VisualPrompter: Semantic-Aware Prompt Optimization with Visual Feedback for Text-to-Image Synthesis
Authors: Shiyu Wu
Mingzhen Sun
Weining Wang
Yequan Wang
Jing Liu
Venue: ICLR 2026 Poster (2026)
Keywords: prompt engineering
image generation
diffusion model
text-to-image synthesis
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A novel training-free prompt engineering framework that refines user inputs for better productions.

Abstract: The notable gap between user-provided and model-preferred prompts poses a significant challenge for generating high-quality images with text-to-image models, compelling the need for prompt engineering.
Current studies on prompt engineering can effectively enhance the style and aesthetics of generated images. 
However, they often neglect the semantic alignment between generated images and user descriptions, resulting in visually appealing but content-wise unsatisfying outputs. 
In this work, we propose VisualPrompter, a novel training-free prompt engineering framework that refines user inputs to model-preferred sentences. 
VisualPrompter utilizes an automatic self-reflection module that identifies absent concepts in the generated images, followed by a target-specific prompt optimization mechanism that revises the prompts in a fine-grained manner. 
By deconstructing prompts, introducing new elements at the atomic semantic level, and then reassembling them, our framework is able to maintain semantic consistency and integrity throughout the optimization process.
Extensive experiments demonstrate the effectiveness of VisualPrompter, which achieves new state-of-the-art performance on multiple benchmarks for text-image alignment evaluation. 
Additionally, our framework features a plug-and-play design, making it highly adaptable to various generative models. 
Our code is available at https://github.com/teheperinko541/VisualPrompter.

Published as a conference paper at ICLR 2026
VISUALPROMPTER: SEMANTIC-AWARE PROMPT OP- TIMIZATION WITH VISUAL FEEDBACK FOR TEXT-TO- IMAGE SYNTHESIS
Shiyu Wu 112.3, Mingzhen Sun*1.3, Weining Wang1.3, Yequan 12.4 Jinng Liu Institute of Automation, Chinese Academy of Sciences Beijing Academy of Artificial Intelligence University of Chinese Academy of Sciences Peeking University wushiyu20220ia en, tswwangyequan gma com, j1 jliu@nlpr. ac.ac.cm
ABSTRACT
The notable gap between user-provided and model-preferred prompts poses sig- nificant challenge for generating high-quality images with text-to-image models, compelling the need for prompt engineering. Current studies on prompt engi- neering can effectively enhance the style and aesthetics of generated images. However, they often neglect the semantic alignment between generated images and user descriptions, resulting in visually appealing but content-wiss unsatis- fying outputs. In this work, we propose VisualPrompter, novel training-fre free prompt engineering framework that refines user inputs to model-preferred sen- tences. VisualPrompter utilizes automatic self-reflection module that identifies absent concepts in the generated images, followed by a target-specific prompt optimization mechanism that revises the prompts in fine-grained manner. By deconstructing prompts, introducing new elements at the atomic semantic level, and then reassembling them, framework is able maintain semantic con- sistency and integrity throughout the optimization process. Extensive exper- iments demonstrate the effectiveness of VisualPrompter, which achieves new state-of-the-art performance on multiple benchmarks for text-image alignment evaluation. Additionally, framework features plug-and-play design, mak- ing it highly adaptable to various generative models. Our code is available at httpss/ssttl....ommmm/eeemo/////
INTRODUCTION
Text-to-image (T2I) generation task requires generating realistic images that consistent with tex- tual descriptions. Recently, diffusion-basee generative models (Ho et al.] 2020 Rombach et al.] 2022) have demonstrated remarkable capabilities in generating vivid images from textual descrip- tions. However, they still face challenges in correctly representing key concepts within user inputs, as shown in Figure This issue arises from the significant discrepancy between user input prompts and the prompts preferred by the models Hei et al. (2024). To be specific, novice users often provide brief, coarse-grained descriptions, whereas models tend to perform better with detailed, fine-grained prompts, as such data are commonly used during training. Thus, T2I models may struggle to gener- ate satisfactory results when directly fed with user inputs.
Given that well-crafted prompt can significantly enhance the quality of generated images (Brown (et al. 2020), researchers have explored prompt engineering to automatically refine user input prompts Hao et al. 2023 Rosenman et al. 2024). For instance, Best Prompts (Pavlichenkoo & Ustalov 2023) simply appends a few empirically effective keywords to improve image quality. BcautifuPromptt (Cao et al. 2023) finetunes large language model to serve prompt engineer and incorporate reinforcement learning to leverage visual feedback. Despite effectiveness, these methods suffer from three major limitations. First, current studies T2I prompt engineering (Hao) ct al. 2023 Rosenman et al. 2024) primarily focus on enhancing the style and aesthetics of gen-
"Equal Contribution.
1 "Coresponning Authors.

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beeearr reading books
backsplesh Hedd modern
A whilless half-emply coffee
recasinn helmet ridea rusty matorbika through streets, with doge following supplies
Missing concept
Weak concept
Wrong concept
S0XL
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Januss-poo
(a) Typical failures of generative models
(b) Same prompt different incorrect ways
Existing T2I generative models often fail to correctly draw key concepts within the pro- vided prompts. Different models also fail differently on the same prompt.
Figure 1:
erated images, neglecting or even compromising the alignment between texts and images. Second, most works (Liu & Chilton| 2022 Pavlichenko & Ustalov 2023) apply similar modification to all prompts, lacking fine -ieeggri case-specific adjustments tailored to individual inputs, resulting in limited performance improvement. Third, existing methods exhibit limited generality (Cao al. 2023 Rosenman et al. 2024). as they are typically designed for a single specific diffusion model. However, as illustrated in Figure [b] different models have varying prompt preferences and may interpret the same prompt in different incorrect ways, making these methods difficult to extend to other models. These limitations highlight the importance of leveraging the outputs of generative models as model-specific feedback for effective optimization.
In this paper, we introduce VisualPrompter, a novel and training-free framework that provides fine- grained, case-specifie, and optimization target, our model incorporates a DElf-REftootion module (SERE) to identify the miss- ing concepts that are included in the input prompts but not reflected in the generated images. SERE breaks down the prompts into atomic concepts using a large language model and verifies each concept reflected in the generated images using a Visual-Language Model (VLM). Subsequently, with the missing concepts as targets, a Target-Specific Prompt Optimization module (TSPO) is em- ployed refine the user prompts. By enriching the missing concepts while preserving the existing concepts, TSPO ensures that the refined prompts are both model preferree and semantically faithful the user's original intent. Furthermore, we introduce another LLM incorporate appropriate aesthetic keywords into sentences, thereby improving the visual quality of optimized images.
optimization for distinct user inputs. To make clear the
Notably, by breaking down the user prompts into fine-grained concepts, our method allows case- specific detailed prompt optimization, which is not only more interpretable but also more power- ful. This capability enables the model to assess key attributes like objects, properties, and rela- tions, thereby refining and expanding vague fragmented user inputs into well-structured, model- preferred prompts. Moreover, our method allows different prompt optimizations for different dif- fusion models according to the distinct answers based their generated images. In this way, VisualPrompter can be compatible with various generative models, obtaining a universal and robust performance improvement.
Experiments show that VisualPrompter outperforms prior prompt engineering works on two text- image alignment evaluation benchmarks, demonstrating the effectiveness of our approach. Images generated from texts optimized by VisualPrompter also achieve higher CLIP Score (Hessel et al.] 2021) than those produced by previous prompt engineering methods. Besides this improved mantic fidelity, our optimization also enhances the aesthetic quality, contributing to more visually appealing results. Furthermore, our model exhibits notable performance improvements across vari- ous diffusion models, highlighting its extensive applicability and adaptability.
Our contributions are summarized follows:
We propose an innovative text-to-image prompt engineering method, named Visual- Prompter. Our approach is capable of generating prompts that effectively align with both model preferences and user intent.
