
45:66
0,405
0.0572
Marginally
Original vs. Tipo
28:80
0,259
< 0.0001
Yes
Original Promptist
35:64
0.354
0.0046
Yes*#
PromptExtend vs. Promptist
41:80
0,339
0.0005
Yes
Promptist Tipo PromptExtend vs. Tipo
30:101
0.229
< 0.0001
Yes
30:90
0,250
< 0.0001
Yes
MagicPrompt vs. Promptist
14:34
0.292
0.0055
Yes
MagicPrompt vs. Tipo
11:48
0.186
< 0.0001
Yes***
MagicPrompt vs. Original
15:28
0,349
0.0660
No
MagicPrompt vs. PromptExtend
19:35
0.352
0,0402
Yes*
Table 11: Pairwise win rates and statistical significance (Overall Dimension). Significance levels: p 0.05, ** p 0.01, *** p 0.001
We conducted a series of A/B tests to compare five prompt transformations, (TIPO, Promptist, Promptext, MagicPrompt, and Original(unmodiifeed) for two models, Illustrious, SD3.5-medium which is known for both core word/natural language understanding. total, we collected responses for 1,500 pairwise comparisons, from more than 20 anonymous evaluators. Each evaluation asked
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Companison WorMelosse Rañes by Term Metric (Sorted by Rater
Wy Westote hil
Daily Wistieleed
WerTeiie fares
Wyfrios Falm
41.7
ALPY 41-79
OP
Full Win- Tie-Lose plot
20.1%
14.0% $1.21
IRIN
(b) Illustrious Win-Tie Lose plot
Compatison Westlettsee Ratos Term Mutric (Sortad by Rittet
Dpellyy
73.7%
SLLAL 21.91
JAAN
JANN
WAN
TAXL
(c) SD3.5 5-edium Los-Tie-Lose
Figure 20: Win-Tie-Lose comparison across different experimental settings showing the relative performance of five prompting methods on prompt adherence, image quality, and aesthetic appeal metrics
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Image Generation Quality Survey
Clo "ertoal oo
rpuan gouneaaiin ragin ses Saleegggoo gotr
frier cpnr
ligh alficiccc apt
Lerica Pongle
wirtee lnmieo sowe,, lirm aed reciphie opsend proiiile secigaand Mear Roie
Jet and Phrec prppeinge
IMBE
Per
Denil Infrend
Aadikanig
(a) The UI survey system before submitting the choices.
Generation Quality
chi
oxphollee fitt plote lepie nutl iici hapiiaalide nalidd
Fooss detiled boclgns baterd nisss Batace shdlggh lo taga pipall durastis and day Soos, pivk masteg
ciland sacigaac diatt
Cuneil in
Aurdi
AMd
ARDHEN
AMCS 2m
AlSSS
AMOE aeegia
(b) The UI of survey system after submitting the choices.
Figure 21: Survey interface for human evaluation of image pairs, showing the evaluation process before submission (a) where users compare two images based on four metrics, and after submission (b) where the generated prompts for each image are revealed
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participants to compare two generated images labeled A and B and select which they preferred (or a tie) according to specific criteria (e.g., prompt adherence, image quality, or aesthetic appeal).
G.1 USER INTERFACE FOR HUMAN PREFERENCE EVALUATION
developed specialized survey interface to facilitate efficient and unbiased human evaluation of generated images. As illustrated in Figure 21 the interface presents evaluators with an original prompt and two corresponding images (labeled A and B) generated using different prompting methods. Before submission, users
the original prompt in the center panel while the processed prompts used to generate each image remain hidden to prevent bias.
The evaluation framework requires participants to compare the image pairs across four distinct metrics: prompt adherence (how well the image follows the original prompt), image quality (detail and correctness), aesthetic appeal (color, composition, and style), and overall personal preference. For each metric, users can select one of three options: "A is better," "A and B are equal," or "B is better."
When evaluators encounter image pairs that appear to be from different prompts or settings, they instructed to click "Refresh" to obtain new comparison. After submitting their evaluations, the interface reveals the transformed prompts used to generate each image, providing transparency about how the original prompt was modified by each method.
