FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning Dataset and Comprehensive Benchmark
Authors: Rongyao Fang
Aldrich Yu
Chengqi Duan
Linjiang Huang
Shuai Bai
Yuxuan Cai
Kun Wang
Si Liu
Xihui Liu
Hongsheng Li
Venue: ICLR 2026 Poster (2026)
Keywords: Text-to-image
Reasoning
Generation chain-of-thought
dataset
benchmark
Primary area: datasets and benchmarks
TL;DR: A Million-Scale Text-to-Image Reasoning Dataset and Comprehensive Benchmark.

Abstract: The advancement of open-source text-to-image (T2I) models has been hindered by the absence of large-scale, reasoning-focused datasets and comprehensive evaluation benchmarks, resulting in a performance gap compared to leading closed-source systems. To address this challenge, We introduce FLUX-Reason-6M and PRISM-Bench (Precise and Robust Image Synthesis Measurement Benchmark). FLUX-Reason-6M is a massive dataset consisting of 6 million high-quality FLUX-generated images and 20 million bilingual (English and Chinese) descriptions specifically designed to teach complex reasoning. The image are organized according to six key characteristics: Imagination, Entity, Text rendering, Style, Affection, and Composition, and design explicit Generation Chain-of-Thought (GCoT) to provide detailed breakdowns of image generation steps. PRISM-Bench offers a novel evaluation standard with seven distinct tracks, including a formidable Long Text challenge using GCoT. Through carefully designed prompts, it utilizes advanced vision-language models for nuanced human-aligned assessment of prompt-image alignment and image aesthetics. Our extensive evaluation of 19 leading models on PRISM-Bench reveals critical performance gaps and highlights specific areas requiring improvement. Our dataset, benchmark, and evaluation code will be released.

Published as a conference paper at ICLR 2026
FLUX-REASON-OM & PRISM-BENCH: A MILLION- SCALE TEXI-TO-IMAGE REASONING DATASET AND
COMPREHENSIVE BENCHMARK
Rongyao Fanglot
Shuai Bai*, Kun Wang', Si Liu', Xihui Liu
Aldrich Yu**, Chengqi D
Linjiang Huang', Hongsheng Li
Yuxuan Cai',
'CUHK MMLab
HKKU MMLab
'BUAA
*Alibaba
*Sensetime
*Equal Contribution
*Corresponding Author
ABSTRACT
The advancement of open-source text-to-image (T2I) models has been hindered by the absence of large-scale, besooing-focussee datasets and comprehensive evalu- ation benchmarks, resulting in a performance gap compared leading closed- source systems. To address this challenge, We introduce FL.U-RReson-66 and PRISM-Bench (Precise and Robust Image Synthesis Mcasurement Benchmark). B.UX-Reeson-6hN is massive dataset consisting of 6 million high-quality FLUX FLUXX-genrrted images and 20 million bilingual (English and Chinese) descrip- tions specifically designed teach complex reasoning. The image are organized according to six key characteristics: Imagination, Entity, Text rendering. Style, Affection, and Composition, and design explicit Generation Sfain-of-Thuuglee (GCoT) to provide detailed breakdowns of image generation steps. PRISM- Bench offers novel evaluation standard with seven distinct tracks, including a formidable Long Texi challenge using GCo'.. Through carefully designed prompts, it utilizes advanced vision-language models for nuanced human-aligned assess- ment of prompt-image alignment and image aesthetics. Our extensive evalua- tion of 19 leading models on CRISM-Bench reveals critical performance gaps and highlights specific areas requiring improvement. Our dataset, benchmark, and evaluation code are released at http:///gthbbcoomooogggaaa/g/gnn
INTRODUCTION
Text-to-image generation models enable machines to produce engaging and coherent images, and have quickly become a key research direction generative artificial intelligence (Ho et al., 2020; Liu et al., 2022; Rombach et al., 2022; Wu et al., 2022; Liang et al., 2022; OpenAl, September 2023; Podell et al., 2023; Chen et al., 2023b; Fang et al., 2024; Duan et al., 2025; Esser al., 2024; Li et al., 2024c; Black 2024; Gong et al., 2025; Gao et al., 2025: Cai et al., 2025; Google, 2025b; Ope- nAI. 2025b; Wu et al.. 2025; Google, 2025c). Among these models, state-of-the-art closed-source models (c.g.. Sommnii.55-Flsh--mmas
(Google, 2025c), GPT-Image-1 (OpenAI, 2025b)) demon- strate strong instruction following and controllable synthesis capabilities, establishing new bench- marks for T2I generation. In contrast, open-source models (Podell et al., 2023; Chen et al., 2023b; Stability-AIl 2022; 2024a;b; Chen et al., 2025a; Xie al., 2025) exhibit limitations when processing complex and detailed prompts.
This disparity stems from two challenges. First, the research community lacks large-scale, high- quality, and comprehensive open-source datasets. Most existing datasets consist of web-crawled image-text pairs (Changpinyo et al., 2021; Schuhmann et al., 2022: Sharma et al., 2018: Hu al., 2022: Gadre et al., 2023). These data are unable to be used to endow T2I models reasoning ca- pabilities, which is the key for synthesizing complex scenes. Although reasoning- exist, they tend to be narrow in scope (Fang et al., 2025). For example, the GoT dataset (Fang al., 2025) primarily focuses on layout planning through bounding boxes, offering limited coverage of other broader dimensions of reasoning. Second, there is absence of comprehensive evaluation
datasets

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Published as a conference paper at ICLR 2026
Cutee Morkie puppy perched inside pink box labeled "Fabulous" Adorned with matching pink bow and surrounded by flffly fnathers and vibrant pink flowers, the puppy scudes charm and playfulness.
Composition:
Affection: Erweloped world
puppy embodies innocence and wonder, radiating an ineeiitible charm that gnites heartwarming sense joy and hope.
of
Cute and charming, Morkle "Fabulous"
siss adorabyy perched Inside pastel pink cardboard labeled fluffy
black script. The puppy, tipped with dark the puppy fluffy
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and
geetly the edge the box, adding playful touch to the composition. Surrounding creating whimskall background soft, uniform pink, which complements the overal color stheme and draws attention the central subject. The image captures a perfect blend of innocence and playfulness, evoking a heartwarming sense of joy and hope through its soft textures, vibrant hues, and meticulous arrangement.
enchanting atmosphere. The
Fabulous
rendering Compasttonn Affechion
DRAGON Text rendering. Style, Entry
Imogination, Composition
Imagination, Affection, Style
Affecction Style
Figure 1: Showease of FLUX- six different characteristics and generation chain of thought. Keywords related to characteristics in the captions are highlighted in color.
benchmark aligned with human judgment. Most existing benchmarks (Ghosh et al., 2023; Huang et al., 2023; Hu et al., 2023; Chefer et al., 2023; Bakr et al., 2023; Feng et al., 2022: Yu et al., 2022; Wu et al., 2024; Cho et al., 2023) evaluate only limited number of dimensions while neglecting key aspects such as imaginative capacity and emotional expression. Additionally, these benchmarks rely on object detectors (Ghosh al., 2023) and crude CLIP scores (Hessel et al., 2021; Lin al., 2024), resulting in evaluation metrics that are easily saturated and fail to effectively differentiate the model's ' actual performance.
To resolve these problems, in this work, we introduce FLUX-Reason-6M and PRISM-Bench. FLUX-Reason-6MM is a 6-million-seal synthesized dataset designed to incorporate reasoning ca- pabilities into the architecture of T2I generation. PRISM-Bench serves as comprehensive and discriminative benchmark with 7 independent tracks that closely align with human judgment.
To build MLUX-Reason-6M. we leverage the powerful capabilities of advanced image generation models and vision-language models to develop a robust data pipeline that includes large-scale data collection, synthesis, mining, annotation, filtering, and translation. We identify six key characteris- ties essential for T21 generation: Text rendering (lypography and legibility) and Composition (layout and spatial relations), which are common in existing researches (Huang et al., 2023; Wang et al., 2025; Chen et al., 2025b; 2023a; Tuo et al., 2023), and introduce Imagination (creative concep- tual blending). Affection (emotional expression), Entity soooweegg-groonddee (artistic and photographic style) capture more nuanced and creative aspects of generation. Fur- thermore, we introduce generation chain of thought (GCoT), which forms the core of our dataset. GCoT are detailed descriptions that break down the content and structure of images by comprehen- sively integrating the six characteristics instead of merely focusing on layout planning, providing supervision for training the reasoning capabilities of T2I models. As a result, FLLUX-Reason contains 6 million high-quality images synthesized with FLUX.1-dev (BlackForest, 2024) and 20 million associated captions, each in both English and Chinese. On average, each image contains least three annotations from different categories. The creation of this dataset takes 15,000 A100 GPU days. Examples from the dataset can be found in Figure 1.
depiction), and Style
Building on the six characteristics and GCoT, we design PRISM-Bench. We first organize into seven distinct tracks: the six categories from BLUU-Reassoe Text track that leverages the GCoT captions to test models' complex instruction following ability. Each track contains 100 carefully selected and constructed prompts. We leverage the sophisticated cognitive judgment of advanced languaela models (GPT-4.1 (OpenAI, 2025a) and Qwen2.5- VL-72B (Bai et al., 2025)) evaluate prompt-image alignment and aesthetic quality, thereby pro-
and uniquely challenging Long
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Published as a conference paper at ICLR 2026
viding more reliable and human-alignnn assessment of model performance. We evaluate 19 lead- ing T2I models, including SOTA closed-source models such as Gemini2.5-Flams F-as--aalaaaaa Image (Google, 2025c), GPT-Image-1 (OpenAI, 2025b), as well as top open-source models like Qwen-Image (Wu et al.. 2025). The results indicate that the gap between open-source and closed-source models is widening. even the most advanced closed-source models still have room for improvement in certain dimensions.
Our contributions are summarized as follows:
We present FLUX- Reason,.,,,-,,,, a 6M-scale dataset for reasoning-oriented T2I, featuring 20M bilingual captions and, to our knowledge, the first multi-dimensional, million-saale generation chain-of of-thought annotations tailored to controllable image synthesis.
We introduce PRISM-Bench seven-track benchmark that evaluates Imagination, Entity, Text rendering, Style, Affection, and Composition, using GPT-4. and Qwen2.5-VL-72B as judges for nuanced and robust assessment.