We propose a deedback-driven optimization system that leverages the visual feedback from the VLM to strategically enhance outputs from diverse generative models.
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Our method operates the atomic semantic level to analyze and refine the prompt, fully preserving its original meaning while incorporating appropriate new content.
Extensive experiments demonstrate the effectiveness of our VisualPrompter, which signifi- cantly outperforms current state-of-the-art prompt engineering methods in multiple bench- marks.
RELATED WORK
2.1
TEXT-TO-IMAGE SYNTHESIS
Diffusion models (Ho et al. 2020 Song al. 2021) have recently attained unprecedented success in image synthesis. These models generate images from random noise through a series of denoising steps, achieving high visual fidelity and diversity. The introduction of fassiffer-free guidance (Ho & Salimans| (022) facilitates the generation of images from brief text descriptions by leveraging text encoders such as CLIP (Radford al. 2021 making text-to-image synthesis possible within the diffusion framework. Another significant innovation is the Latent Diffusion Model (LDM) (Rom- bach et al. 2022). which implements the diffusion process latent space. LDM is capable of generating images with exceptional visual fidelity, while significantly reducing the required compu- tational resources. Furthermore, the emergence of open-source diffusion models (Esser al. [0224] Podell et al. 2024) has brought text-to-image generation into the public spotlight. Consequently, well-designed prompt engineering framework can be immensely beneficial for novice users.
2.2 PROMPT ENGINEERING
Prompt engineering aims optimize the interaction between humans and AI systems by crafting
effective input prompts that elicit desired outputs (Liu et al., 2023; Sahoo et al. 2024). In the field of text-to-image generation, prompt engineering involves carefully selecting and combining phrases to achieve high-quality and satisfactory synthesized images. Early studies (Liu & Chilton
2022 Pavlichenko & Ustalov 2023) have focused on automatically searching for keywords that
significanlly influence the style and quality of generated images through mining techniques. With the rapid advancement of large language models, several studies (Cao et al. 2023 Wang et al.] 2023a] Rosenman et al. 2024) have explored their utilization as prompt engineers, particularly through fine-tuning and reinforcement learning. These models are capable of producing detailed and varied content prompts, based short phrases provided by users.
To achieve precise control, other methods (Brade et al. 2023 Feng et al. 2024) focus on the inter- action with humans. PromptCharm (Wang al. 2024b) provides convenient attention adjustments for the keywords within prompts. Prompt Expansion (Datta et al. (024)) outputs a set of expanded text prompts and images, providing users with multiple optimization directions for selection. Al- though these methods can improve the visual quality of generated images, they often neglect the semantic consistency with usov-provided descriptions, which significantly limits their practicality in real-world scenarios.
3
VISUALPROMPTER
In this section, we present our VisualPrompter in detail, which is a semantic-aware, plug-and-play prompt engincering method for the text-to-image generation task.
3.1 OVERVIEW
The target of our VisualPrompter is to automatically optimize user input prompts into model- preferred prompts. The principal strength of our approach stems from its atomic semantic-level prompt optimization, which offers more precise and reliable control over semanties compared to direct expansion by models. Figure eegure2ilinn illusstraee the overall framework of our proposed method.
module (SERE) is employed identify the specific aspects that require enhancement. It analyzes the image generated by the diffusion model based on the input text. By thoroughly evaluating the semantic information in the prompt through question
Given input prompt,
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SEVREitacionn (SERE)
Target Specitt Pronyt Optiniatioon (TSP0)
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Figure 2: The refinement pipeline of our VisualPrompter. The optimization starts with LLM-driven VLM-verified semantic evaluation, followed by LLM--cntrolled concept expansion and sentence composition. Our approach emulates the chain-of-thought reasoning in human prompt refinement. All utilized models not require any extra training.
generation and answering, can effectively identify the missing concepts. Notably, the missing concepts are those textual descriptions that are included in user inputs but are not reflected in the generated images. To obtain prompts that remain faithful to user inputs while aligning with model preferences, we employ Target-Specific Prompt Optimization module (TSPO) to refine the user prompt based on the identified missing concepts. TSPO first strategically enriches the attributes of the missing concepts to simulate model-preferred patterns. It then combines these expanded atomic concepts to form syntactically complete and semantically fluent result.
3.2 SELF-REFLECTION MODULE
Our self-reflection module utilizes multi-step reasoning to obtain explicit visual feedback by eval- uating the generated images against sentence-derived atomic concept questions. This mechanism ensures fine-grained evaluation of the alignment between textual descriptions and visual content, effectively pinpointing discrepancies where the generative model fails to capture specific aspects of the user input. An example of this fine-grained evaluation shown in Figure 2
Question Generation. For detailed question generation, we adopt Davidsonian Scene Graph (DSG) (Cho et al., 2024) to identify atomic concepts within the input prompts. A DSG is a structured rep- resentation comprising set of general questions. Each question corresponds to an atomic concept in the given prompt. These questions are interconnected through directed edges, which capture the entailment dependencies among them. We employ a Large Language Model (LLM) to generate the DSG, and its detailed construction process is elaborated Appendix A
Question Answering. Upon completing the construction of DSG, employ Visual-Language Model (VLM) as the visual question answering model to evaluate the generated image against the questions in DSG. The VLM is instructed provide binary response for each question, deter- mining the corresponding concept appears in the generated image. The dependency relationships within the DSG are utilized for question pruning, process that eliminates the need for redundant assessments. Specifically, a particular concept is judged to be missing, all subsequent concepts dependent on it are automatically regarded as missing well. The answers enable comprehensive and fine- mee-ggained semantic analysis of the generated images, which will be employed as semantic- aware information to refine the prompt.
3.3 SARGIT-PPECIICC PROMPT OPTIMIZATION
With these identified missing concepts by our self-reflection module, can perform targeted op- timization of the input texts, reducing unnecessary modifications and expansions. We introduce a novel, training- free optimization approach that effectively manages prompt adjustments.
Prompt Regeneration. Rather than directly rewriting the sentences, VisualPrompter first utilizes an LLM to expand the generated atomic concepts based the responses to the questions from the SERE module. This expansion process specifically targets the identified missing concepts, enriching them with additional detail such as attributes, actions, and spatial relationships. Our observations
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reveal that augmenting the missing concepts can nudge the user input toward the distribution of preff-rreeerred prompts, providing a alug-and-plan lever for optimizing various generative models. These additional elements also expressed as atomic concepts, ensuring consistency and clarity in representation. Notably, if no missing concepts are detected, no further modifications will be made.
Subsequently, VisualPrompter generates new prompt from the expanded atomic concepts with an LLM. The original prompts and concepts are also provided to guide the synthesis, ensuring that the generated prompt remains contextually aligned with the initial input. The TSPO module retains textual descriptions that are already accurately reflected in the generated images to avoid unneces- sary modifications, thereby preserving original user intent. By adopting this two-step optimization methodology, our framework is able to produce more accurate and reliable prompts.
Prompt Decoration. To further enrich the diversity and aesthetic quality of the generated content, we augment the optimized results with several aesthetic keywords that are automatically selected by an LLM. By providing example keywords such as high-quality, detailed, and fantasy, we leverage the LLM to automatically generate rich and diverse keywords that do not conflict with the input sentences. These stylistic keywords are seamlessly integrated into the texts or appended at the end in comma-separated format.
4 EXPERIMENTS
In this section, we first present the benchmarks involved and our experimental configurations. Then, we compare VisualPrompter with three previous SOTA prompt engineering works both quantita- tively and qualitatively. Human evaluation is conducted to underscore the preeminence of our ap- proach. Furthermore, we select several widely-used text-to-image generation applications to demon- strate the effectiveness of our model in open-world scenarios.