G.2 EXTENDED HUMAN EVALUATION.
Participants assessed each image's performance prompt adherence, image quality, and aesthetic appeal, with visually shown unmodified and image pairs. TIPO exhibited superior outcomes in all comparison settings. Notably, it attained 64.4% peak win (against MagicPrompt) under the Full scenario and 57.5% (also against MagicPrompt) under SD35-medium, emphasizing TIPO's proficiency in generating images that closely follow prompt specifications while maintaining visual coherence.
G.3 ELO RATINGS.
We computed theoretical ELO ratings from the aggregated pairwise comparisons to quantify overall performance differences among the five methods. The rating update rules based on each pair's binary outcome (win or lose), ignoring tie cases. The result is depicted in Figure 19 TIPO has secured the highest ELO rating over other models.
G.4 HUMAN PREFERENCE ELO METHOD
We computed theoretical ELO ratings from human-judged pairwise preference data to quantitatively evaluate the relative performance each prompting method. The ELO rating system, initially designed for ranking chess players, aggregates binary outcomes into numerical ratings representing comparative performance.
Pairwise Outcomes. Human evaluators assessed comparisons between methods, resulting one of three outcomes:
Method wins: assigned score of 1 for method i, and 0 for method j.
Method wins: assigned score for method j. and for method i.
Tie: assigned score 0.5 to both methods.
Conversion to ELO Differences.
Win and tie rates were converted ELO rating differences using:
Tie Rate
Adjusted Win Rate Win Rate +
2
Adjusted Win Rate
ELO Difference = 400 x log
Adjusted Win Rate
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To ensure numerical stability, extreme adjusted win rates constrained follows:
-800,
Adjusted Win Rate - 0.001
ELO Difference
+800,
Adjusted Win Rate 2 0.999
Calculating Method ELO Ratings. Final ELO ratings were determined by averaging each method's pairwise ELO differences and centering these averages around baseline rating (e.g.. 1000):
ELO Base Rating+ (Average ELO Difference for method Overall Mean ELO Difference)
Interpretation of ELO Scores. Methods with higher ELO scores consistently outperform lower- scored methods. A rating difference of 400 points corresponds to 90% expected win probability for the superior method.
G.S STATISTICAL SIGNIFICANCE
As summarized in Table 11 we conducted two-sided binomial and McNemar's tests (p <0.05) to assess the statistical significance of observed differences. The result confirms that TIPO's advantages unlikely be explained by random variation, which also supports consistent performance hierarchy: TIPO ranks highest, followed by Promptist, PromptExtend, Original, and MagicPrompt. Collectively, these findings illustrate TIPO's robust, model- effectiveness and underscore the model senei-vensttivity of alternative methods, particularly Promptext and MagicPrompt.
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G.6 SURVEY RESPONSE EXAMPLES
In this section we provided some responses of our human preference survey as reference.
shop, detailed background. background, best quality, ebsurdres. background, general, sky, night, anaglyph, masterpiece masterpiece
Image
Image B
Prompt Adherence: better
Image Quality: better
Aesthetic: betzer
Overall: is better
outdeors, animal tocus, detailed background, gradient backgroundshade, shadow, darkness, bloom, depth field bloom, depth fleid, sensitive, dark, cafe, anaglyph, scenery, depth scenery, depth of field, masterpiece
Image A
Image B
Prompl Adherence: is better
Image Quelityy A better
Aesthetic: better
Overall: better
Figure 22: Some survey responses illustrious-3.55 3.5-00s--------eee generated image with different prompt optimization method
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hightwaist headphones, background. background.