We conduct comprehensive evaluation of 19 leading models, revealing revealing key gaps across characteristics and outlining concrete opportunities for future research.
We will publicly release the dataset, benchmark, and evaluation suite lower the financial and computational barriers, enabling research on cassobll generative models.
FLUX-REASONE DATASET
The core limitation of current souresource T2I datasets is the absence of structured signals for complex reasoning (Podell et al., 2023; Li et al., 2024b; Stability-AI, 2024a). Most flat im- age-caption collections that describe content but not the compositional rationale. We design FLUX- Reason-6M as a principled framework for learning T2I reasoning: beyond high-quality images, supplies structured supervision that teaches the rules of composition and control. An overview of the curation pipeline appears Figure 2.
2.1 SIX CHARACTERISTICS AND GENERATION CHAIN-OF-THOUGHT
We identify and define six key characteristics that are crucial for modern T2I models, including Imagination, Entity, Text rendering, Style, Affection, and Composition. These categories intention- ally overlap to reflect the multifaceted nature of complex scene synthesis. For example, "The Eiffel Tower rendered the style of Van Gogh's 's Starry Night" requires both Entity fidelity and Style trans- fer. At the core of the dataset the generated chain of thought. As shown in Figure 1, GCoT breaks down semantic intent and compositional logic into multiple steps, providing rich supervision that teaches models not only vocabulary-piss raphy, emotional tones, and stylistic choices. This GCoT-centered multi-dimensional framework forms the conceptual foundation of FLUX- Reason-6... Detailed definitions are in appendix B.
associations but also underlying rules for layout, typog-
2.2 SYNTHESIZING A HIGH-QUALITY VISUAL. FOUNDATION
To avoid the uneven quality of web data, we select the powerful FLUUX. (BlackForest, 2024) as our synthesis engine, which produces detailed and aesthetically consistent images. We first rewrite captions from LAION-Aestheties (Schuhmann al., 2022) using vision language model to obtain high quality, broad descriptions as starting points. However, this strategy underrepresents Imagina- tion and Text rendering, thus we implement targeted augmentation.
Progressive Imagination Cultivation. We initiate progressive generation process. First, we use Gemini-2.55Pro (Google, 2025a) to generate 200 imaginative seed prompts. Then we randomly select 10 of these prompts, input them as in-context examples to Qwen3-32B (Yang et al., 2025) for expansion, and increase the model's temperature parameter to enhance novelty and diversity.
Pipeline for Textual Rendering. We develop three-stage pipeline. First, we mine Laion-2B (Schuhmann et al., 2022) with Qwen2.5-VL-32 (Bai et al., 2025) to find images with clear, legible text. For each hit generate precise captions that describe
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A, Synthesizing a Visual Foundation
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D. Original Caption Integration and Bilingual Translation
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Figure 2: An overview of L.UX--Raaoo--M. data curation pipeline. The entire process com- pleted using 128 A100 GPUs over a period of 4 months.
the text content, visual presentation, and context. We then synthesize images with FLUX.1-de so that the rendered text matches the caption.
Combining the baseline and augmentations yields 8M images for subsequent filtering, multidimen- sional categorization, and dense annotation, ensuring consistent standard of quality and relevance for FLUX-Rcason-6M.
2.3 VLM-POWERED QUALITY FILTERING AND MCLTIDIMENSIONAL SCORING
We convert million synthesized images into carefully curated resource using pipeline powered by vision language models. The goal is to ensure high visual quality and reliable aligmment with the six characteristics. We first apply a basic quality screen with Qwen- VI. to remove blur, artifacts, and structural errors. The full rubric and thresholds to score each remaining image on all six characteristics separately. The model assigns relevance scores from to 10 for each category. We calibrate thresholds for each characteristic and assign categories accordingly. When an image meets multiple thresholds, it is assigned multiple labels. For the Text rendering category, we add a specialized pass that rejects images with illegible, low contrast, or incorrect text. Implementation details are provided in appendix B.3. From the original million candidates, about million pass all checks. These images are quality validated and earry multidimensionall labels, and they used for the final dense annotation stage.
in appendix B.2. Then again Qwen-V VL
2.4 VLM-DRIVEN DENSE CAPTIONING AND REASONING FRAMEWORK CONSTRUCTION
With the filtered and classified images in place, we generate multidimensional captions and construct reasoning framework. The goal is to move beyond simple descriptions and create annotations that explain what is in the scene and how it is organized.
Category-Specific Dense Captioning. Using Qwen-VL, create category aware captions for each assigned characteristic. For Entiry, the caption prioritizes precise identification and attributes. For Style, describes techniques, aesthetics, and visual motifs. For Text rendering. captures typography and layout. The same principle applies to Imagination, Affection, and Composition. Images with multiple labels receive parallel captions from each perspective, which yields denser supervision than generic captioning methods (Li et al., 2020; Jia et al., 2021; Yu al., 2024).
Generation Chain-of-Thought Synthesis. We then build GCoT, a central feature of FLUX- Reason-6M. The VLM receives the image and all category specific captions and returns detailed
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Compostion fre
1.093
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reogiotive 14.02%
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Asslidiia 74.FS
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Test rendering
Raw prompt source
Category percentage
100
Prompt lengih
Figure 3: Left: Three subsets of raw prompt sources. Middle: Image category ratio. Right: Prompt Suite Statisties.
plan that explains scene elements, their interactions, layout choices, and guiding principles. These narratives cover spatial relations, color and style decisions, typography quality, and emotional tone. The result is explicit template for reasoning and provides structured signals for training.
2.5 ORIGINAL CAPTION INTEGRATION AND BILINGUAL RELEASE SCALE
Original Caption Integration. We reintegrate high quality captions from LAION- Aesthetics (Schuhmann et al., 2022) when they match the FLUX generated images. Qwen-Ve VL serves as an alignment judge that scores caption-image alignment. Captions above a calibrated threshold are kept expand linguistic variety while avoiding drift. After merging original captions, category specific captions, and GCoT annotations, the corpus contains about 20 million unique cap- tions. Figure summarizes source proportions, counts and shares of caption types, and word count distributions.
Comprehensive Bilingual Translation. We translate entire caption set into Chinese using VLM. For the to preserve the task meaning. The result is a bilingual resource that supports broad use across regions and applications.
Text rendering category, any English string that must appear in the image remains in English
PRISM-BENCH
To address the evaluation gap in T2I synthesis, introduce PRISM-Bench. It consists of seven distinct tracks, each containing 100 carefully selected prompts designed to explore the capability boundaries of T21 models. These tracks align with six characteristics, namely Imagination, Entiry, Style, Text rendering, Composition, and Affection, and include challenging Long Text built from GCoT prompts. An overview our PRISM-Bench shown Figure 4.
3.1 PROMPT DESIGN AND CONSTRUCTION
Each track contains 100 prompts split into two sets of 50. The first set is representative samples from FLUUX-Reaoo--GM. The second set targets hard cases within the track.
Representative Prompt Sampling. For each track, we collect the top 10,000 prompts based on the scores section 2.3. We use k-means (Krishna & Murty, 1999) for semantic clustering (k=50) and select prompts closest to each centroid, removing them from the dataset. In this way, obtain 50 prompts that cover major themes and reduce bias toward frequent patterns.
Category-Specific Prompt Construction. The other 50 prompts for each track come from careful curation. For Text rendering, we define pools for content length, typography, and placement context. Content ranges from single word multi-sentence slogans. Typography spans handwrit- ing, serif, sans serif, and graffiti. Placement covers signs, garments, packaging, and other surfaces, with explicit constraints on viewpoint, lighting, and contrast. We sample combinations from these pools and ask Gemini2.5-Pro to compose natural prompts, with checks for legibility and string accu- racy. The other tracks, including Imagination, Entity, Style, Affection, Composition, and Long Text, follow the similar principle and detailed in appendix C.I.
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Published as a conference paper at ICLR 2026
A. Prompt Design and Construction
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Figure 4: An overview of the prompt design and evaluation protocol of FRISM-Benchh
PRISM-Bench-ZIN We construct PRISM-Bench-ZH by translating all English prompts into Chi- nese with Gemini2.5-Pro. For Text rendering track, do not simply translate all text into Chinese but adapt it according to Chinese contexts. For example, "A bottle labeled 'WHISTLEPIG" featuring 'SMOKED BARREL-AGEE
RYE' sits alongside clear whiskey glasses, showcasing refined presentation of the spirit" is translated as H
MI 3
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Human-in-the-Loop Refinement. Five human reviewers participate in the validation of prompts: two senior PhD) candidates (with 3-4 years of experience AIGC), one master's student (with expertise in numan-computer interaction research), one graphic designer (possessing over 5 years of experience in digital art and visual communication), and one postdoctoral researcher (with extensive research background in AIGC). All the reviewers instructed evaluate each candidate prompt on grammatical correctness and clarity, logical consistency of the described scene, feasibility for T2I generation, absence of ambiguity in spatial/attribute descriptions, alignment with the specific track's focus, absence of duplication with existing prompts, and natural language flow and creative coherence, Only prompts approved by all reviewers entered the final benchmark. Prompts with disagreements were revised iteratively discarded. The final benchmark contains 700 prompts that are diverse, representative, challenging, and bilingual.
3.2 EVALUATION PROTOCOL
We use vision language models judges to assess prompt-image alignment and image aesthetics. This yields a two-axis evaluation that captures both correctness and visual quality. We employ GPT- 4.1 and Qwen2.5-VLL-728 representative closed source and open source judges.
Fine-Grained Alignment Evaluation. Alignment scoring uses track-specific instructions that di- rect judges focus the core challenges of each track. For each generated image, the judge provides a one-sentence explanation and assigns a score from I to 10. Detailed criteria for all tracks are provided appendix C.2.
Uniform Aesthetic Evaluation. Aesthetic scoring adopts a unified evaluation standard across all tracks. The VLM considers factors such as lighting, color harmony, detail rendering, and overall visual appeal, providing one-sentence rationale along with numerical score ranging from to 10.
For each model and track, we average the alignment and aesthetic scores across 100 prompts, map- ping them scale of O to 100, and report their mean the comprehensive track score. The

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Published as a conference paper at ICLR 2026
Table 1: Quantitative results PRISM-Bench evaluated by GPT-4.1. Ali., Aes., and Avg. denote alignment, aesthetic, and average scores, respectively. The best result is in bold and the second best result underlined.
Insaginationn
Failtt
Affection
Composition
Long
Overall
Model
Ali. Aes. Arg.