4.1 BENCHMARKS AND EVALUATION METRICS
Benchmarks. For our evaluation, we utilize two comprehensive benchmarks: DSG-Ik (Cho et al, 2024) and TIFA v1.0 (Hu et al. 2023). Both of them contain human-writtes prompts from various established text-to-image benchmarks. The DSG-Ik benchmark comprises 1, 060 prompts from 10 publicly available prompt datasets, encompassing wide range of skills and writing styles. It provides 8, 182 questions associated with these prompts. facilitating multifaceted evaluation of generated images. Meanwhile, TIFA v1.0 contains 4, 081 diverse prompts from 4 datasets. As the original TIFA v1.0 benchmark contains multiple-choice and free-form questions, we further refine these questions and represent them in the DSG structure. Ultimately, the TIFA benchmark used in experiments contains 19, 233 general questions.
Metrics. To evaluate the text-image alignment, we adopt a robust and innovative approach (Yarom ct al.] 2023 Cho et al., 2023). which leverages question generation and answering techniques (Deutsch et al. 2021 Mi et al. 2023). This VLM-as-judee metric demonstrates strong correlations with human judgments (Hu et al. 2023). making it particularly suitable for fine- tne-grained evaluation tasks. We also employ the CLIP Score (Hessel et al. 202T) to evaluate the semantic alignment be- tween textual descriptions and generated images the feature level. For aesthetic assessment, employ the Aesthetic Score (Schuhmann et al., (2022), a well-established metric for objective image quality assessment, reducing subjectivity in human judgments.
4.2 EXPERIMENTAL SETTINGS
Our VisualPrompter leverages a dual-model architecture that requires an LLM for DSG generation and prompt optimization, connected by a VLM for comprehensive image semantic evaluation. We employ the state-of-the-ard open- Qwen2 (Yang al. 2024) and the Qwen2-VL (Wang et al. (0224) as our visual question answering model. Each language generation task in our pipeline is initiated by feeding Qwen2 with meticulously designed preamble and 23 manually curated samples from the TIFA dataset, ensuring consistent model output.
primary language model
To thoroughly evaluate framework's performance,
conduct extensive testing across three prominent diffusion models and one autoregressive generative model, including Stable Diffusion v1.5, Stable Diffusion v2.1 (Rombach et al. 2022). Flux-dev (BlackForestl .ass) 2024), and Janus-
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Table 1: Summarized results on the DSG and TIFA benchmark. Reported scores are based on the percentage of "yes" answers to the questions, while the best scores are highlighted in boldface. Our VisualPrompter demonstrates notable improvements in semantic consistency evaluations.
DSG benchmark
TIFA benchmark
Methods
Average
SD 1.5
SD 2.1
Flux-dev
Janus-Pro
SD 1.5
SD 2.1
Flux-dev
Janus-Pro
Baseline
67.5
72.1
79.1
79.5
75.0
80.4
87.9
85.7
78.4
NeuroPrompts
58.4
68.7
75.5
81.7
62.8
75.5
84.0
89.8
74.6
Promptist
65.1
68.7
76.9
78.0
71.4
77.2
85.3
86.9
76.2
BeaurifulPrompt
47.9
49.6
51.5
55.3
50.9
53.6
57.4
62.0
53.5
VisualPrompter
69.5
77.0
84.3
82.6
80.7
82.8
93.7
83.0
Table 2: Semantic evaluation across all categories of the DSG benchmark using Flux-dev.
Model
whoops
localized
posescript
Category of source texts countbench midjoumeyy
tifa160
stanford
diffusion db
Flux
82.2
85.1
73.3
85.8
72.1
56.7
88.6
83.3
89.1
68.9
NeuroPrompts
75.3
86.1
74.8
84.8
70.3
49.6
85.7
75.6
88.2
58.5
Promptist
79.1
84.1
75.4
81.9
73.3
56.6
85.4
81.2
85,2
61.4
BeautifulPromps
56.4
53.0
61.2
64.5
44.7
38.6
58.2
40.1
45.7
48.7
VisualPrompter 86.5
89.7 (+3.6) (+0.5) (+6.6) (+8.4)
(+4.5)
(+4.8)
(+3.7) 72.1 (+3.2)
Pro (Chen et al. 2025). Our evaluation utilizes prompts from both the DSG-Ik and TIFA bench- marks. We compare our method with several open-source prompt engineering methods, including NeuroPrompts (Rosenman et al. (024), Promptist (Hao et al. 2023), and BeautifulPrompt (Cao al. 2023). All of these methods utilize supervised fine-tuning and reinforcement learning to ad- just LLMs for optimizing prompts. Our approach is implemented using PyTorch. All experiments are conducted on a single A100 with 40GB memory.
4.3 SEMANTIC ACCURACY EVALUATION
We compare VisualPrompter with several state-of-the-art prompt engineering methods on two se- mantic consistency evaluation benchmarks, and summarize the results in Table [ We refine the original text prompts with each method, and use the refined prompts to generate images with differ- ent generative models. We then use Qwen2-VI.-7 to assess whether the generated images contain the specified semantic information. We quantify the results by measuring the average answering accuracy, which has been demonstrated to be effective by DSG (Cho et al. 2024).
As illustrated in Ta our model achieves significant improvements across all evaluation bench- marks and all generative models. Notably, VisualPrompter consistently outperforms the four base- line generative models in terms of average performance scores. The results also show that Visual- Prompter achieves higher performance gains when integrated with Flux-dev and Janus-Pro, which be attributed their state-of-the-art generative capabilities, particularly in handling long sen- tences and intricate details. This indicates that VisualPrompter can better collaborate with high- capacity generators produce results with more accurate semanties. It also demonstrates the strong adaptability of VisualPrompter, enabling its effective application across various scenarios.
Surprisingly, previous methods not only fail preserve the semantic consistency during optimiza- tion but also exhibit varying degrees of decline in this aspect. Our analysis reveals that both Neu- roPrompts and Promptist are unable to modify the original descriptions, even though they utilize an LIM for prompt optimization. This limitation prevents them from effectively refining those model- dispreferred sentences. Additionally, they often introduce irrelevant keywords that are inconsistent with the overall context of the prompts, thereby compromising the semantic fidelity of the generated images. In contrast, BcautifulPrompt is able to adjust the original prompts and add contextually rel- evant details. However, it tends to omit critical information during the modification process, which significantly deviates from the user intent.
In Table 2 present the semantic evaluation results across all categories of the DSG benchmark using Flux-Dev. Additional results from other generative models, as well as those from the TIFA benchmark, be found in Appendix E These results confirm that our approach effectively rein-

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Original
Qwen2
NeuroPrompts
Promptist
BeautifulPrompt
VisualPrompter
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Figure 3: Comparison of prompts generated by large language model and different prompt engi- neering methods. Images the two rows correspond to outputs from Stable Diffusion v2.1 and Flux-dev, respectively.
forces semantic components in the input prompts and ensures their accurate representation in the generated images. Our model demonstrates consistent improvements across specific categories, in- dicating its strong capability to comprehend and enhance key elements such objects, attributes, and relationships.
We visualize the generation results of various generative models, shown in Figure 3 Qwen2 for zero-shot prompt optimization as comparison. The results demonstrate that our model capable of producing more elegant prompts that result in more visually compelling images. successfully corrects the deficiencies of the original images, indicating that our optimized prompts are preferred by generative models. Additional visual examples are presented in Appendix
We also
4.4 TEXT-IMAGE RELEVENCE EVALUATION
To further validate the effectiveness Visual- Prompter, evaluate the CLIP Score between the optimized prompts and their corresponding generated images. We report the mean scores of all the prompts from the DSG benchmark, gen- erating images with those different generative models. The results are illustrated in Table
Table 3: Results of CLIP Score between opti-
mized prompts and generated images on DSG benchmark. Baseline represents the results on im- ages from original prompts.