field
depth field, sensitive, transparent, sceniryy becklighting, masterplece backlighting. masterpiece
Image A
Image
Prompt Adherencc:: A and B are equal
Image Qual ity: and are equal
Aesthetic: B is better
Overall: B is better
house, entrence. 1pir), Hat, detailed background, rainbew background, background, sensitive, clessroom, feding, masterpiece
Image A
Image B
Prompt Adherence: and 8 are equal
Image Quality: A and B are equal
Aesthetic: A and are equal
Overall: is better
Figure 23: Some survey responses on Suuustrious335-vpred generated image with different prompt optimization method
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semireelistic, sceneryy animal focus, delailed background, Lwo-tone backgroundbhade, shadow, darkness, bloom darkness, bloom, depth field, general, sketch, scratch art, coffee, fading coffes, tading, masterpiece
Image
Image
Prompt Adherence: is better
Image Quality: better
Aesthetic: A is better
Overall: better
shrine, animal focus, detailed background, rainbow background. sensitive, sketch, scratch funeral, fading, muted colors, masterpiece
sketch, scratch
Image
Image
Prompt Adherence: A better
Image Quality:
Aesthetic: A and are equal
Overall:: is better
A better
Figure 24: Some survey responses on SD3.5-medium generated image with different prompt opti- mization method
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semireelitii,, 1girl, Jochpurs, crowns,, delailed beckground, greyscale with colored beckground, generall sketch general, skeltch, scratch art, oceen, feding, masterpiece
Image
B
Image Quality:
Aesthetic:
Overall:
extremely aesthetic, 1giri Jubbah, headphones, detailed background, multicolored backgroundshade. shedow, derkness shadow, darknass, generol, sketch, scratch art, coffee, transparent. sconery transparent, scenery, JPEG artifacts on purpose, masterplece
Image
Image B
Image Quality:
Figure 25: Some survey responses SD3.5-medimn generated image with different prompt opti- mization method
G.7 CONCLUSION
The extended evaluations presented here reinforce TIPO's standing as a reliable and effective prompt- optimization strategy. Its consistent performance gains under diverse model conditions highlight its potential for broad applicability, with its strong alignment with user-specified prompts, high image quality, and favorable aesthetic outcomes.
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H ABLATION STUDY ON TIPO
In this section, we investigate the effect of incorporating TIPO (Tags + Inferred Prompt Objects) across various generation settings. Our primary goal is to validate whether additional structured information (e.g., core tags and minimal spatial/contextual cues) can improve image quality, reduce artifacts.
H.1 EXPERIMENTAL SETUP
Prompt Variants To systematically analyze TIPO's contribution, we consider four types of input prompts improvement task:
1. Tag + More core words: Given an initial set of core words, generate more refined or expanded core words.
2. NL + More NL: Given a short natural language (NL) description, elaborate into a richer NL prompt.
3. Tag - (More core words + NL): Combine expanded tags with corresponding NL descrip- tion derived from them.
4. NL (More
core words): Use the NL prompt add relevant tags, forming a mixed prompt of NL plus core words.
In each case, we compare the baseline prompts (without TIPO cues) against prompts incorporating TIPO's structured, tag-based critical information and minimal spatial hints.
Data Preparation We start by randomly sampling core words from word table to represent diverse range topics (e.g., objects, environments, descriptors). Additionally, for each word set, we generate corresponding short NL sentence using a compact language model (GPT40-mini). Overall, the six prompt variants are tested on 4,000 images, ensuring a balanced comparison.
Inference Procedure Prompts are fed into image-generatee generation pipeline under identical model settings (classifier free guidance, sampler, steps, etc.), using the v-parameterized variant of Illustrious (P.r al., 2024). We focus how TIPO modifications alter the generation outcomes and whether they introduce additional computational overhead.
H.2
EVALUATION METRICS
Comcat Sesemutirn
Aestrel Scre Dombetee
(a) Corrupt Score Distributions
(b) Acsthetic Score Distributions
Figure 26: bide-by-side comparison of Corrupt (left) and Aesthetic (right) score distributions across prompt types.
Aesthetic Score We employ an off-the-shell aesthetic predictor to estimate image quality. In the following paragraph, we discuss the model's bias.
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AI Corruption Score Using an automated corruption detection model, measure generation artifacts, such as distorted objects and unnatural shapes. Higher scores imply cleaner, more coherent outputs.
H.3 RESULTS & DISCUSSION
Impact on Aesthetics Figure 26b shows that TIPOOenhancee prompts generally achieve higher aesthetic scores than their non- noP- counterparts, albeit with some variance. Notably, we observe correlation between wider color ranges and higher aesthetic scores discussed in Figure 27 suggesting a bias toward more colorful or varied compositions.