All.
Acs.
Ang.
Ali.
Acs.
Avg.
All.
Acs.
Ang.
All.
Acs,
Avg.
Ali
Aes.
Arg.
Ali.
Aes.
Arg.
Ali.
Aes. Avg.
SDL.5
56.6 36.1
36.4
5.0
33.1
20.6
55.3
64.4
61.0
61.1
51.0
304
44.9
44.2
SD2.1
47.9 41.2
44.6
60.9
46.7
53.8
11.2
30.6
20.9
62.7
58.6
00.7
06.7
58.5
02.6
65.7
53.1
39.4
40.1
28.2
34.2
50.7
45.5 48.0
SIXI.
61.1
58.3
13.8
25.4
75.4
75.4
39.6
58.9
604
IanusPro778
20.4 65.8 68.1
67.1
51.9
59.5
15.5
36.7
26.1
71.4
73.8
72.6
79.2
71.5
75.4
83.7
51.0
72.4
62.4
39.7
51.1
64.2
57.2 60.7
Playground
10.4
77.0
41.0
62.6
FLUX.I schnell
63.3 66.2 64.8
61.8 51.2
56.5
46.2 54.1
50.2
68.6
70.1
69.4
75.4
69.9
72.7
85.1
67.5
76.3
69.4
49.7
59.6
67.1
61.2 64.2
Bagel
69.4 68.7
50.3
50.2
37.4
71.3
69.5
881.7
90.5
73.1
81.8
68.1
55.3
61.7
06.7
65.1
Hagel-CoT
68.4 74.2 71.3
62.4 60.0 61.2
23.2 40.1
51.7
64.4
70.1
67.5
(7.1
80.5
83.8
88.5
77.9
X3.2 64.0
52.0
50.0
05.4
65.0
65.2
SD3-Mediim
61.0 65.6
63.3
64.8
56.3
60.6
30.8
53.1
43.0
74.8
75.6
75.2
78.7
80.3
79.5
85.5
79.1
82.3
61.5
46.1
53.8
65.66
65.4
SD3. SD3.5-Medium
69.5 73.0 71.3
72.8 63.7
68.3
53.3 50.1
41.7
77.4
80.3
78.9
84.9
85.5
85.2
89.4
79.2
84.3
63.3
50.5
56.9
70.1
68.9
69.5
HiDrcam 11 Dev
68.2 69.7 69.0
72.0 67.0
69.5
53.4 64.1 59.8
68.7
78.6
73.7
84.2 83.1
83.7
87.00
79.8
83.7
58.1
47.5
52.8
70.3
70.0
702
Larg
73.3 712 72.3
71.9
74.3
52.0
65.8
58.9
84.2
97.1
85.2
86.2
87.0
84.7
85.9
64.3
51.7
73.9
73.7
PLUX.1 day
68.1 74.0
71.1
70.7
71.2
71.0
48.1
64.5
56.3
72.3
80.5
76.4
89.3
91.1
89.7
89.0
84.6
86.8
70.5
58.5
64.6
72.4
74.9
73.7
71.5 73,0
67.5
47.5
55.4
83.55
84.0
85.8
74.3
74.7
HiDeeam-I--Fll
14.4 75.6 15.0
74.4
72.4 73.4
58.2
70.4
64.3
K1.4
84.8
83.1
(90.1
88.8
89.5
90.1
85.4
87.8
63.8
52.0
57.9
76.1
75.6
75.9
SDEDream
73.8
56.1
80.5
79.6
Qwen-Imag
R0,5
78.6 79.6
79.3
73.2 76.3
54.3
68.9
61.6
84.5
88.7
86.6
91.6
89.1
90.4
93.7
86.9
90.3
83.8
65.1
74.5
81.1
78.6
79.9
Comini,.5-Haah-llmm
12.4
88.6
66.2
09.2
M1.5
90.8
90.7
96.0
92.5
90.5
76.2
87.1
85.3
GPU-Image-1 [High]
96.2
$6.6
26.4
90.0
86.3
SM.2
68.8
30.1
74.5
92.8
93.3
93.1
90.7
50.9
90.8
96.2
89.4
92.8
83.8
72.8
73.3
56.9
85.6
86.3
final model score is calculated as the average across all seven tracks. We evaluate leading closed- source and open-source T21 systems using both PRISM-Bench and PRISM-Bench-2H to facilitate a comprehensive comparison.
EXPERIMENTS
We evaluate 19 advanced image generation models on the PRISM-Benche including Gemini2.5- Flash-Image (Google, 2025c), GPT-Image-1 (OpenAI, 2025b), Qwen-Image (Wu et al., 2025),
SEEDream 3.0 (Gao et al., 2025), FLUX series (BlackForest, 2024; 2025), HiDream series (Cai al., 2025), Stable Diffusion series (Rombach al., 2022: Podell et al., 2023; Stability-AI, 2022; 2024a;b), Playground (Li et al., 2024b), Bagel (Deng et al., 2025), and JanusPro (Chen et al., 2025c). Results under the GPT-4.1 judge appear in Table 1. The corresponding Qwen-VL. judge results are provided in appendix A. Mcanwhile, we evaluate several models with Chinese language capabilities on the PRISM-Bene's ZH. including GPT-Image-I, 1, Qwen-Image, SEEDream 3.0, HiDream series, and Bagel. Table 2 reports results, and the Qwen- VI. results in appendix A.
4.1 RESULTS AND ANALYSIS ON PRISM-BENCH
As shown in Table 1, the overall results highlight the advantages of current SOTA closed-source models. GPT-Image-I achieves the highest total score of 86.3, closely followed by Gemini2.5- Flash-Image with 85.3. These models outperform others across nearly all evaluation tracks. Among the remaining models, competitive tier led by lwen-Image is emerging. Although there is still noticeable performance gap compared to top models, these models represent significant leap forward from the open-source community. HiDream-I1-Fut and FLUUX. K-rea-Kre also achieve excellent results, indicating rapid progress the field. Evolution within model series also evident, with SDXL showing substantial improvement over SD1.5, while the newer SD3.5-Large further narrows the gap with top-performing models. Among all tracks, Style and Composition are relatively mature domains, whereas Text rendering and Long text remain the most challenging. Imagination and Entity recognition also distinguish top models by their emphasis on creative synthesis and factual accuracy. Figure 5 presents representative Long text case, illustrating the difficulty in adhering
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From a high vantage polnt, person's left hand holds a bleck smartphone in the foreground, creating clever frame within-o frame effect. The screen the dispiyys crystui-clear image transporent wirdow rother thon o device. The rood, flanbed by a dense, lush conony dark green trees that form natural tunnel, stretches the distones Seweral cors, iocluding Mork,, white, and distnct red whith,, are vioble draing along the multilone axphatt, curved, modern street lamp extends the roedway from the left, nestled
perfectly alinedd
real-worlt
though
looking
the folagg. The photegraph shot with shallw depth of field, cousing the hand the immeskote sofly blurred drowing polette is moedy and cirematic, with cool, descturated tones emphasizing the deep greens of the trees and the dork grey of the rood, which markes with white pointed lires.
fureground
eye shorp focus
phone
just beyond
Justification: Accurately major elements (high vantage, left hond, black smortphone, frome- within-a-frame, road with dense green conopy, modern street lamp, shallow depth). Minor deduction for imperfect phone screen alignment and only moderate cinematic desaturation.
Alignment score:
Justification: Frume-within--frome is convincing, but slight unnotural blending at street lamp/tree conopy transition hand oppeors stiff maintoined.
texture. flows, overoll realism Aesthetic score:
GeminiZ. 5-minni----lash-mmmaee
Justification: All møjor elements present (high vontage, left hand with black smortphone, frame- within-a-frame, road with cors including red one, dense green conopy, shollow depth, moody palette). deduction: street lomp only partially visible, not clearly among faliage. Alignment score:
Strong taitee-wtthn----frmee and realism, but major flaw: phone screen and background misaligned (rood perspective, street lamp position), breaking the transporent window illusion; hand anatomy off [unnatural thumb curve). Deductions for logical and anatomical issues.
Justification:
Image [High]
Aesthetic score:
Justification: Critical flow: smortphone screen misoligned with scene-road and vehicles don't match, breaking transporency illusion. Hand slightly distorted; street lamp base owkwardly blended with fallage, reducing realism.
Alignment score:
key elements present (high vantage, left hand, black smartphone, frame-within- frame, road, specified car colors, tree tunnel, street lomp, shollow depth, moody polette). Minor issues: street lomp not clearly modem/curved; desaturation only moderate.
Justification:
Qwen--Image
Aesthetic score: 2
Strong hrame-withnnoo-rrmme alignment and depth of field, but minor hand-phone blending issues: fingers are slightly stiff and thumb position is subtly owkword, offecting
Justification:
anatomical realism.
Alignment score: 6
Captures all major elements (high vantage, black smartphone, soine--wthi----raaaee rood, cars, dense canopy, modern lamp, shallaw DOF, moody palette), but the hand is not clearly identifiable as a left hand and cinemotic, cool, desaturated grading is only moderately present. Aesthetic score:
Justification:
SEEDream 3.0
Figure 5:
Showcase of Long text track in the PRISM-Bench. GPT4.1 is not only required score based on image-text alignment and image aesthetics, but also to provide brief justification.
to dense instructions. The Qwen- VL evaluation results yields consistent rankings, as detailed in appendix A.
4.2 RESULTS AND ANALYSIS ON PRISM-BENCH-ZHH
The evaluation results from FRISM-Bench-e reveal a distinct performance hierarchy, with GPT- Image-1 establishing its dominance at total score of 87.5. It consistently leads across most tracks, including Imagination, Entity, Style, Affection, and Composition, demonstrating exceptional cre- ative interpretation, knowledge foundation, and spatial arrangement in response to Chinese prompts. Meanwhile, SEHDream 3.0 and Qwen-Image demonstrate strong competitiveness across all tracks, frequently performing nearly on par with the leader. Particularly noteworthy is the performance of SEEDream 3.0 and Qwen-Image in Text rendering, which stands in stark contrast to the gen- eral weakness observed in English text generation. Among these, SEEDreaa 3.0 and GPT-Image-1 share the highest average score, with SEEDream 3.0 achieving the highest aesthetic score, indicat- ing its capability to render high-quality Chinese characters. The robust performance of these models validates the benchmark design's of culturally adaptive prompts in Chinese and highlights signif-
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Table 2: Quantitative results PRISM-Bench-ZA evaluated by GPT-4.1. The best result is in bold
and the second best result is underlined.