Generative Models
Methods
Mean
As shown in Table 3 prompts optimized by Vi- sualPrompter achieve the highest scores across all the generative models. The results indicate that the prompts produced by VisualPrompter can be effectively understood by the genera- tive models, generating images with improved semantic coherence. Optimized prompts from Promptist and BeautifulPrompt also achieve relatively high CLIP Score compared to baselines, whereas NeuroPrompts performs the worst. This primarily because NeuroPrompts offers only limited keyword options, which significantly restricts the diversity of the resulting prompts. In con- trast, we leverage LLMs to generate aesthetic keywords that align well with the sentence content, leading to greater overall coherence and harmony in the prompts.
SD v1.5
SD v2.1
Flux
Janus Pro
Baseline
31.53
31.88
31.58
31.83
31.71
NeuroPrompis
25.61
24.56
26.21
25.41
Promptist
32.14
31.61
31.49
31.85
31.77
BeautifulProppt
31.70
31.38
31.35
32.10
31.63
VisualPrompter
32.21
32.50
33.81
32.22
32.69
Additionally, we also notice that Promptist and BeautifulPrompt achieve higher CLIP Score than the baseline with Stable Diffusion v1.5 and Janus-Pro, while their performance drops with Stable

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Preforence
Table 4: Results of Aesthetic Score for gen- erated images on DSG benchmark. Baseline represents the results on images from original prompts.
Stable Diffusion v1.3
Semantic Consistency
62%
12%
25%
Aesthetics
71%
8%
21%
Flux-dav
Methods
Generative Models
Mean
Semantic Consistency
65%
16%
19%
SD v1.5
SD v2.1
Flux
Aesthetics
48%
32%
Baseline
5.30
5.48
5.77
5.52
PeuroPrompts
6.21
6.03
6.40
6.21
Promptist
5.93
5.86
6.25
6.01
Figure 4: Human evaluation results. From left to right, the segments indicate preference for images from optimized prompts, equal prefer- ence for both results, and preference for images from original prompts.
BeautifulPrompt
5.97
5.98
6.48
6.14
VisualPrompter
5.73
6.03
5.81
Ours NeuroPrompts
6.15
6,44
6.24
Diffusion v2.1 and Flux-dev. The performance gap arises from their specific optimization for Stable Diffusion v1.5. Another reason is that Janus-Pro's autoregressive design enables stronger semantic understanding, allowing it to better handle complex sentences. The discrepancy highlights their lim- ited generalization capability across different generative models. contrast, our prompt engineering framework functions in model-agnostic way, which does not rely on any specific generative model, demonstrating the broad compatibility and robustness of VisualPrompter.
4.5 AESTHETIC EVALUATION
Recent studies (Hao al. 2023 Podell et al. 2024) have revealed that conventional image quality metrics, such as FID (Heusel et al. 2017) and CLIP Score (Hessel et al.) (021), do not exhibit positive correlation with visual aesthetics.
alleviate the burden of labor-intensive human evalua- tion, the Aesthetic Score (Podell et al. 2024) has emerged as an effective metric, as it is specifically designed to align with human perceptions of beauty, leveraging insights from large-scale datasets. Thus, previous studies have placed greater emphasis forcement learning frameworks. We also evaluate our method with the Aesthetic Score to show the visual elegance of optimized images. The results are summarized in Table
this metric and integrated it into their rein-
The experimental results on aesthetics show that VisualPrompter achieves modest improvements in aesthetic quality, which ily stems from the tendency of our method to classify the original prompts as descriptions of realistic photographs, which results in inherent lack of imaginative fantastical elements among the op- timized prompts. This also indicates that our prompt decorator is overly simplistic for enhancing aesthetics. To address this problem, we employ NeuroPrompts which adds additional stylistic keywords while preserving the original sentence. This integrated approach achieves optimal performance on average in the aesthetic evaluation. However, we ob- serve a notable decline in semantic consistency, compared to the results without the involvement of NeuroPrompts. It implies that adopting the Aesthetic Score as a training metric may inadver- tently encourage the model to optimize for superficial features at the expense of semantic integrity, highlighting the need for more balanced and semantically meaningful training guidance.
substantial those previous studies. This discrepancy primar-
replacement for our decorator,
4.6
USER STUDY
To enhance the credibility of the above quantitative results, we additionally conduct human evalu- ation experiment based on Stable Diffusion v1.5 and Flux-dev. We compute the average preference distribution and report the results in Figure In detail, we generate image pairs using both the orig- inal prompts and the optimized prompts derived from VisualPrompter. A panel of 10 independent annotators is tasked with selecting the most desirable images from semantic and aesthetic aspects. Each annotator is provided with 50 samples randomly selected from both benchmarks.
The results of human evaluation indicate that annotators generally prefer images generated by op- timized prompts in both semantic and aesthetic aspects, which demonstrates the superiority of our
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Midjoumey
Table 5: Impact of different modules in Visual- Prompter. We report the Semantic Accuracy Aesthetic Score based the DSG benchmark.
Prompt Modification Approach
Visual Feedback
ge
Malibe
sago:
None
Qwen
DSG
w/o w/
71.9 5.69 7 5.54
72.1 67.8 5.32
5.45
77.0 /.73
Table 6: Inference time comparison.
sblec zoaloned dightt on tharsoart aua cruvded var.
calooig ho chorssiigg mode the de bigh
The sale au
Prompt Modification Approach
T21 Models
NeuroPrompts Promptist Qwen3-32B
Ours
8.5s
21.3s
10.7s
Figure 5: Adaptation for online generators. The second row presents results optimized by Visu- alPrompter.
FLUX-dev
20.3s
18.8s
38.18
34.3s
approach. The optimized prompts slightly outperform the original prompts in terms of aesthetics on Flux-dev, owing its robust generative capability, which can effectively fill in intricate details.
4.7 ABLATION STUDY
Module Impact Evaluation. To systematically analyze the impact of different components in Vi- sualPrompter, we conduct an ablation study to evaluate the individual contributions of the self- reflection module and the target-specific optimization module in Figure Specifically, compare two approaches for prompt refinement: direct prompt optimization using Qwen and constructing prompts from expanded concepts based DSG. For the self-reflection module, visual feedback leveraged to refine the targeted location of the given prompts. Additionally, we account for the impact of regeneration, where visual feedback employed to evaluate the original images. Prompts that result in defective images will be used to regenerate images without any modifications.
As shown in T our fine-grained semantic extraction and composition approach achieves better results than directly using LLM for optimization. We observe that Qwen often produces prompts with semantically irrelevant details. These long sentences increase the difficulty for the diffusion models to comprehend them, leading to worse results than the original prompts. Another obser- vation that the regeneration evaluation metric. Despite this influence, our VisualPrompter achieves superior results, demonstrat- ing its outstanding capability in enhancing semantic consistency.
images actually exerts a non- non-neglii impact on the semantic
We compare the inference times of several optimization methods and summarize the results Table ( We calculate the total time from when a prompt is provided to when complete image is generated. Our model uses Qwen2 1.5 B for fast DSG generation and concept integration. As shown in Table 6 while our VisualPrompter is marginally slower compared with existing optimization models, the difference falls within tolerable limits for practical applications.
Inference time.
Online Generation Evaluation. To show the robust adaptability of VisualPrompter, we select several online generative models, including Midjoumey (Midjourney (021). Kolors (Kolors) 2024). and DouBao (DouBao] 2023). We provide them with numan-written prompts and utilize the feed- back further optimize the prompts. The results are presented in Figure 5 Each image is selected from three samples with the same prompts.