Improvements in Corruption Score As shown in Figure 26a TIPO- based prompts yield signifi- cantly lower corruption scores, indicating fewer artifacts. We hypothesize that the additional spatial and contextual details encoded via TIPO help the model place objects more consistently.
(a) Saturation Aesthetic Score
(h) Binned Saturation vs. Aesthetic
Saturation vs. Corrupt Score
Figure 27: Scatter plots (left and right) and binned analysis (middle) showing the relationship between saturation and image metrics. We find moderate positive correlation between saturation and aesthetic score (Pearson = 0.2821), particularly at lower saturation ranges, based on 24k samples. However, saturation shows no notable correlation with corrupt score (Pearson r = 0.0125).
H.4 SPEED TEST AND OVERIEAD ANALYSIS
Inference Speed A key concern for production pipelines is whether TIPO generation imposes substantial time overhead. We benchmarked prompt-generation inference on four smaller models, excluding any large proprietary LLMs. As illustrated Table [2] the additional TIPO-related computation remains well below the image-generation time. Hence, even in a synchronous pipeline, TIPO prompt expansion does not constitute a bottleneck.
Table 12: Speed Test Results for TIPO and Other Prompt Methods
Method
Model/Config
Runs
Avg. Time (s)
Std. Dev. (s)
LLaMA-500M
500
1.4207
1.0730
TIPO
LLaMA-200M
200
1.0306
0.8982
LLaMA-100M
200
1.0078
0.9394
PROMPTIST
GPT2-125M
1000
1.4593
0.2857
PROMPTEXTEND
GPT2-125M
1000
1.3849
0.2151
MAGICPROMPT
GPT2-125M
1000
1.1398
0.4043
Memory Footprint We also confirm that TIPO's overhead in terms VRAM usage minimal
(e.g., 0.5 GB for TIPO-200M and < 1.5 GB for TIPO-500M) with the quantization supported by llama.cpp. The practical adoption TIPO in pipelines has shown no critical memory concern,, which aligns with our measurements.
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Training Costs To contextualize the training cost of TIPO, we compare its training time with reinforcement learning (RL)-based prompt optimization methods, based on the reported settings in their original papers. While it is technically difficult to reproduce RL-based methods our hardware (4xRTX 3090), their reported GPU-hour budgets provide an approximate reference for scale and efficiency. The comparison is summarized in Table 13
Although TIPO requires more total GPU-hours than PAE, it is trained on over 30 million prompts-Iwo orders of magnitude larger than both Promptist and PAE. After normalization, TIPO achieves the lowest cost per 1k prompts, demonstrating strong scalability. It is also worth noting that Promptist and PAE rely on reinforcement learning with external T2I rollouts. Even for SD1.5, each rollout takes roughly five seconds, and the cost increases dramatically for larger models such as SDXI,, SD3, or Flux. By contrast, TIPO requires no rollouts, so training cost scales linearly with corpus size and remains independent of the target T2I model.
Table 13:
Training cost comparison between TIPO and RL-based prompt optimization methods. GPU-hours per 1k prompts are normalized for fairness.
Method
#Params
#Prompts
#GPUs
GPU-h
GPU-h /1k
TIPO
200M
30,000k
4xRTX 3090
1,680
0.056
PAE
125M
450k
1xA800
90
0.20
Promptist
125M
90k
VX0000
(SFT),
63
0.70
V20 (RL)
H.S CONCLUSION OF ABLATION
Our experiments suggest that TIPO (1) consistently lowers Al corruption artifacts, (2) can boost aesthetic scores, and (3) remains computationally inexpensive. The improvements in metrics support the viability of TIPO prompts for real-world image-generation tasks. In short, a concise natural language prompt with core tag-based critical information appears to be an effective, suggested form for most use cases.