Imagination
Entily
Test rendering
Style
Affection
Composition
Long text
Overall
Modd
Ali.
Avg.
Avg.
Avg.
Aug.
Ali.
Avg.
Avg.
Avg.
Avg.
HiDreum-Il-De
47.3
41.1
44.2
52.8
49.0 50,9
35.2
14.5
24.9
64.5
52.4 58.5
76.3
66.5
71.4
67.6
68.3 68.0
41,1
46.4
43.8
55.0 48.3 51.7
HiDream-Il-Pall
53.6
50.5
62.0
16.3
80.9
73.5
45.4
48.1
57.9
Bagel-CoT
75.1 69.3
72.2
53.3
58.8
56.1
42.6
16.3
29.5
73.6
66.6
70.1
81.2
78.0
79.6
74.0
83.6
78,
50.7
64.3
57.5
64.4
62.4
634
Bagel
72.8 64.7
68.8
53.9
62.2
58.1
69.2
29.0
39.1
73.9
68.4
71.2
$1.4
73.5
77.5
69.0
89.8
79.4
58.1
68.7
63.4
65.5
65.2
65.4
Quen-Image
86.6
76.6
81.1
SDBDreum 3.0
77.2
77.8 77.5
77.6
78.6
78.1
19.7
71.9
75.8
87.8
83.2
85.5
58.7
85.1
$6.9
87.7
94.4
91.1
74.3
82.7
78.5
81.9
82.0
82.0
GPT-Image-- [High]
88.8
89.2
67.7
75.8
91.7
92.8
92.4
77.2
80.8
97.7
87.5
icant advancements in handling Chinese typography. Nevertheless, consistent with PRISM-Bench test results, the Long text track remains the greatest challenge for all models. While GPT-Image-1 again leads in this category, the generally lower scores highlight the substantial obstacle of under- standing and synthesizing lengthy, multifaceted Chinese instructions. This further emphasizes the urgent need for reasoning-fooe focusee datasets like the next generation of truly intelligent T2I models. The Qwen-Vi VL judge results and visualizations for PRISM-Bench-ZIT are included in appendix A.
address existing gaps and train
4.3 HUMAN EVALUATION ON PRISM-BENCII
We further conducted a comprehensive human evaluation on FRISM-Bench and compared human scores with our automatic metrics. We randomly sampled 20 prompts from each of the 7 tracks and generated images using diverse T2I models (BAGEL, FLLUX.1-dey. Qwen-Image, Gemini2.5- Flash-Image yiclding 560 smage-prompp pairs. We recruited 10 well-educated graduate students to conduct human evaluation. These evaluators represented diverse disciplinary backgrounds includ- ing arts, design, and STEM fields. Each image was randomly assigned to three evaluators who con- ducted independent evaluations. Evaluators were instructed to rate each image according to the same prompts provided to the VLM, assessing both coompt--mage alignment and aesthetic quality. They then provided composite score (1-10) for each image based on these criteria. We averaged the three scores to obtain human reference score for each prompt-image pair. We computed Spearman's 's and Kendall's T between human scores and three automatic evaluators, including CLIPScore (Hes- sel et al., 2021), our VT---aseed scores using Qwen2.5-VL. GPT-4.1. We also calculated inter-judge correlation between GPT-4. and Dwwn2.55VL-722 across the average scores of all 19 models each track. The results are shown in Table 3. Our VLM- based metrics achieve substantially higher correlation with human judgment than CLIPScore across all tracks, validating PRISM-Bench metrics. Besides, the consistently high correlation (p > 0.86 across all tracks) demonstrates re- markable agreement between the two judges despite their different architectures and training. This validates the robustness and reliability of PRISM-Bench' 's evaluation protocol.
and our VI.M-based scores using
more human-aligned evaluation standard than traditional
RELATED WORKS
5.1 DATASETS FOR T21
Large-scale web-scraped corpora (Changpinyo et al., 2021; Schuhmann et al., 2022; Sharma et al., 2018; Hu et al., 2022; Gadre et al., 2023) form the foundation of most open-source T21 models. These datasets provide broad coverage across domains, but at the cost of heterogeneous quality and weak alignment signals. While such datasets describe what is in an image, they typically lack structured supervision about how or why the content is composed, thus limiting the ability to train reasoning-oriented models. To partially address this issue, the GoT dataset (Fang et al., 2025) scales 9M samples and introduces reasoning elements for composition via bounding box layouts. However, this dataset is primarily assembled from existing sources (e.g., 2022), JourneDDB (Sun al., 2023)), leading inconsistent quality and imbalanced distributions
(Schuhmann et al.,

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Published as a conference paper at ICLR 2026
Table 3: The correlation between automatic evaluation metrics and human evaluation, and the cor- relation between the two metrics used in MeNSh-BBneh (GPT-4.1 I vs. Qwen2.5-Ve. VL).
Imagination
Entity
Text rendering
Style
Affection
Composition
Long text
Metries
CI PPoore
0.415
0,306
0.371
0.292
0.645
0.498
0.427
0.318
0.356
0.283
0.698
0.502
0.223 0.154
Ours (Qwea2.5-VL-72B)
0.527
0.382
0.585
0.456
0.709
0.547
0.658
0.484
0.503
0.372
0.735
0.576
0.662
0.497
Ours (GPT-4.1)
0,580
0.453
0.626
0.481
0.722
0.569
0.694
0.531
0.559
0.397
0.741
0.585
0.683
0.510
I vs. Qwen2.5-VL
0.861
0.720 0.872
0.704
0.978
0.903
0.973
0.888 0.872
0.739
0.935
0.806 0.982 0.947
of image content and style, and its chain-of-thought remains largely layout-centrie. In contrast, FLUU-RRason-66M is constructed through a unified synthesis and annotation pipeline, targeting six complementary features (Imagination, Entity, Text rendering, Style, Affection, Composition), and provides multi-aspect natural language generation chain of thought supervision. To our knowledge, large-scale datasets with such multi-dimensional, bilingual GCoT remain scarce in public literature.
5.2 EVALUATION AND BENCHMARKS OF T2I
Evaluating T2I models is challenging.
metrics (e.g.. CLIPScore (Hessel et al., 2021))
and detector-driven checks (Ghosh et al., 2023) have been widely used due to scalability, but can sat- urate and sometimes mis-rank outputs (Lin al., 2024). Benchmarks like T21-CompBench (Huang et al.. 2023), TIFA (Hu et al.. 2023), Conceptmix (Wu et al., 2024), and DSG (Cho et al.. 2023) provide targeted coverage (e-g.. attribute binding. spatial relations). yet often emphasize limited set of dimensions and rely automated proxies that do not fully reflect human preferences. A complementary direction is leveraging vision-language models as judges (Hu et al., 2024; Li et al., 2024a). Strong VLMs (e.g., GPT-4.1 (OpenAI, 2025a), Qwen2.5-VL. (Bai et al., 2025)) can per- form fine-grained, instruction-aware comparisons that better correlate with human assessments than pipelines using only detectors or CLIP, especially for nuanced aspects like creativity, emotional tone, and long-instruction compliance. tion into six tracks aligned with our six characteristies plus Long text track using GCoT prompts, and employing VLM judges to score prompt-image alignment and aesthetics.
follows this direction by organizing evalua-
6 CONCLUSION
In this work, we address critical gaps in text-to-image models through two key contributions: the FLUX-Reasone 66M dataset and the PRISM benchmark. FLUX-Reason-66M is an extensive 6-million- image dataset with 20 million high-quality prompts specifically designed for reasoning, featuring novel generation chain-of of-thought that imparts image synthesis logic across six characteristics to models. To measure progress, we develop PRISM-Bench, a comprehensive seven-track benchmark utilizing advanced VLMs for fine-grained human-aligned evaluation. Our extensive experimen- tation across 19 models reveals that while leading closed-source systems demonstrate impressive performance, all models struggle with complex tasks such as text rendering and long instruction following, underscoring the necessity of our work. By publicly releasing the dataset, benchmark, and evaluation code, we provide the community with essential tools for training and evaluating the next generation of more intelligent and capable T2I models.
7 REPRODUCIBILITY STATEMENT
We describe the dataset creation process section 2, with additional details provided in appendix B. The benchmark construction is outlined in section 3, with further elaboration presented in appendix C. The prompts used for benchmark evaluation are exhibited in appendix C.2 and C.3. Our dataset, benchmark, and code will be publicly available.
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Table 4: Quantitative results on PRISM-Bench evaluated by Qwen2.5-VIL-22B. The best result is in bold and the second best result is underlined.
Imagination
Eatlyy
Text rendering
Style
Affection
Compusition
Long text
Overall
Model
All.
Acs.
Avg.
Acs.
Ali.
Avg.
Aes.
Ali.
Avg.
AlL
Aeg.
Aes:
Arg.
All.
Avg.
SDI.5
60.7 23.7
32.2
61.2 52.7 56.9
11.4
24.1 17.8
56.7
61.5
59.1
66.9
60.7
63.8
57.5
53.4
55.4
47.3
26.8
37.0
48.8
43.3 46.0
SD2.1
4899 26.4
386
66.0 57.6
16.7
24.0
66.5
68.5
65.3
64.38
58.3
61.5
50.7
293
54.0
508
SDXL
54.5 34.1
44.3
71.1
65.0 08.0
18.6 37.3
27.9
71.7
72.6
72.1
78.7
66.5
72.6
72.2
67.8
70.0
54.1
34.5
44.3
00.1
54.01
57.0
Playgrund
56.7
15.3
23.6
74.6
68.8
72.2
55.3
62.2
57.1
Ragel
68.0 45.0
56.5
67.6
53.4
60.5
20.4
42.3
35.8
69.0
69.7
69.3
87.1
66.7
76.9
86.6
59.2
77.9
64.5
50.3
57.3
67.5
56.6
62.0
Ragel-CooT
44.1
53.4
20.4
35.8
69.7
87.1
50.2
67.5
62.0
JanusPro 7B
65.0 38.8
51.9
68.6
63.5 66.0
23.1
50.3
36.7
70.7
75.2
72.9
80.7
68.0
74.3
82.4
71.1
76.7
63.9
49.0
56.4
64.9
59.4
62.1
FLUX.I schnell
54.3
71.5
78.6
56.4
68.3
64.7
SD3 MDd-Medium
64.3 37.7 51.0
69.4
63.3
66.3
58.5
63.3
50.9
74.6
79.5
77.0
80..5
75.5
78.0
85.6
79.5
X2.5
63.4
50.3
56.8
68.0
64.2 66.1
SDS.S-Medium
34.7 4939
70.9
71.7
36.6
50.5
80.0
73.9
77.9
85.4
53.0
M3.2
50.6
08.6
66.8
FLUX, I-dev
65.5 42.9 54.2
70.6
61.9
66.2
52.3 73.0
62.6
72.6
74.2
13.4
86.0
72.9
79.4
67.4
75.8
81.6
70.5
53.8
62.1
72.1
64.9
68.5
HiDrea--I--Dev
68.8 45.8 57.3
73.5
68.