5 CONCLUSION
In this paper, we propose VisualPrompter, training free prompt engineering framework that im- proves text-to-image generation by refining prompts to model-preferred sentences. We reveal that prompts generated by existing LLM-based methods often struggle to maintain semantic consis- tency with the original intent and consequently produce images that deviate semantically. To tackle

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this issue, VisualPrompter operates the atomic semantic level to analyze and revise the prompt. leverages mrodel-specffie insights obtained through visual reflection to perform targeted expan- sion and modification of the initial imput, thereby ensuring semantic consistency throughout the optimization process. Extensive experiments demonstrate the effectiveness of our VisualPrompter, which achieves state-of-the-ar performance in enhancing semantic consistency while maintaining superior aesthetic quality, Compared with previous methods, VisualPrompter exhibits better gener- alization capabilities, which can be easily adapted to various diffusion models. We intend to extend this feedback-drive optimization system to multimodal generative models in our future work.
ACKNOWLEDGEMENTS
This research is supported by Artificial Intelligence- Natiiona Science and Technology Major Project (2023ZD012 1200), and the National Natural Science Foundation of China (62531026, 62437001, 62436001), and the Natural Science Foundation of Jiangsu Province under Grant BK20243051, and the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA04080400.
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A AUTOMATIC CONSTRUCTION OF DSG
Davidsonian Scene Graph (DSG) (Cho et al. 2024) is a set of questions in the form of a directed acyclic graph. which has been used for text-image alignment evaluation. As shown the right side of Figure 6 each question in DSG is about an atomic concept, which represents specific and indivisible semantic unit through a tuple. By feeding the generated images and the related questions into a visual question answering model, we can effectively identify and analyze the missing concepts present in the images.
Quesdions
Prorptt
A boser ong witing the grass coear my mouh
Ia there dog'?
grase?
Davidsonian Graph (D50)
3. la there chere toy?
Tock Tople Generstio
Task Queetion Traneforation
dog elting?
the dog gross?
the dog adaig?
the dog the grass
Taok Depardency Mentitoortoon
there graes?
Grad-shdde
Inilyy
S-here chose loy?
1. Eriliyy whole (oog)
Indepen
gaasa]
Intepeettntt
Eriitt whole tshow tuy)
dede
Palatie spanaa
Adtibutt bouar tog)
Polles
Scuar dog?
the holding chipp
trog
5. Adnoeta state icea the griaat
5. Padies on und
Figure 6: The automatic construction of DSG with an LLM.
Large language models (LLMs) have been employed to construct DSG from textual descriptions automatically. To meticulously construct DSG that is both comprehensive and non-redundant, our approach adopts three-step pipeline for the overall generation process, as illustrated in Figure 6 Specifically, an input prompt is first decomposed into detailed concepts represented as tuples, which include entities, attributes, relations, and actions in the text. Based on these tuples of atomic con- cepts, L.LMs are able generate corresponding general interrogative sentences and determine the dependency relationships among them. Finally, the questions and dependencies are combined to form the DSG for further use.
To ensure consistent and standardized outputs from the large language model (LLM), for each step in the process, we provide the LLM with a well-designed preamble and multiple manually annotated samples. This methodology guides the LLM to generate tuple annotations in uniform format for new inputs, thereby enhancing the reliability and scalability of DSG generation across diverse textual inputs. We assign numerical identifier to each tuple to distinguish between them and ensure the integrity of data. An example of tuple generation is shown in Figure 7 The preambles and annotated examples for other tasks will be included in our code release.
For the generation of questions and dependencies, we provide the LLM with both the prompts and tuples enhance comprehension. Any invalid results will be regenerated multiple times to ensure accuracy.
B TARGET-SPECIFIC OPTIMIZATION
Here we provide examples for the two key technologies in our target-specific optimization module: concept expansion and prompt regeneration. We utilize the LLM engineering method used in DSG generation above. Examples of prompt templates are shown in Figure & and Figure
C KEYWORDS FOR DECORATION
To enhance the aesthetics of generated images, employ an LLM decorator to incorporate key- words, which have been demonstrated to substantially improve the aesthetic appeal of the images. To constrain the generation scope of the L.M,, provide several keyword examples in the task
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Prommbl
Adl cumell leprc
(rtd merth the Koles. faer are poite
Vaet ascod demert the Rioe citerosee
PIDAAP
Aocule bei dialical
Exemppoy
Preamble
"Vole
ciaatio the citroer
"voler cas with "ndercoe Tonder coserin
Tepu
bh
More
Ouggu
parsel diipoer
-
Figure 7: Example of tuple generation.
sooc pand chipee
ANC/0115
Examples
enaileyyle (M0)
the
seir afridd
cuals
gounts
allon mecal sothene
Text
aoon aider decurly puarkk brids thrugg
a9t cerTiaar srllnng
sacutiy pusne
Output
ponks meaft
10 (maatos unCr To quart SPodlrs oul toint
Irpul
Figure 9: Example of concepts expansion.
Figure 8: Example of prompt regeneration.
preamble and instruct the LLM decorator to select appropriate keywords that align with the prompt content. These suitable keywords are appended at the end the input sentence, separated by com- mas. We collect some keywords from the results of NeuroPrompts and BeautifulPrompt. These
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Table Class and examples of aesthetic keywords
class quality style background light aesthetics
Samples
best quality, 4k, 8k, highres, masterpiece, fantasy art, highly detailed photo-realistic, oil painting, portraits, digital art, landscape, impressionist anime, concept artists, cyberpunk
blue sky, crowded road, beautifully decorated wall, high mountain studio lighting. soft lighting, sun lighting, diffuse lighting, cold lighting beautiful, clegant, stunning, lovely, majestic, charming. graceful
Table Detailed results on the TIFA benchmark. Reported scores represent the percentage of "yes" answers.
Model
dunbbacch
parlijeonnt
painkiill
Model
Calquey jarlipecenj
jaintkill
Avenge
c1.5
SD
67.5
Neurohhremps
69.7
49.5
58.2
52.4
62.8
NeuroPrrcnpe
31.4
64.5
72.5
64.3
75.5
Promprist
61.
67.9
71.4
Prompris
94.8
67.3
71.2
45.9
48.9
46.8
$3.6
VisudPPoomper (-5.7 70.5
77.0 (+4.5)
66.8 (+8.3)
80.7 (15.7)
VissaiPhompter (-2.5)
(-2.5) 74.5 (15.4) 7966 (-1.7) 69.7
$2.8 (+2.4)
(a) Semantic evaluation on Stable Diffusion v1.5.
(b) Semantic evaluation Stable Diffusion v2.1.
Model
coco
drowheenh portiprempe
Categery of source teats
peimskill
Averaee
Model
cooo
Category of source tests driwleench portiprompt
paimtcill
Average
$5.4
NeuroPhocpps
NeursPhompes
83.9
BesutillPhougg
ViendProuugte 1962 (-3.8) (466 (14.6) 94.7 (45.5) 9338 (+5.9)
ViaziProuptaz (+2.5) (4.2 (14.0 98.8 (19.0) (44()))))
(c) Semantic evaluation on Flux-dev.
(d) Semantic evaluation on Janus-Pro.
keywords are systematically organized according their descriptive categories, including image quality, artistic style, background description, environmental illumination, and perspective. A selce- tion of examples is illustrated in Table 7
For each class of keywords, we guide the LLM to select or create to 2 words and incorporate them into the input prompts. This approach yields sentences that both detailed and enriched with aesthetic keywords. Our experimental findings indicate that the inclusion of these aesthetic key- words does not substantially affect the semantic consistency between generated images and textual descriptions.