I TOPIC DISTRIBUTION VISUALIZATION
Latent Dirichlet Allocation (LDA) Blei et al., 2003) is a generative probabilistic model for topic modeling (Jelodar et al. 2018), which assumes that each document is mixture of topics, with each topic represented by distribution over words. LDA uncovers hidden thematic structures by analyzing word co-occurrence patterss, while methods like TF-IDF and TextRank (Mihaleea & Taraul 2004) enhance its ability to extract meaningful insights from large textual datasets. We implemented multi-stage topic modeling and clustering methodology using LDA to extract varying numbers of topics (20, 30, 50, and 100) from the corpus. This approach focuses on identifying significant representative words while filtering out stop words and irrelevant terms to ensure meaningful topic classification.
We empirically assessed whether the resulting topics were sufficiently large and diverse by employing multi-level topic analysis. This iterative process mitigates potential challenges such as substantial topic overlap. which can diminish distinctiveness when extracting large number of topics (Stevens et al. 2012)
To address the potential overlap and further assess the diversity and meaningfulness of the topies, we performed secondary clustering (Zhao & Karypis, 2002). We grouped the initially extracted topics into five major clusters using k-means clustering. We evaluated the clustering performance by calculating the inertia (Sum of Squared Distances) (Hartigan et al. 1979), shown in Table 14 [5] and
16 Since the topics have already been filtered for meaningful content, higher inertia value indicates greater diversity among the clusters, reflecting a broader range of valid and meaningful topics across the dataset. This two-tiered approach allows for a more nuanced analysis of topic diversity and ensures the robustness of the topic modeling against meaningless word groupings.
48

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Published as a conference paper at ICLR 2026
Size
MagicPrommpt
GPT4o-mini
Promptist
TIPO
Run 1
Run 2
Run 1
Run 2
Run 1
Run 2
Run
Run
20
170.78
137.94
1078.38
204.71
125,90
144.59
1037.44
271.77
30
461.79
417.74
1758.40
736.74
327.48
195.13
1323.88
512.07
50
829.68
730.73
861.15
1036.33
400.90
373.74
823.51
959.18
100)
1656.74
1245.60
1987.63
1628.32
877.36
657.14
1622.79
1777.61
Table 14: Inertia for COYO-Dataset inference, higher is better
Size
MagicPrompt
GPT4o-mini
Promptist
TIPO
Run
Run
Run
Run 2
Run
Run 2
Run 1
Run 2
20
184.53
352.16
244.79
246.15
125.47
198.94
278.56
211.98
30
452.01
566.74
505.56
441.34
204.28
328.70
372.07
471.28
50
571.77
895.47
1227.30
990.17
438.89
313.48
737.65
788.41
100
1291.60
1742.36
1675.41
1550.32
631.61
628.78
1573.47
1855.90
Table 15: Inertia for GBC-Dataset inference, higher is better
We attach a simple visualization of topics in scenery prompt generation, with topic n=100, cluster k=5,
49

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Published as a conference paper at ICLR 2026
Size
MagicPrompt
GPT4o-mini
Promptist
TIPO
20
60.82
734.52
210.60
139.29
30
275.76
1141.77
415.95
355.20
50
630.50
826.29
722.75
1002.36
100
2026.39
1879.08
802.93
1883.70
Table 16:
Inertia for Scenery extend inference, higher is better
(a) GPT-4o-mini
(b) MagicPrompt
(c) Promptist
(d) TIPO
Figure 28: Topic visualization for scenery prompt generation. A wider spread indicates a greater diversity of generated topies.
6+girls
2boys
2girls
5girls
book
3boys
3d
bag,
architecture
4boys
6+boys
air conditionen
cloud
scenery
artist name
Agirls
humans
Lother
day
blue sky
abandoned
2others
animal
bird
Igirl
arch
bridge
1boy
6+others
3girls
buildings
aircraft
Figure 29;
This visualization represents a filtered subset posts from the Danbooru2023 dataset, centered on the 'scenery" tag. The network graph is ego network (depth 1), which includes only nodes directly connected to the 'scenery' tag. To refine the data and focus on meaningful associations, uncommon tags with fewer than 10 occurrences were excluded. The analysis, conducted using Gephi, focuses on nodes with a degree greater than 600 to highlight critical components. Nodes are color-coded by modularity class by Fast Unfolding Algorithm (Blondel al. 2008), revealing clusters of closely associated tags. Node size reflects Eigenvector CentralityBonnccchh 1972), emphasizing highly connected and influential tags within their network.