70.8
56.7
75.7
66.2
70.3
77.4
73.8
88.2
74.3
81.2
84.7
78.5
81.6
64.0
49.3
72.3
69.6
SD5.5-
66.7 43.4 55.0
72.7
74.8
(53.6
73.1
63.3
77.3
78.2
17.7
85.6
73,9
79.7
87.8
80.9
84.3
52.2
73.4
70.6
IHDream II Full
73.0 44.0 58.5
76.3
72.8
74.5
60.5
76.4
68.4
81.4
81.5
81.4
90.0
76.5
83.3
88.5
80.3
84.4
66.3
48.6
57.4
76.6
68.6 72.6
FLUX. --Kraddwe
43.1
70.7
51..7
63.0
86.6
82.6
73.4
74.4
74.0
Ques-Iaage
75.5 37.4 56.5
79.5 64.5 72.0
57.99
71.2
64.5
N6.6
84.4
85.5
89.9
70.4
80.1
93.9
70.5
86.7
76.8
70.9
73.8
80.0
68.3 74.1
SDEDream
74.2
58.8
03.6
89.1
80.1
76.2
Gemini2.5 S--hhshh--aag
84.7 38.1
61.4
56.0 76.7
81.3
72.8
64.3
78.5
89.5
87.8
88.6
94.3
74.8
$4.5
91.2
88.2
89.7
76.3
80.6
78.4
85.0
75.8 80.4
GPT-Image-1 (ffigh)
66.7
76.8
87.8
58.1
77.5
82.7
Table 5: Quantitative results on PRISM-Bench-ZH evaluated by Qwen2.5-VL-72B. The best result is in bold and the second best result is underlined.
Imagination
Eniity
Text rendering
Style
Affection
Compooltion
Long text
Overall
Model
Acs. Avg.
Acs.
Aug.
Ali.
Acs.
Aug.
Ali.
Aug.
Acs.
Avg.
Ali.
Acs.
Airg
Ali.
Acs.
Arg:
Ali.
Avg.
HiDeeam-Il-Dev
48.3
24.6 36.5
52.6
54.1 53.4
18.6
35,3
27.0
59.0
68.3 63.7 65.9 62.3 64.1
66.5
64.6
65.6
54.2
38.6 46.4
52.2 49.7 50.9
MiDream-1--uu
51.2
30.8
41.0
60.1
61.3
60.7
20.7
40.6
30.7
61.5
73.8
69.2
65.2
69.1
67.2
72.4
69.0
70.7
57.1
42.8
50.0
55.9
55.3 55.6
Bagel
36.3
50.5
55.5
66.0
71.3
66.2
70.6
81.3
72.2
62.
54,9
61.5
54.3 57.9
Hagel-Caa
64.4
36.6
50.5
62.6
53.8
58.2
25.2
51.9
38.6
65.4
76.7
71.1
74.0
65.0
69.5
M1.3
71.3
76.3
61.4
46.6
54.0
62.0
57.4 59.7
Quen-Emage
64.3
75.2
79.2
64.5
89.8
72.6
75.0
70.1
SEEDeeam 3.0
21.4
36.6
56.0
74.8
23.8
24.3
70.7
58.0
79.4
74.1
58.0
81.1
79.0
21.4
25.2
90:30
83.2
96.8
73.0
21.2
22.1
26.2
23.2 74.2
GPT-Image-1 [High]
73.0
37.6
55.3
82.1
73.1
89.9
77.1
84.8
78.0
77.8
77.9
91.9
85.7
72.4
76.3
78.0
77.7
A ADDITIONAL RESULTS OF PRISM-BENCH AND PRISM-BENCH-ZA
A.1 RESULTS WITH QWRN2.5
The evaluation results using Swen2.5-VLL-72. are summarized in Table 4 and Table 5. We present examples of Chinese text rendering across different models in Figure 6.
A.2 TRAINING WITH FLUX-REEEEE REASON- AND THE EFFECT OF GCOT
To demonstrate the practical utility of our dataset, we fine-tuned Bagel (Deng et al., 2025) on FLUX-
Reason-6M with two configurations: (i) using only the standard captions (no GCoTT), and (ii) using an additional step where the model first predicts reasoning text (supervised by GCoT) conditioned on the prompt, and then generates the image. Both were trained on 16 A100 GPUs for 10k steps. We evaluated the results on PRISM-BB BRISM-Bench and GenEval (Ghosh et al., 2023). The results in Tables 6 and 7 indicate that FLUX-Reason-65 Reason-6M does deliver practical benefits and that GCoT supervision further strengthens compositional and cntrruction-olllowwee abilities, even on an external benchmark. This suggests that the reasoning skills learned from our dataset are not confined to our own benchmark but are generalizable.
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-
, x & "RRAK :
Justification: The text is perfectly spelled and fully legible, but its integration is unrealistic and flat, appeoring unnaturally impased on the wotch face without proper curvature.
8
Alignment score: 6
Justification: The watch face is rendered with a large block of text that covers the entire dial, making it implousible as a functional wristwatch since there are no numerals and the text would obstruct the movement and readability of the honds, constituting a pritiaa in physicol plausibility.
Aesthetic score: 2
Image 1 [High]
Justification: The text is perfectly spelled and legible, but its integration poor appears unnaturally flat and pasted onto the watch face without following the dial's curvature or lighting.
Alignment score: 6
REAIORRAHR ZURSEAN..
The watch is generally well-rendered, but there is significant flaw in the form of illegible or nonsensical branding/text at the bottom of the dial, which breaks realism for a luxury watch, and the placement and alignment of the sentimental text on the dial awkward and would be impractical for an actual watch face.
lustification:
Aesthetic score: 5
Qwen-Image
Justification: The text the watch face contains garbled ond nonsensical characters the middle lines, deviating significantly from the requested heartfelt message and rendering ports of it illegible.
Alignment score: 2
Stea TOKE ARRUCES SEALOVROG,
Justification: The watch is generally well-rendered with realistic materials and lighting, but some characters are distorted, overlapping, or nonsensical. The position and alignment of the text on the dial is olso distorted.
Aesthetic score: 4
SEEDream 3.0
The text on the watch face garbled, contains significant spelling and wording errors compared to the prompt, and is partially illegible, resulting in severe failure of text accuracy.
Justification:
ERIRRES' EFP' BROIIIKA R&B CERES
Alignment score: 1
Justification:
The watch face contains garbled and nonsensical text, which breaks logical coherence; additionally, the text is not cleanly rendered, and the overall image is slightly blurry.
Aesthetic score: 2
Bagel
Figure 6: Showcase of Text rendering track in the PRISM-Bench-ZH.
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Table 6: Performance of BAGEL fine-tuned with FLUX-RReson--e PRISM-Bench. Ali., Aes., and Avg. denote alignment, aesthetic, and average scores, respectively. The best result is in bold.
Imagination
Aflection
Composition
Modd
Ali. Aes. Avg. All. Aes. Ang. All. Aes. Aog. Ali. Ads, Ang. All. Aes. Ang. All Aes. Ang.
All Aes. Ang. All. Aes. Asg.
BAGEL (hsscline)
69.4 68.0 68.7 39.0 50.1 54.6 302 44.5 37.4 67. 71.3 09.6 81.7 514 $1.6 90.5 73.1 $1.8
68.1 55.3 61.7 667 6344 65.1
LUX-Resson-MM
75.3
39.6
$2.9 89.
76.7
72.3
PLUU--Reanae DeN- 7488 79.5 77.2 71.7 642 68.0 37.
49.2 43.3 754 82.6 79.0 88.6 90.1 $7.9 948
82.7 88.8
76.9 61.5
69.2 738 72.8 73.3
Table 7: Performance of BAGEL fine-tuned with FLUX-Reason-6M on GenEval. The best result is in bold.
Model
Single Obj.
Obj.
Counting
Colors
Position
Color Attri.
Overall
BAGEL (baseline)
0.99
0.94
0.81
0.88
0.64
0.63
0.82
LLUX-Reason-Oo 6M
1.00
0.96
0.85
0.89
0.64
0.64
0.83
MAUX--Rcaoo--MM + GCoT
1.00
0.97
0.88
0.94
0.71
0.67
0.86
A.3 MORE QUALITATIVE VISUALIZATIONS
Figures 7, 8, 9, 10, and 11 show more visualizations on PRISM-Bench and PRISM-Bench-ZH.
B DETAILS ON THE CONSTRUCTION OF FLUX-REASON-CN
B.1 DETAILED DEFINITIONS OF SIX KEY CHARACTERISTICC
The detailed definitions of six reasoning characteristics are:
Imagination: This category is populated with captions and images that represent surreal, fantastical, or abstract concepts. The prompts describe scenarios that defy real-world physics or combine disparate ideas in novel ways (e.g.. "a city made of glass where rivers of light flow"). The resulting images provide rich examples of creative synthesis, offering data that pushes beyond literal interpretations.
Entity: This focuses on sewwlede-ggrouddee depiction. It contains image-caption pairs where the emphasis is on the accurate and detailed generation of specific real-world objects, beings, named entities. Captions in this category are often rich with specific attributes (e.g., "Lionel Messi dribbling past defenders in the World Cup final"), providing the model with data for high-fidelity knowledge-awee
generation.
Text rendering: To address well-known weakness in generative models, this category con- sists of images that successfully and legibly incorporate English text. The corresponding captions provide explicit instructions for the text's content, style, and placement within the image (e.g., "a sign that reads 'FLUX-Reesonn-GM in glowing neon letters")). This provides direct and clean data for training models in typographic control.