D EVALUATION RELIABILITY WITH DSG
We adopt the Davidsonian Scene Graph (DSG) (Cho et al. 2024 as a stable metric for text-image alignment. DSG decomposes prompts into atomic, dependenccy-structureed questions whose accu- racy yields precise measurement of semantic consistency. The alignment score is computed as the proportion of "yes" responses from a VLM, ensuring that images with stronger textual fidelity receive higher scores. Notably, DSG demonstrates near-perfect reliability: questions achieve 98.3% precision and 96.0% recall against human annotations, while state-of-the-arr VLMs answer these questions with 82.6% accuracy in entity judgments. These results substantiate the reliability of DSG, enabling our VisualPrompter to deliver dependable defect detection and targeted optimiza- tion. Furthermore, DSG's dependency-awarc design inherently prunes invalid queries such as asking about an attribute when the object is absent, thereby reducing noise and reinforcing measurement stability across diverse prompts and models. For additional experiments and implementation details validating DSG's stability, please refer to tooCho et al. (004)).
E DETAILED RESULTS ON BENCHMARKS
The benchmarks used in our study, namely TIFA v1.0 (uu et al. 2023) and DSG (Cho et al. 2024) (as introduced in the paper), consist of multiple constituent datasets. We have systematically evalu- ated VisualPrompter's 's performance across sub-datasets and compiled the comprehensive results in Table & and Table
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Table 9: Detailed results on the DSG benchmark. The best scores are highlighted in boldface. Our VisualPrompter stands out as the only method that demonstrates notable improvement in semantic consistency evaluation.
Model
whoops
localized
posescript
vrd
Calegury source lexts counthench midjourec
18f1160
draw text
stanford
di diflusion db
Average
SD
NeuroPrompes
57.8
62.9
$8.3
62.2
51.1
50.3
61.6
47.6
62.0
60.6
58.4
Prompist
73.7
68.0
61.8
64.7
66.2
$7.9
70.5
56.1
65.1
63.3
65.
ResutfolPrompt
48.0
48.3
52.8
53.7
43.7
46.3
50.1
41.5
38.9
54.5
47.9
VisuilPrompter 76.5 (-2.8) 75.7 (+1.3) 61.5
71.4 (+4.7) (+3.))
(+1.9
77.8 (-0.7) (-16)
63.6 (:2.0)
73.3 (+1.6)
69.5 (+2.0)
(a) Semantic evaluation on Stable Diffusion v1.5.
Model
whoups
localized
poseseript
vrd
Culegury source texts coumbench
midjourney
tif.160
draw text
staforrd
diclusion db
Average
SD v2.1
26.9
17.7
66.9
70.6
69.5
63.0
81.5
61.1
73.3
71,6
72.1
NeuroPrompts
70.8
76.4
62.2
74,5
66.4
58.7
74.5
63.8
66.1
70.1
68.7
Promptiod
71.5
73.3
65.8
68.1
71.3
59.8
75.9
67.1
64.5
65.7
68.7
ReuntiullProege
54.7
47.1
43.4
VisulPtomptts
$2.0 (-5.3) 80.6 (+2.9)
71.1 (44.2)
77.5 (13.0) 79.1
(+9.6)
69.6 (46.6)
838 (42.2) 73.3 (16.2)
74.2 (40.9)
74.9 (43.3)
77.0 (44.9)
(b) Semantic evaluation Stable Diffusion v2.1.
Citepory source texts comntiench midjoumey
Model
whtoops
Incalized
posescript
tifa160
drow
stanford
ditfusion dh
Average
Jaaus-Pro
86.1
87.2
09.8
K2.4
73.7
65.6
87.2
79.9
85.9
72.1
79.5
NeuzoPrompts
86.2
88.7
76.7
86.4
76.0
68.3
88.1
85.9
$6.8
70.0
81.7
Prompiit
BewntfolPruppt
66.3
54.2
39.1
62.4
54.7
45.2
61.0
45.5
44.3
56.8
55.3
VisuaiPrompter
91.2
(/2.1
74.2 (12.))
86,9 (+0.) (9.0 (9.5)) 90.8 (+2.5) 85.8 (-0.1)
80J1
76.9 (+4.8) 82.6
(c) Semantic evaluation on Janus-Pro.
From Table 9 our model demonstrates more pronounced improvements across prompts from whoops, localized, and countbench datasets, which care more about object attributes and spatial relationships. The results demonstrate that our model is capable of effectively enhancing semantic elements in the prompt, ensuring their representation in the generated images. We also notice that there are still sub-datasets in the detailed tables on which our method exhibits a slight performance decline. We attribute this fact to the limitation of Stable Diffusion v1.5, which is not sufficiently ca- pable of comprehending human poses and processing long textual descriptions.
The experimental results demonstrate that our method achieves significant improvements across all subcategories of the TIFA benchmark, indicating strong capability optimizing naturally de- scribed sentences. On the DSG benchmark, our model shows consistent performance gains in most subcategories, which further validates generalization ability across different evaluation metrics.
While our approach delivers robust performance overall, we observe marginal performance de- creases when applied to SD v1.5 and Janus-Pro models on the DSG benchmark. This can be attributed to two main factors: (1) SD v1.5's limited capacity in processing moderately complex sentences often leads to substantial detail loss, and (2) Janus-Pre 's superior language understanding sentences, yiclding slightly higher scores. We plan to conduct more in-depth investigations to develop better optimization strate- gies in our future work.
capability makes it particularly responsive to XeuroPrompt-optinin
F MORE CASES OF VISUALPROMPTER
Additional results for our optimization approach are shown in Figure Figure I1 and Figure 12 Each image pair consists of one generated from the original prompt and another from the prompt optimized by our VisualPrompter, with the corresponding prompt text displayed beneath each im- age. The visualization results convincingly demonstrate that VisualPrompter exhibits robust adaptability to various prompts and substantiates superior performance in rectifying multiple types of generation artifacts.
17

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Published as a conference paper at ICLR 2026
countbench
sasd cas
lake
aroars, wanks and
Landing ceeedii
reppinn caaingg
lebaaiy male
lighty
lightgg
be cnd dell
localized narratives
seatte odly
ougort
der phota
caly.
fo
Emadle
dag
lightyy aegied
OO
midjoumay
Hullery te Loveero
Hebiig VMC. glowng enyind
no nin Codtse
cood lighty
latt sotte bonng ssttl
chee thi
trato saeck art light dlgiind
whoops
Aneeer deuch bike Bonding
Rove fael
Aroe Jhal calno bes coiting
weth Medooe
fig digaed
liging Servey eegidd
Examples of images generated by Stable Diffusion v1.5. Each line corresponds to the results of prompts derived from distinct sub-datasets. The right image each pair is generated from the prompt optimized by VisualPrompter, as detailed below the image.
Figure 10:
G FAILURE CASES OF VISUALPROMPTER
Despite its effectiveness, our VisualPrompter still exhibits certain limitations. A few typical failure cases shown in Figure 13] Most failure cases stem from the inherent constraints of the gen- erative model, particularly its suboptimal accuracy in multi-object generation, which occasionally leads irrecoverable artifacts in the final output. Additionally, some errors arise from the VLM's judgment, where semantically subtle or ambiguous prompts are incorrectly validated, preventing VisualPrompter from identifying and optimizing these issues. Nevertheless, we anticipate that ad- vancements in VLM capabilities will progressively mitigate these challenges, further enhancing the robustness of our model.
H INFLUENCE OF RANDOM SEED
To investigate the impact of random seeds, conduct extensive experiments two text-to-image generative models using five different random seeds and present the comprehensive results in Ta- ble 10 The experimental outcomes show remarkably consistent performance across all trials, with only minimal deviations observed. This stability clearly demonstrates that the influence of random seeds results is statistically negligible.