50

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Published as a conference paper at ICLR 2026
J DISCUSSION AND FUTURE WORK
Despite the promising performance demonstrated by TIPO, several limitations and future directions remain open for further study.
Distribution Dependence and OOD Generalization. The optimization behavior of TIPO inher- ently depends on the distributional bias of the open text-image corpus used for training. As result, it may exhibit unstable behavior on extremely rare stylistically unconventional prompts. Besides, when applied to out-of-distribution (OOD) T2I models whose training data or prompting conventions deviate significantly from LAtON-style distributions, aesthetic performance may degrade slightly. In addition, our current OOD evaluation uses GPT-4o-mini aniini-geeerate prompts, which are high-quality but do not fully represent real- world user queries, thus limiting external validity. Addressing the generalization to unseen models and long-tail prompts remains an important direction for future work, which can be pursued from complementary perspectives: improving the model's generalization capacity via stronger backbones and domain-divers, fine-tuning, and enhancing evaluation through large-scale collection of authentic user prompts and cross-model benchmarking.
Stronger Backhone Initialization. Our current implementation trains mid-sized LLaMA variant from scratch. Future work could instead initialize from stronger open-source LLM backbones and fine-tune them on T2I corpora, potentially improving robustness on long-tail and domain-specific distributions. This could also mitigate failures on small or highly biased datasets by leveraging more general linguistic priors.
Model-Specific Adaptation and Style Variance. For models that require structured or JSON- formatted prompts. TIPO can be combined with a lightweight adapter or fine-tuned on small set of model-specific data. Extending TIPO with such adaptation modules could better accommodate systems whose prompt syntax diverges from mainstream diffusion models. In the longer term, TIPO can also serve backbone integrated with RL-bbased refinement for model-specific alignment.
Personalization and Style Preservation. The current TIPO is a general- purpose optimizer and does not incorporate level stylistic conditioning. Building prior personalization techniques such as LoRA (Hu et al., 2022). future work could explore lightweight adapters or online learning mechanisms that track user preferences and maintain project-level stylistic consistency, enabling personalized and context-aware prompt optimization.
Image-Aware and Fredback-Driver Refinement. TIPO currently operates purely in the text domain without utilizing generated images or user feedback. A promising extension is to incorporate vision- models like Qwen3- VI. (Bai et al., (023) for image-aware refinement, allowing iterative refinement with optimized prompts, visual outcomes, and user instructions. However, such integration requires non-trivial data curation and training pipelines, which we leave for future work.
Scaling Behavior and Model Capacity. From 200M 500M parameters, TIPO continues to yield improvements in FDD and Aesthetic scores. Due to limited compute resources, we could not explore larger configurations observe scaling saturation. A systematic study of TIPO's scaling behavior and architectural variants would be a valuable direction for future research. Such analysis could reveal scaling laws unique to prompt optimizers and guide practical model sizing for future deployments.
K DISCLOSURE OF LLM USAGE
We used GPT-5 only polish writing by improving the readability and grammar correctness. No LLMs were used in the main contributions of this work, such as ideation, experiment design, or result analysis.