Style: This characteristic curates vast and diverse library of artistic and photographic styles. The captions explicitly reference specific art movements (e.g.. Cubism, Impression- ism), visual techniques (e.g.. long exposure, fisheye lens), and even the aesthetic signatures of famous artists. The images serve as high-quality examples of the successful application these styles.
This category contains image-caption pairs designed to connect abstract emo- tional concepts to concrete visual representations. The captions use evocative language to describe mood, feeling, or atmosphere (e.g.. "a sense of peaceful solitude", "a chaotic and joyful market scene"). The corresponding images translate these intangible concepts into visual cues, such color palettes, lighting, and subject expression.
Affection:
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colorful assortment of toy food items, including two homburgers, three hot dogs with various toppings, container of yellow French fries, cup with white ice cream, and cleor glass of cola with a strow, are neatly arranged on plain white surfoce.
20
Playground:
FLUX.1-schnelt:
Bagel:
SD3 Medium:
WAbi
S03.5-Medium: 3, 2
HiDream-I1-Dev: 3,
SD3.5-Large:
FLUx.1-dev:
|II/
Faze
FLUX.11Kreaadeee dev: 5,
HiDream-I1-Ful:: 9,9
SEEDream 3.0: 8, 10
Owen Image: 10, 10
Geminiz.5- Faaahii----aas---aaee 6, 10
GPT-Image-1: 10, 10
Figure 7: Showcase of Composition track in the FRISM-Bench. Alignment score marked in red, and aesthetic score is marked in violet.
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Amidst the golden glow of o sunrise-drenched cityscape, the morning rush hour unfolds as a symphony of movement and solitude, where the vibrant red buses and cloaked figures navigate the transient dance of urban life, evoking both the anonymity the crowd and the quiet introspection of individual journeys.
PCP
ACTO
SD 1.5: 6.
SD 2.1: 6,
SDXL:
FMM
JanusPro-7B:99
Playground:
FLUX.1-schnell:
Figure 8: Showcase of Affection track in the PRISM-Bench. Alignment score is marked in red, and aesthetic score is marked in violet.
whimsical seascope where colossal toasters float serenely among fluffy clouds, baking them into golden-broo brown bread loaves that drift like stormy cumulus ocross the azure waters below.
Bagel: 5, 10
SD3-Medium:
SD3.5-Medium:
HiDream-I1-Dev:1 10
SD3.5-Large: 4,
FLUX.1-dev: 7, 10
Figure 9: Showcase Imagination track in the Bench. Alignment score marked in red, and aesthetic score is marked in violet.
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Professor LeCun smiles while at desk, holding pen and notebook, ready to work on world model project.
FLUX.1-Krea-dev:e.
HiDream-I1-Full: 4,
SEEDream 3.0: 5,
WIERDECIEIT
WORLD MODEL
MINAAN MODEL
Qwen-Image:
Gemini2.55Flash-lmaae:
GPT-Image-1:
Figure 10: Showcase of Entity track in the PRISM- aesthetic score is marked in violet.
Alignment score is marked in red, and
E 7 BORK + E
#
E TE MIX
Bagel: 4,
HiDream-I1-Dew:
MoDream-I1-Fulle
SEEDream 3.0:7,9
Qwen-Image:
GPT-Image-1: 10
Figure 11: Showcase of Style track in the PRISM-Bench-2 -ZH. Alignment score is marked in red, and aesthetic score is marked in violet.
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Composition: This focuses the precise arrangement and interaction of objects within a scene. The captions use explicit compositional language, including prepositions (e.g,, under, behind, next to) and relative positioning. The images provide clear examples of how these complex spatial instructions are executed correctly.
B.2 FOUNDATIONAL. QUALITY FILTERING
We employ Qwen-V VI. as automated quality assurance inspector. Its task is to analyze each image for fundamental clarity and structural consistency. This step identifies and discards images suffer- ing from undesirable artifacts such as excessive blurring, disruptive noise, significant structural distortions in objects and figures. By pruning these low-quality samples, we establish a foundation of images with both aesthetic and structural integrity for the subsequent, more complex annotation and filtering phases.
B.3 TYPOGRAPHIC QUALITY FILTERING FOR TEXT RENDERING
Given the unique challenges of typographic generation, we implement specialization filtering stage exclusively for the Text rendering category. To ensure the dataset provides clear and reliable signals for this difficult task, we again employ Qwen-VI. VL strict typographic quality inspector. It per- forms detailed scans of images flagged for the Text rendering category and filters out any instances containing low-contrast, distorted, nonsensical text. This crucial step guarantees the highest fi- delity of data for this characteristic.
B.4 VLM-IUMMN ALIGNMENT IN DATASET FILTERING
We conducted human validation of the Qwen- VL-based filtering used in dataset construction to verify alignment between Qwen- VL's filtering results and human assessment. We randomly sample 500 images that Qwen-VI VL had rejected and 500 that it had accepted during quality filtering. 10 graduate students judge each image as "accept" or "reject" according to our criteria (blur, severe artifacts, structural failures, illegible/nonsensical text). Each image was randomly assigned two evaluators who conducted independent evaluations. An image is considered "accepted by humans" both annotators marked it as acceptable. Finally, 95% of the images matched Qwen-V VL's decision, indicating strong agreement between the automatic filter and human judgment.
B.5 QUALITY CONTROL FOR GCoT ANNOTATIONS
Our GCoT quality control involves three stages. First, Only images that pass strict VLM quality checks and receive high scores on relevant characteristics receive GCoT annotations. This ensures the visual foundation reliable. Second, we design detailed prompts for GCoT generation that require the powerful VLM to integrate all six category-specific captions (when applicable). main- tain consistency with factual captions (entity names, text content, spatial relations), and produce coherent step-by-step narratives about scene composition. Third, randomly sampled 500 images with their GCoT annotations and asked 10 graduate students to identify any errors, including hal- lucinations, contradictions with factual captions, logical inconsistencies. 96% of GCoTs passed this review, demonstrating high annotation quality. The 4% failure cases typically involved minor attribute mismatches rather than fundamental logical errors.
C DETAILS ON THE CONSTRUCTION OF PRISM-BENCH
C.I CATEGORY-SPECIIC PROMPT CONSTRUCTION
50 prompts for each track come from our careful curation. Specifically,
Imagination: first divide imaginative concepts into several major categories, such as physical impossibilities and surreal narratives. Then we use an LLM (Gemini2.5-Pro) to randomly select elements from one or multiple categories to generate corresponding prompts.
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famous landmarks, specific animal and plant species, historical figures, and branded objects. Then we utilize LLM to randomly select to three entities generate corresponding prompts.
Entity: We curate lists of different categories of entities:
Text rendering: We design text content of varying lengths (e.g..
"Welcome the future ..."), different font styles (e.g. handwritten script, graffiti spray paint), as well surfaces and positions (e.g.. on a wooden sign, on a t-shirt). By sys- tematically combining elements from these three categories through LLM, we generate the corresponding prompts.
Style: We define four major style categories, including art movements (e.g. Impression- ism, Cubism), mediums (e.g., oil painting. watercolor), photography techniques (e.g.. long exposure, macro photography), and digital/modern aesthetics (e.g., pixel art, vaporwave). These comprise total of 25 detailed styles, and we use LLM to generate prompts for each style.
Affection: We use Plutchik's Wheel of Emotions Mohsin Beltiukov (2019) as a foun- dational source, selecting not only the eight primary emotions (joy, trust, fear, surprise, sadness, disgust, anger, anticipation) but also their milder and more intense forms. The LLM is asked to create corresponding prompts based on these emotions.
Composition: We build several attribute pools, including colors, quantities, sizes, spatial relationships, and more. For each generation, we draw several attributes from each pool and have the LLM freely combine them to create prompts featuring multiple objects with various relationships.
Long text: We select 50 high-quality images from the all their corresponding captions. These are fed into Gemini2.5-Pro for long-text expansion, ultimately resulting in 50 challenging prompts.
dataset along with
C.2 ALIGNMENT EVALUATION
For each generated image, VLM provides a one-sentence justification and a score from I (extremely poor alignment) to 10 (perfect alignment) based on the following specifpe criteria:
Imagination: The evaluation focuses on whether the model successfully synthesizes the de- scribed novel surreal concepts, rewarding creative and coherent interpretations of imag- inative ideas.
Entity: The alignment score based on the accurate rendering of specific, named real- world entities, including their key defining features and context.
Text rendering: The scoring criteria are strict, focusing on the legibility, spelling accuracy, and the precise positioning of specified text within the image.
Style: VLM is instructed to assess the fidelity of the generated image to the explicitly requested artistic or photographic style (e.g., "Impressionism." "long exposure"), checking for characteristic techniques.
Affection: The assessment centers on whether the image effectively conveys the specified mood, emotion, or atmosphere through visual cues like color, lighting, and subject expres- sion.
The prompt for VLM emphasizes verifying the spatial arrangement of ob- jects, their relative positions (c.g., "to the left of." "behind"), color appearance, and correct object counts as dictated by the text.
Composition:
Long text: For this challenging track, the evaluation measures the model's ability to incor- porate high density of details from the complex, multi-sentence GCoT prompts.
This targeted approach allows for more precise and meaningful measurement models' abilitics across each distinct category. Tables 8, 9, 10. 11, 12, 13 and 14 show the prompts of different tracks for the evaluation of prompt-image alignment.
C.3 AESTHETIC EVALUATION
Table 15 presents the prompt score the image aesthetics.
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You are highly critical AI evaluator for text-to-image generation benchmark. Your task is meticulously analyze generated image against prompt describing imaginative object. You will provide a one-sentence justification for point deductions and a score from 0 to 10 in JSON format. Your evaluation must be stringent. Scoring Philosophy (Apply this strictly):
9-10 (Exceptional): Flawless. All described features are seamlessly and creatively integrated into a coherent, believable whole. The object feels truly unique and masterfully executed.
7-8 (Good): The object is well-designed and incorporates almost all key features from the prompt with good coherence.
5-6 (Average): A competent attempt. The object includes the main features de- scribed, but they appear "stitched together" or incoherent. Key details are missing or misinterpreted. The result recognizable but flawed collage of ideas.
3-4 (Poor): The object is confusing mess, missing most of the core features described in the prompt.
0-2 (Failure): The object is completely wrong or the image is unrelated to the prompt.
Track-Specific Instructions: Imaginative Object Generation Start at 10 and deduct points for each failure. Focus on coherence.
Missing Core Features (-4 to -6 points): Fails to include a defining feature of the object.