The observed robustness can be attributed to two key factors. First, the DSG benchmark incorporates extensive set of over thousand diverse prompts, providing sufficient data volume effectively
18

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Published as a conference paper at ICLR 2026
9
countbench
recor
alder loing
Lidder opettal lighs
hes fic
leeaee ael
ring wepi sel
Medes
BEE
drawtext
MAds
BEE
bout will
leys certennn
ga cessig the
made
Take light quitt)
soft bigiii sngaet
made mlein
vrd
The
bame b0te
Ueer on
whee
anco iie setee oeeco pertis
caeocing Bai cakadl
bodeers sartal light
mestie coedel ait
edees luetel fight
cake
deall
whoops
fame Rener Mew
Bow plice dil
Tade
legurt
lyding eng
Figure 11: Examples of images generated by FLUX-dev. Each line corresponds to the results of prompts derived from distinct sub-datasets. The right image in each pair is generated from the prompt optimized by VisualPrompter, as detailed below the image.
diffusiondb
periiaaa onl
and silee
coveed tor
nalei Maro,
callo
the daereey
roving
midjoumay
Aumbiin moidd
Allrr waliing
wible
cerovdd light
Figure 12: Examples of images generated by Janus-Pro Each line corresponds to the results of prompts derived from distinct sub-datasets. The right image in each pair is generated from the prompt optimized by VisualPrompter, detailed below the image.
average any potential randomness. Second, while random variations might affect certain pects like layout generation, they have minimal impact on semantic content representation. This is particularly relevant VisualPrompter framework is specifically designed to enhance and com-
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Published as a conference paper at ICLR 2026
Flux-dev
Recrape
mesiiue Light detalo deaint
cheeell cogil chily
shell ongeet
SEOTTLMECTEN 9.VCU UM
Furroomel
Janus-Pro
The hoarcheree
Log
Ada sileta
Hikapdar sale
ploce place did
horel dec Ard
Figure 13:
Some failure cases of VisualProopter. The two rows of images are from Flux-dev and Janus-pro pro, respectively.
Table 10: Impact of random seed. We report the mean and standard deviation of semantic accuracy based on the DSG benchmark.
Prompt Modification Approach Qwen3-32B
T2I Models
None
VisualPrompter
SD v1.5
67.4 + 0.13 60.9 + 0.43
69.8 = 0.56 84.3 = 0.12
FLUX-dev 79.2 + 0.08 79.7 0.21
plement semantic information processing, further mitigating any potential variability from random initialization.
I POTENTIAL TOWARD MULTIMODAL ANALYSIS
To evaluate the broader applicability of our approach, integrate ControlNet (Zhang et al. 2023) with human pose estimation, enabling image generation conditioned on both skeletal inputs and textual prompts. We quantitatively assess deeetonntt-imagg consistency using our VisualPrompter's VLM module, which additionally provides refinement suggestions for improved alignment.
framework demonstrates promising potential for multimodal anal- effe-effettv bridging visual structural data (skeletons) and linguistic descriptions. This multi- modal capability suggests significant versatility in handling diverse input modalities. We identify this as pivotal research direction and plan to systematically explore: (a) cross-modal interaction mechanisms, (b) scalability to additional modalities. and (c) quantitative evaluation frameworks, which will constitute a cornerstone of our future work.
As illustrated in Figure 14
J USER STUDY SETTINGS AND MORE COMPARISON
We provide simple test system for our human evaluation experiment based on streamlit, as shown in F5g 15) Departing from the experiments in previous text-to-image prompt engineering studies that only focus on aesthetic evaluation, our assessment framework specifically incorporates analysis of semantic alignment between generated visual content and the corresponding textual prompts, which measures how well the generated images align with the user intent.
As shown in Figure 15 raw prompt and pair of images are given to the participants for each test. The images pair consists an image generated by the original prompt above and another image generated by the optimized prompt. In experiment, the optimized prompts invisible to the participants. To eliminate potential bias, images in each pair are randomly assigned to left or right positions. Participants are required to evaluate key aspects individually: the visual consistency
20

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Published as a conference paper at ICLR 2026
Human Evaluation
Reference
Original
Optimized
Prompt:
boal at crmal massage 'anaddeno
amackarod
comparison
on the vibrant grass, raising champlonship high celebration
the vibrant grass, raising championship cup high celebration, with his right hand planted detailed, on outdoors >
VisualPrompter
suggestion and refinement
Figure 14: Multimodal application VisualPrompter.
Figure 15: Screenshot of human eval- uation interface.
Table 11: Comparison of VisualPrompter with other optimized methods. User preferences are mea- sured the percentage of choices for VisualPrompter, the competing model, tie, respectively,
Methods in Comparison Promptist
T2I Models
Aspects
NeuroPrompts
BeautifulPrompt
Semantic
58% / 24%
18%
52% 27% 211%
60% / 26% 14%
SD v1.5
Aesthetics
41% / 43%
16%
44%
39% 17%
35% / 46% 19%
Semantic
44% / 30%
26%
43%
39%
/ 18%
44% /31% / 25%
FLUX-dev
Aesthetics
34% / 46% / 20%
37% 41%
22%
27% / 49% 24%
with the given prompt and the aesthetic appeal of each image. These evaluations provide essential insights into the overall quality of the visual representation.
To evaluate the semantic consistency and aesthetic quality of our model, we conduct the user study above based on Stable Diffusion v1.5 and Flux-dev. comparing it with other optimized models rather than the original generators. As illustrated in Table TT the results demonstrate clear user prefer- ence for our model in terms of semantic consistency. Furthermore, it achieves competitive perfor- mance on aesthetic metrics, underscoring its comprehensive capability.
K COMPARISON ON COMPLEX PROMPTS
To investigate performance complex prompts, collected additional prompts describing in- tricate scenes and generated images using Flux-dev, a generative model capable of handling com- plex scenarios. We then compared these results with outputs from our VisualPrompte--piimize prompts. As shown in Figure [6] our model demonstrates clear ability to correct missing or er- roneous semantics in the generated images. We also observed that the capability of the underlying generative model is crucial. Since long prompts already contain substantial detail, our optimization approach remains constrained when dealing with structures that are inherently challenging for the base generative model. We plan continue research in this direction.
To validate the effectiveness of our method in optimizing multilingual text-to-image generation, we use Chinese text accepted by Qwen for testing. Since Stable Diffusion v1.5 and FLux-dev do not support Chinese, we select the text-to-image model Kolors (Kolors) 2024) as our subject and modify the prompt templates in VisualPrompter to support Chinese input. Visualization examples are shown in Figure The results indicate that our method can also enhance Chinese -ottto- generation. This effectiveness stems from language-agnostt atomic semantic structures and analytical approach, allowing for the processing different languages supported by the underlying I.M.M
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Published as a conference paper at ICLR 2026
Original
VisualPrompior
Original
VisualPrompter
Originall
VisuaiPrompter
Original
VisuaiPromplar
snnaeee Laaal unten di ylea mechee
whei Idea ong tow
fesenii Tevate bowe
andd hovutee seleed
chanss
viding
plm
leyaer plli
Figure 16: Visual comparison between the original Flux-dev (left) and our optimized framework
(right) on complex prompts.
Original
VisualPrompter
Original
VisualPrompter
EU0 Munt BUn 1914 wiw.iiiiiiee
DIS: 460 pnD IER SEE BuRi ROC MORT
reen von voste
to
on rpa toree the
wore
Examples of multilingual optimization based on Chinese, validating the effectiveness of the proposed method across languages. We employ Kolors (Kolors) 2024) as the image generator.
Figure 17:
Table 12:
Summarized results on the DSG benchmark using Llama- Vision. Reported scores are based on the percentage of "yes" answers to the questions, while the best scores are highlighted in boldface.