51


Reviews:
[{"review_id": "HVjM1877Bm", "reviewer": "Reviewer_fBC3", "summary": "In this paper, the authors propose a new method for prompt rewriting for text-to-image generation. They design a specific format to structurally combine natural language and tag-based prompts and implement a pre-sampling algorithm that progressively refines arbitrary, coarse user input into organized, fine-grained prompts by training LLMs. They evaluate their models against prior baselines as well as SOTA LLMs on a large variety of text-to-image models as well as native multimodal models using standardized metrics and human preference survey.", "questions": "From the qualitative examples, it seems like the diversity of the images should be improved after rewriting. However, this is not reflected with the Vendi scores. Do the authors have an explanation of why this may be the case?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. This paper has very comprehensive experiments, especially the range of text-to-image models and native multimodal models that they test with. And the fact that it is effective even for models with self-refinement capabilities also shows good practical applicability of their method.\n2. ELO rating is an interesting and creative way to showcase human evaluation comparison results among multiple models. I don’t think this is a standard metric for image generation evaluation yet, but I think it should become one.\n3. The description of their training recipe is also very clear and easy to follow, making the method also very adoptable.\n4. The inference speed is very fast.\n5. The models are trained with relatively small GPUs in a relatively short amount of time, which makes the method resource efficient.", "weaknesses": "1. The improvement on benchmarks that TIPO brings is not very consistent across the board and is sometimes very marginal.\n2. Mild writing suggestions: Section 3 does not really provide much information and is not very well connected to the rest of the paper. Given how packed the remainder of the paper is, I would suggest either shortening that section or removing it entirely and just explaining the notations when using them later on.\n3. Minor: there are some inconsistencies in citation styles in the first paragraph of the introduction.", "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": "OsmAdL0FVD", "reviewer": "Reviewer_TMB7", "summary": "TIPO (Text-to-Image Prompt Optimization) proposes an efficient approach to automatic prompt enhancement for T2I models. Instead of fully rewriting user inputs with a large LLM, TIPO uses a lightweight, multi-task pretrained language model to perform text presampling before image generation: it retains the original user prompt and appends a structured, detail-rich continuation that better matches the text distribution T2I models are actually trained on.", "questions": "- Since a single TIPO model is used for multiple T2I backbones (rather than training one optimizer per T2I model), does this create subtle distribution-mismatch issues for models whose caption/style format differs from the main training corpus?\n- Does the method exhibit a scaling law? In other words, if we move from the 200M model to larger variants, do we see monotonic gains in fidelity, aesthetic score, and OOD robustness — and where is the efficiency/quality turning point?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper’s key strength is conceptual clarity: it treats “bringing the prompt back to the T2I training text distribution” as the central objective. To achieve this, it adopts a lightweight model that does prefix-preserving, suffix-expansion prompt optimization, which (i) avoids the off-topic drift often seen in full LLM rewrites, and (ii) is cheaper, faster, and more deployable than RL-based prompt optimization. The experimental section is also solid: it covers in-domain and out-of-domain settings, multiple T2I backbones, and evaluates with FDD, Aesthetic Score, AI Corrupt Score, Vendi, plus human preference, so the claimed gains are reasonably well supported.", "weaknesses": "The method is still distribution-dependent: if the user prompt is very niche, domain-specific, or stylistically unusual, the expanded prompt may not be reliable. Once the prompt is made more detailed, the generation space narrows, and Vendi indeed drops in some settings. For closed-source or stylistically distant T2I models, the “distribution-aligned” expansion can sometimes misfire, leading to slight aesthetic regressions. Finally, the current TIPO is a generic optimizer — it doesn’t condition on user profile, project domain, or target style, so it cannot precisely do “expand this, but keep my style.”", "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": "Oy9firTMet", "reviewer": "Reviewer_LKuZ", "summary": "The proposed TIPO is a prompt optimization method for text-to-image (T2I) models that leverages a lightweight pre-trained model to automatically expand users' simple inputs into detailed and semantically rich prompts. The core idea is to transform user input prompts into prompts that align with the text distribution of T2I model training data, thereby significantly improving visual quality, coherence, and detail while preserving the original intent. The experiments report that prompt refinement can be performed in a lightweight, fast, and general manner across various evaluation metrics such as FDD and Aesthetic Score.", "questions": "1. Can you add metrics that directly measure text distribution alignment (e.g., embedding distance from training data or perplexity)?