Lack of Coherence (-3 to -5 points): The described parts are present but look like a poorly assembled collage rather than a single, integrated object.
Misinterpreted Attributes (-2 to points): key material or quality is rendered incorrectly.
Incorrect Context (-1 to -3 points): The object is rendered well, but the surround- ing environment described in the prompt is wrong.
Required Output Format:
Your response must be single JSON object containing a one-sentence "justification" for point deductions and "score":
"justification""
"score":
text prompt: ftext prompt}
The prompt template for prompt-image alignment of Imagination track. The text high- lighted in eyan is replaced with the specific prompt for each image being evaluated.
Table 8:
D THE USE LARGE LANGUAGE MODELS
Throughout the preparation of this manuscript, utilized Large Language Models, specifically GPT-5, as writing assistance tool. The primary use of the LLM was for improving grammar, spelling, and overall readability. All authors have reviewed and edited the final and take full responsibility for the paper's content.
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You are a highly critical Al evaluator for a text-to-image generation benchmark. Your task meticulously analyze generated image against text prompt naming a specific entity. You will provide one-sentence justification for point deductions and score from 0 to 10 in ISON format. Your evaluation must be stringent.
Scoring Philosophy (Apply this strictly):
9-10 (Exceptional): Flawless. The entity is rendered with photographic accuracy, and the surrounding scene perfectly matches all details the prompt.
7-8 (Good): The entity highly recognizable and accurate, and the overall scene is good match for the prompt with only minor deviations.
5-6 (Average): A competent attempt. The entity is recognizable but has clear flaws, OR the entity is perfect but the surrounding scene described in the prompt is incorrect. accurate entity in wrong context not a success.
3-4 (Poor): The entity is barely recognizable or is a generic substitute. The scene is also likely incorrect.
0-2 (Failure): The entity is wrong or absent, and the image is unrelated to the prompt.
Prrei-Specificc Instructions: Specific Entity Generation
Start 10 and deduct points for each failure. Prioritize overall alignment, then entity accu- racy.
Incorrect Scene/Context (-4 to -6 points): The entity correct, but the back- ground, style, action described in the prompt is completely wrong. This is major failure.
Unrecognizable or Flawed Entity (-3 to -5 points): The entity is poorly rendered, has significant anatomical structural errors, or looks like generic version.
Missing Scene Details (-2 to -4 points): The scene is generally correct, but key descriptive elements are missing.
Minor Entity Inaccuracies (-1 to points): The entity is recognizable but has small, specific inaccuracies.
Required Output Format:
Your response must be single JSON object containing one-sentence "justification" for point deductions and a "score":
"Justification"
"score": ....
text prompt: prompt)
Table The prompt template for prompt-image alignment of Entity track. The text highlighted in cyan is replaced with the specific prompt for each image being evaluated.
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You are a highly critical AI evaluator for a text-to-image generation benchmark. Your task is to meticulously analyze generated image that should contain rendered text. You will provide one-sentence justification for point deductions and score from 0 10 in JSON format. Your evaluation must be stringent.
Scoring Philosophy (Apply this strictly):
9-10 (Exceptional): Flawless. The text is perfectly spelled, legible, and seamlessly
integrated into the scene with correct perspective, lighting, and texture.
7-8 (Good): The text is perfectly spelled and legible, with only very minor issues in its integration.
5-6 (Average): competent attempt. The text is spelled correctly but is poorly integrated into the scene. It may look flat, have unnatural lighting, be placed awkwardly.
3-4 (Poor): The text contains significant spelling errors or is partially illegible, even if the placement is roughly correct.
0-2 (Failure): The text is nonsensical, completely wrong, or absent.
Track-Specific Instructions: In-Image Text Generation
Start at 10 and deduct points for each failure. Text accuracy is paramount.
Spelling or Wording Errors (-6 to -8 points): Any deviation from the requested text string. This is the most severe failure.
Poor Integration (-3 to -5 points): The text looks pasted on, with incorrect per- spective, lighting, or shadows for the scene.
Illegibility (-3 to -5 points): The characters are garbled, distorted, or difficult to read.
Incorrect Placement/Font (-2 to -4 points): The text is the wrong object in the wrong location, or the requested font style is ignored.
Required Output Format:
Your response must be a single JSON object containing a one-sentence "justification" for point deductions and "score":
"jutiffiaation"
"score":
text prompt: ftext prompt)
Table 10:
The prompt template for prompt-image alignment of Text rendering track. The text highlighted in cyan is replaced with the specific prompt for each image being evaluated.
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You are highly critical Al evaluator for text-to-image generation benchmark. Your task is to meticulously analyze generated image against text prompt requesting specific style. You will provide one-sentence justification for point deductions and score from to in JSON format. Your evaluation must be stringent.
Scoring Philosophy (Apply this strictly):
9-10 (Exceptional): Flawless. The image perfectly captures the content and cutes the requested style with deep, nuanced understanding of its aesthetics, tech- niques, and historical context.
7-8 (Good): The content is correct, and the style is clearly recognizable and well- executed, with only minor deviations from the style's core principles.
5-6 (Average): competent but superficial attempt. The content is correct, the style is applied like simple filter. It captures the most obvious stylistic clichés but misses the nuance the art form.
3-4 (Poor): The content is correct but the style is wrong, OR the style is vaguely correct but the content wrong.
0-2 (Failure): Both content and style are wrong.
Track-Specifie Instructions: Specific Style Application Start at 10 and deduct points for each failure. Penalize superficiality.
Incorrect Content (-5 to -7 points): The image shows the wrong subject matter,
even if the style is correct. This is major failure.
Superficial Style Application (-4 -6 points): The image uses only the most obvious clichés of a style without understanding its underlying principles.
Missing Stylistic Elements (-2 to -4 points): The image misses key technical identifiers of the style.
Inconsistent Style (-1 to -3 points): Parts of the image are in the correct style while other parts are not.
Required Output Format:
Your response must be a single JSON object containing a one-sentence "justification" point deductions and "score":
"justification";
"score":
text prompt: ftext prompt)
The prompt template for prompt-image alignment of Style track. The text highlighted in cyan replaced with specific prompt for each image being evaluated.
Table 11:
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You are a highly critical Al evaluator for a text-to-image generation benchmark. Your task meticulously analyze generated image against its text prompt using strict, two-step process. You will provide a one-sentence justification and a score from 0 to 10 in JSON format. Your evaluation must be stringent.
Scoring Philosophy (Apply this strictly): Core Principle: The primary criterion is always Text-Image Alignment. The image must first be a faithful depiction of the literal content de- scribed in the prompt. The evaluation of the emotional aspect is secondary, but important, step.
9-10 (Exceptional): Flawless. The image perfectly depicts all literal content from the prompt AND masterfully visualizes the specified emotion with depth and cre- ativity.
7-8 (Good):
The image depicts all literal content correctly, AND the emotional visualization is strong and accurate.
5-6 (Average): A competent attempt. The image depicts the literal content cor- rectly, but the emotional visualization is weak, superficial, or relies heavily on clichés.
3-4 (Poor): Major failure in content alignment. Key subjects, objects, settings from the prompt are missing or wrong. The emotional evaluation largely irrele- vant because the content is incorrect.
0-2 (Failure): The image shows no significant resemblance to the literal content of the prompt.
Track-Specifie Instructions: Two-Step Evaluation You must follow this sequence. Start at 10 and deduct points for each failure. Step 1: Verify Content Alignment (Primary Criterion) First, ignore the emotional com- ponent and check only the physical description. Does the image contain the correct subjects, objects, setting, and actions?
Content Mismatch (-6 to -8 points): This is the most severe failure. The image is missing key subject, setting, object described in the prompt. If the core content is wrong, the score cannot be high.
Attribute Error (-3 to -5 points): The content is generally right, but key attributes are wrong.
Step 2:
Evaluate Emotional Visualization (Secondary Criterion) Only after confirming the content alignment, evaluate the emotional layer.
Emotional Dissonance (-3 to -5 points): The image content is correct, but the mood is completely wrong. The lighting, colors, and composition fail to evoke the requested emotion.
Missing Nuance Clichéd Symbolism (-2 to points): The content correct, obvious cliché without
but the emotion is handled superficially. The image any depth, or captures generic version of the emotion.
Literal Interpretation Emotion (-2 to points): The content is correct, but the emotion is interpreted in clumsy, literal way.
Required Output Format: Your response must be a single ISON object containing one- sentence "justification" for point deductions and a "score":
"justification""
"score":
text prompt: prompt)
Table 12:
The prompt template for prompt-image alignment of Affection track. The text high- lighted cyan replaced with the specific prompt for each image being evaluated.
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You are highly critical AI evaluator for text-to-image generation benchmark. Your task is to meticulously analyze generated image against its text prompt, focusing object count and spatial relationships. You will provide a one-sentence justification and a score from 0 to 10 in JSON format. Your evaluation must be stringent. Scoring Philosophy (Apply this strictly):
9-10 (Exceptional); Flawless. Every object, count, attribute, and spatial relation- ship is rendered with perfect accuracy and logical consistency.
7-8 (Good): The main objects and their primary relationships correct. There might be a single, minor error a secondary object's s attribute or position.
5-6 (Average): A competent attempt. The image contains the correct primary ob- jects, but there significant errors in their count, spatial relationships, interac- tions.
3-4 (Poor): Major errors object count or the relationships between primary ob- jects. The scene is fundamentally incorrect.
0-2 (Failure); The wrong objects are depicted, the image is completely unrelated to the prompt.
Track-Specific Instructions: Object Layout and Relationships
Start 10 and deduct points for each failure. Be systematic.
Incorrect Object Count (-3 to -5 points): The number of a key object is wrong.
Incorrect Spatial Relationship (-3 to -5 points): The relative position of key objects is wrong.
Incorrect Object Attributes (-2 to points): key object has the wrong color, size, or other specified attribute.
Incorrect Interactions (-2 to -4 points): A described interaction between objects subjects is missing or wrong.
Minor Positional/A Attribuue Errors (-1 to -2 points): A secondary object is slightly misplaced or has minor incorrect attribute.
Required Output Format:
Your response must be single JSON object containing one-sentence "justification" for point deductions and a "score":
jjustification"
"score":
text prompt: (text prompt)
Table 13:
The prompt template for prompt-image alignment of Composition track. The text
highlighted in cyan is replaced with the specific prompt for each image being evaluated.