DSG benchmark
Methods SD 1.5
Average
SD 2.1
Flux-dev
Janus-Pra
Baseline
67.2
72.0
79.4
79.5
74.5
NeuroPrompts
59.0
69.5
77.2
81.4
71.8
Promptist
65.9
68.9
77.6
78.2
72.7
BeautifulPrompt
49.4
51.1
53.4
57.0
52.7
VisualPrompter
70.1
77.9
85.4
82.9
79.1
L ROBUSTNESS OF EVALUATION
We notice that the use of the same VLM for both the internal optimization feedback and the fi- nal evaluation may introduce potential bias. To address this concern and validate the robustness of our reported performance, we conduct an additional experiment where the results are re-evaluated using another VLM. We employ --- Vision for comparative analysis, it is both open-source and readily accessible. As summarized in Table 12 and Table [T] the evaluation scores between the two VLMs are highly consistent. Despite minor fluctuations in evaluation scores, the
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Published as a conference paper at ICLR 2026
Table 13: Summarized results on the TIFA benchmark using Llama-Vision. Reported scores are based on the percentage of "yes" answers to the questions, while the best scores are highlighted in boldface.
TIFA benchmark
Methods SD 1.5
Average
SD 2.1
Flux-dev
Janus-Pro
Baseline
76.1
81.0
88.4
85.6
82.8
NeuroPrompts
64.3
77.2
85.5
89.8
79.2
Promptist
72.7
79.0
86.9
86.8
81.4
BeautifulPrompt
53.0
55.9
62.8
63.5
58.8
VisuaePrompter 81.7
83.4
94.3
94.5
88.5
relative performance ranking of all optimization methods remains identical under both VLM asses- sors, underscoring that our comparative conclusions are robust to the choice of evaluator.
M DISCLOSURE OF LLM USE
The authors confirm that the use of LLMs, which limited exclusively to polishing sentence struc- tures and correcting grammatical errors without being involved in generating ideas, contributions, data, or any substantive content.
23


Reviews:
[{"review_id": "0ek78147XH", "reviewer": "Reviewer_cJ9v", "summary": "The paper aims to improve the semantic alignment between generated images and user descriptions. \n\n\nIt proposes VisualPrompter, a training-free prompt engineering framework that iteratively refines user prompts. VisualPrompter has a self-reflection module that analyzes generated images, and a target-specific prompt optimization that revises the prompt later. The method is plug-and-play and achieves state-of-the-art alignment on multiple image generation benchmarks.", "questions": "- Robustness: How is the performance with difficult prompts, for example, long prompts, multilingual prompts, code-mixed prompts? Could you show any qualitative failure cases?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Easy to use: the proposed VisualPrompter is model-agnostic and plug-and-play, making it highly adaptable to various generative models.\n- Good results: VisualPrompter outperforms many baselines on multiple benchmarks and multiple generative models, as shown in Table 1.", "weaknesses": "- Auxiliary LLM bias: Introducing an additional LLM in the loop may inject its own biases, especially there’re multiple LLM calls. \n- Compute overhead and latency: the generate - analyze - revise cycles may be significantly more expensive than a single forward pass. In addition, LLMs were called multiple times in one image generation, which might be costly. \n - Limited contribution: modules are not novel. For example, regarding the reflection module, the LLM Expander, the LLM Composer, similar techniques can be found in references. It'll be great if author could explain what's the unique contribution and novelty in VisualPrompter.", "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": "ZTVnYYLu0P", "reviewer": "Reviewer_wv9m", "summary": "This paper introduces VisualPrompter, a training-free, plug-and-play framework that uses self-reflection to detect missing concepts and performs atomic-level prompt edits to improve semantic alignment between user descriptions and text-to-image outputs. Experiments show state-of-the-art results on multiple text–image alignment benchmarks.", "questions": "Questions：\n1. VisualPrompter uses Qwen2-VL as the visual question answering model; however, SEMANTIC ACCURACY is also evaluated using Qwen2-VL as the assessment model. Would this introduce a bias?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Visual Prompter leverage visual-language models (VLMs) for question–answer-based detection of missing semantic concepts in generated images, aligns with human intuition and exhibits high interpretability.\n2. Visual Prompter significantly outperforms current state-of-the-art prompt engineering methods in multiple benchmarks.", "weaknesses": "1. The user study compares Visual Prompter only with the baseline (original prompts), rather than with other prompt optimization methods. \n2. Lacks comparison with recent methods, such as 《TIPO: Text to Image with Text Presampling for Optimal Prompting.》\n3. In Figure 11, the original prompts themselves are ambiguous and unnatural for human expression, such as “person next to person” or “bottle on the left of bottle.” I would like to see the performance of VisualPrompter on more natural and human-like prompts, such as those mentioned in Figure 1.", "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": "YDb2EoYbHA", "reviewer": "Reviewer_5nxL", "summary": "This paper addresses the semantic misalignment problem in text-to-image (T2I) generation, where sometimes model-preferred output images fail to match  user-provided prompts. The authors propose VisualPrompter, a training-free, semantic-aware prompt optimization framework that refines user prompts through atomic-level semantic decomposition based on the Davidsonian Scene Graph (DSG). \nBy parsing prompts into fine-grained semantic concepts through the Self-Reflection Module (SERE) and reconstructing coherent, model-preferred prompts via Target-Specific Prompt Optimization (TSPO), the method effectively produces prompts that better capture user intent and enhance text–image semantic alignment.The addressed problem is highly practically relevant for creative image generation and the proposed approach is easy to reproduce in real-world applications", "questions": "1. Please clarify the definition  of “Baseline.”\nWhat exactly is the “Baseline” in Tables 1–3?  Is it simply the raw user prompt or an internal standardized prompt? Why does this baseline outperform some optimized methods?\n2. Please justify the selection of comparison methods.\n  Given that the Baseline achieves higher semantic alignment scores than other existing methods, please explain why NeuroPrompts, Promptist, and BeautifulPrompt were chosen as baselines of optimized methods.  Additionally, why were other families of methods (eg, multi-objective optimization methods, diffusion-specific) not considered for inclusion in the comparison?\n3. Robustness and Ablation\n  The paper would benefit from a more detailed ablation analysis showing the relative contribution of each module. Which component—SERE (Self-Reflection) or TSPO (Target-Specific Prompt Optimization) drives the largest improvements across different types of semantic concepts (e.g., objects, attributes, relations)? It would also be helpful to report whether the model’s gains are consistent across different diffusion backbones (e.g., SD v1.5 vs Flux-dev) and whether any module exhibits degradation or instability when applied to more complex or compositional prompts.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- By explicitly addressing the problem of semantic omissions, the authors provide a fresh direction for prompt engineering research, shifting the focus from “visual beauty” to semantic faithfulness.By detecting and repairing semantic omissions between user text and generated images, the framework improves  intent alignment, which are crucial for real-world creative and design applications.\n- The approach of decomposing prompts into atomic semantic units (entities, attributes, relations), using a Visual-Language Model (VLM) to detect absent ones, and then reassembling an optimized prompt with an LLM is conceptually new and technically creative  on the target problem. The integration of LLM reasoning and VLM verification within a self-reflective pipeline demonstrates strong originality.\n- The use of two authoritative benchmarks, DSG-1k (ICLR 2024) and TIFA v1.0 (ICCV 2023), is appropriate and technically justified, as both measure fine-grained semantic alignment between text and image — directly matching the paper’s objective.", "weaknesses": "- Limited diversity of baselines: All three comparative methods (NeuroPrompts, Promptist, BeautifulPrompt)  share similar reinforcement-learning-based optimization paradigms.  The omission of  other omitted categories may weaken the empirical scope.\n- Improvements over the baseline are modest (≈ 4–5 points on DSG/TIFA).  Given that VisualPrompter adds several modules and increases inference time (Table 6), the cost–benefit balance remains questionable.Since all reasoning and evaluation rely on Qwen2 and Qwen2-VL, it is also unclear how robust the approach is under different model backbones.\n- The paper provides limited empirical evidence on the effectiveness of individual modules. More detailed analyses would strengthen the work — for example, examining which concept types (e.g., objects, attributes, or relations) or which modules (SERE vs TSPO) contribute most to performance gains.", "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)