\n\n2. While diversity improves in OOD environments, do you have any causal analysis or improvement strategies regarding the relative performance differences with other baselines?\n\n3. Can you provide a quantitative comparison of training computational costs and resource requirements (GPU memory, training time, etc.) with RL-based methods such as Promptist and PAE?\n\n4. Can you add evaluation results using automatic metrics that directly measure text-image alignment, such as CLIPScore or GenEval?# Review of \"TIPO: Text to Image with Text Presampling for Prompt Optimization\"", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The implementation approach, which uses a LLaMA-based 200M parameter model for pre-training and does not depend on large-scale LLMs or reinforcement learning, is attractive from a practical standpoint. Additionally, it does not involve fine-tuning of T2I models.\n\n- For tag-based prompts, TIPO achieves the best FDD (0.2282), and for natural language-based (short) and truncated long prompts, it demonstrates the best or second-best performance in Aesthetic Score and AI Corrupt Score with stable performance.\n\n- Beyond Stable Diffusion family models, performance improvements are confirmed on diverse T2I models including FLUX.1-dev, Omnigen2, Lumina-2, and HiDream-I1. The fact that TIPO is effective even for models with undisclosed training data demonstrates the high generalizability and robustness of the proposed method.\n\n- While conventional methods indirectly optimize prompts using reward models, the finding that better prompts can be created simply by directly aligning with the data distribution learned by T2I models, as in the proposed method, provides an interesting insight.", "weaknesses": "- The verification of \"distribution-aligned\" is indirect. While claiming to \"align with T2I training distributions,\" the paper does not directly measure the distance of text distributions themselves (e.g., KL divergence of embedding distributions or perplexity differences). The alignment is primarily inferred from image-side metrics (such as FDD) and human comparisons, lacking direct measurement of text distribution.\n\n- In OOD settings, while diversity (Vendi Score) improves significantly, there is an issue that GPT-generated prompts are accurate but lack diversity. Although TIPO optimization adds additional details that harmonize with the original themes and significantly improves the diversity of generated images, the relative positioning of aesthetic scores and corruption rates compared to some baselines should be explicitly stated as a limitation of generalizability.\n\n- While the paper presents inference time cost comparisons (Table 4 showing up to 29.4% speedup), and Table 6 shows training settings for each TIPO model (GPU types, training time, datasets), there is insufficient direct quantitative comparison with RL-based methods such as Promptist and PAE regarding training costs. Explicit comparison is desired for fair evaluation.\n\n- The adopted metrics are primarily image-centric, such as Aesthetic Score, AI Corrupt Score, Vendi Score, and FDD, and do not include automatic metrics that measure text-image alignment such as CLIPScore or GenEval. There is insufficient quantitative evidence to support the claimed \"stronger text alignment.\"", "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": "SsYRf5U8M9", "reviewer": "Reviewer_TZN3", "summary": "TIPO (Text-to-Image Prompt Optimization) is an efficient framework designed for the automatic refinement of simple user inputs into rich, detailed prompts for text-to-image generation. The core intuition is to align the optimized prompt with the text distribution used in text-to-image model training. Compare with using expensive LLM rewriting or reinforcement learning approach, TIPO trains a lightweight multi-task language model, employing a pre-sampling mechanism to expand the original user prompt. This multi-task training includes pretext tasks for handling both tag-based and natural language inputs. Experiments demonstrate that TIPO achieves superior image quality, stronger text alignment and a 62.8% human preference win rate against strong baselines, while also providing up to a 29.4% runtime efficiency improvemen.", "questions": "What sampling method do you use, top-p or greedy decoding?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The core intuition is that optimal prompts must align with the large-scale text distributions used to train T2I models, which can reduce the mismatch between the training and inference. It is novel and straightforward. Moreover, the method is designed to be universal, leveraging its large-scale curated corpus of over 30 million text descriptions to ensure compatibility across various T2I models.\n\n2. The method employs a lightweight language model to expand the use prompts. This computational efficiency is a major practical advantage over resource-intensive LLM-based or RL-based methods.\n\n3. The experimental results are solid. It is conducted on both in-domain and out-of-domain tasks, which shows that the method is general.", "weaknesses": "1. The limitation of this work is that it needs the large-scale open-sourced text-to-image training data to make sure that prompt expanding is robust. But some current models are trained use higher-quality close-sourced data, often containing some synthetic data. It is impossible  to get the reasonable data distribution of these models, making the proposed method ineffective.\n\n2. The framework does not consider any feedback from the generated images, which means that it cannot adaptively improve the prompts according to user feedback or model feedback.", "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)