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You highly critical AI evaluator for text-to-image generation benchmark. Your task to meticulously analyze a generated image against a long, detailed text prompt. You will provide one-sentence justification for point deductions and score from 10 in JSON format. Your evaluation must be stringent.
Scoring Philosophy (Apply this strictly):
9-10 (Exceptional): Flawless. The image comprehensively and coherently visual- izes virtually every detail from the prompt, from major elements to minor attributes. The image captures all major elements and a clear majority of the secondary details and attributes. The omissions are minor.
7-8 (Good):
5-6 (Average): competent attempt. The image correctly depicts the main subject and setting but omits significant number of secondary details and attributes. The core there, but the richness is lost.
3-4 (Poor): The image captures only one of the major elements and misses almost descriptive details.
0-2 (Failure): The image fails to capture any of the major elements described in the prompt.
Track-Specific Instructions: Long Text Comprehension
Start 10 and deduct points for each failure. Be detail-oriented critic. First, identify the Major Elements (primary subject, setting, main action). Second, list all Secondary Details (other objects, characters, specific attributes). Deduct points for each omission error.
Missing Major Element (-5 to points): Fails to include the primary subject,
setting, or action.
Missing Majority of Secondary Details (-3 to -5 points): The image feels generic because it ignored most of the specific deseriptors that gave the prompt character.
Incorrectly Rendered Detail (-2 to points): detail is included but rendered incorrectly.
Each Minor Omission (-1 point); For every small, specific detail that missing, deduct a point.
Required Output Format:
Your response must be a single JSON object containing a one-sentence "justification" for point deductions and "score":
"justification
"score":
text prompt: (text_prompt
Table 14:
The prompt template for prompt-image alignment of Long text track. The text high- lighted in cyan is replaced with the specific prompt for each image being evaluated.
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You are hyper-critical quality assurance inspector for a text-to-image generation bench- mark. Your task is to evaluate images with forensic, microscopic scrutiny. Your primary directive to penalize any deviation from physical, anatomical, and logical coherence, un- less such deviations are explicitly requested by the text prompt. Assume all subjects and environments must be perfectly sound and plausible by default.
Scoring System:
You will start with perfect score of 10 and deduct points for any flaws you identify. A single significant flaw should prevent a high score.
Flaw Categories (Deduct points for each instance):
Critical Failures (-7 to -9 points):
Any violation of the fundamental anatomical or structural integrity of the main subjects. This includes inconsistencies in form, function, natural appear- ance.
breakdown logical or physical plausibility within the scene, when not specified by the prompt. Prominent, distracting digital artifacts, watermarks, signatures that ruin im- mersion.
The central subject rendered as grotesque or nonsensical, when not specified by the prompt.
Significant Flaws (-4 to -6 points):
Noticeable warping, distortion, or lack of convincing texture on key objects or surfaces.
Unnatural blending, texture repetition, or other clear indicators AI synthesis that break realism.
LLac of sharpness or resolution in the primary subject, making crucial details indistinct.
Incoherent or illogical features on secondary elements.
Minor Imperfections (-1 to -3 points):
Slight compositional awkwardness or minor issues with lighting and shadow that don't 't break realism.
Minimal blurriness or noise in secondary, non-focal areas of the image.
Faint, non-distracting artifacts that are only visible upon close inspection.
Required Output Format:
Your response must be single JSON object containing a one-sentence "justification" for point deductions and a "score":
"justification "score":
text prompt: (text_prompg
The prompt template for image aesthetic evaluation. The text highlighted in cyan is replaced with the specific prompt for each image being evaluated.
Table 15:
30


Reviews:
[{"review_id": "3iCORNeUoN", "reviewer": "Reviewer_rbSW", "summary": "This work present a training dataset, FLUX-Reason-6M, and a benchmark, PRISM-Bench. FLUX-Reason-6M contains 6M FLUX-generated images paired with carefully curated prompts that requires reasoning during image generation. Each text-image pair comes with multiple captions and a Generation Chain-of-Thought (GCoT) that provide details breakdowns of the image generation process.\n\nThe PRISM-Bench is a challenging benchmark that covers the complex reasoning abilities introduced in FLUX-Reason-5M. The benchmark uses strong MLLMs like GPT-4.1 and Qwen2.5-VL-72B to do evaluations. Experiments show big performance gaps across models, and hights the areas that need improvements.", "questions": "1. Have you tried train any model with FLUX-Reason-6M? Can it yield improvements on tasks?\n\n2. Is there any evidence that GCOT can improve model training? Any ablation experiments showing that?\n\n3. Have you conduct any human evaluation on PRISM-Bench? How well does the evaluation metrics align with human judgements?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The field needs resources toward more complex and reasoning-heavy text-to-image generation tasks. This paper provides both the training data and the evaluation benchmark for it.\n\n2. The paper contains detailed description of the carefully designed data curation pipeline. The pipeline make sense and can generate data of high quality. The benchmark is further examined by humans to ensure quality. These make the datasets included in this paper valuable to the community.\n\n3. The paper is well-written and easy to read.", "weaknesses": "1. The dataset FLUX-Reason-6M only used a single text-to-image model, FLUX.1-dev to create images. It potentially has the model's biases and inherits its failure modes.\n\n2. There is no experiments that trains any model with FLUX-Reason-6M. There lacks evidence that this dataset is helpful for model training.\n\n3. PRISM-Bench relies on MLLM for evaluation. However, MLLMs can be unreliable, especially with these challenging tasks. There is no human experiments that show how well this benchmark aligns with humans.", "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": "oMborMcLYX", "reviewer": "Reviewer_YCdw", "summary": "This paper presents two contributions to the text-to-image generation field, primarily aimed at improving open-source models by focusing on complex reasoning. The contributions are: 1) FLUX-Reason-6M: A large-scale dataset of 6 million high-quality synthetic images paired with 20 million bilingual captions. The dataset is structured around six key characteristics and notably includes \"Generation Chain-of-Thought\" annotations to provide richer, structured supervision for reasoning. 2) PRISM-Bench: A new, 7-track benchmark designed to evaluate T2I models on these characteristics, including a \"Long Text\" track based on GCoT. A key feature of this benchmark is its use of advanced Vision-Language Models as judges to score both prompt-image alignment and image aesthetics, moving away from simpler, saturating metrics. The authors use this benchmark to evaluate 19 different models, identifying \"Text rendering\" and \"Long text\" comprehension as major, persistent challenges across the board. The authors intend to release both the dataset and the benchmark.", "questions": "1.\tCould the authors elaborate on the quality control process for the GCoT annotations? Specifically, how did the authors ensure that the VLM-generated reasoning steps were logical, accurate, and of high quality?\n\n2.\tThe authors state that the Qwen-VL judge yields “consistent rankings” with the GPT-4.1 judge. Could the authors provide a more quantitative measure of inter-judge agreement between the two VLM judges across the seven tracks to substantiate the claim of benchmark robustness?\n\n3.\tHave the authors conducted any preliminary experiments to examine whether training on FLUX-Reason-6M, particularly with GCoT supervision, improves performance on other existing benchmarks compared with training on other large-scale datasets? If so, what do these results suggest about the practical benefits of the proposed dataset and GCoT supervision?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1.\tThe FLUX-Reason-6M dataset is impressive in its scale (6M images, 20M captions) and its use of a high-quality generator (FLUX.1-dev) ensures a strong baseline of visual quality, avoiding the noise of typical web-crawled data.\n\n2.\tThe Generation Chain-of-Thought is a novel and important contribution. Providing explicit, multi-step reasoning breakdowns as a supervisory signal is a promising direction for teaching models complex compositional logic, which is a key limitation of current T2I systems.\n\n3.\tPRISM-Bench represents a significant improvement over existing T2I evaluation methods. The 7-track design allows for a much more granular analysis of model strengths and weaknesses than a single, aggregate score.\n\n4.\tThe comprehensive evaluation of 19 models yields useful insights, particularly the clear confirmation that even SOTA closed-source models struggle immensely with reliable text rendering and long, complex instruction following. This helps focus the community on the next set of hard problems.", "weaknesses": "1.\tThe dataset is entirely synthetic, generated by a single model (FLUX.1-dev). This creates a significant risk of \"dataset bias\" or \"domain gap.\" Models trained on FLUX-Reason-6M may learn the specific style, quirks, or common failure modes of the FLUX generator, and this improved performance might not generalize to real-world images or other domains.\n\n2.\tThe PRISM-Bench relies entirely on VLM judges. While the use of VLMs is well-motivated, the central claim that these judges are \"human-aligned\" is asserted rather than proven. Without a correlation study comparing the VLM scores to scores from human evaluators, the validity of the benchmark is not fully established. This is a omission for a paper introducing a new evaluation standard.\n\n3.\tThere is a potential self-referential loop in the methodology. The GCoT reasoning annotations are generated by a VLM, and the benchmark is then evaluated by other VLMs. This creates a system where models are trained on VLM-generated text and then evaluated by VLM judges. This VLM-in-a-loop system risks optimizing for what VLMs think is good reasoning or good alignment, which may not perfectly overlap with true human intent or logic.", "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": "jUTJmpQsBF", "reviewer": "Reviewer_oMcM", "summary": "This paper introduces FLUX-Reason-6M and PRISM-Bench. FLUX-Reason-6M is a 6-million-scale synthesized dataset curated by Flux, with the first multi-dimensional, million-scale generation chain-of-thought annotations.  PRISM-Bench serves as a comprehensive and discriminative benchmark with 7 independent tracks that evaluate Imagination, Entity, Text rendering, Style, Affection, and Composition. By evaluating 19 leading models, the work identifies critical performance gaps that require improvement.", "questions": "The paper relies heavily on VLM-based automation: Qwen-VL for dataset filtering and GPT-4.1/Qwen2.5-VL-72B as judges for benchmark evaluation. However, the alignment of these automated judgments with human evaluation is not sufficiently detailed. \n\nIn dataset construction, are the filtering results of Qwen-VL aligned with human checking? \n\nSimilarly, how well do the VLM judges (GPT-4.1, Qwen2.5-VL-72B) align with human preferences and scoring on the PRISM-Bench tasks? While an automatic pipeline is valuable, its quality at each step needs to be validated against human assessment.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "Unlike many datasets that are simple collections of image-caption pairs, FLUX-Reason-6M is specifically designed to teach complex reasoning capabilities to T2I models. It moves beyond merely describing what is in an image to also explaining why it is composed in a particular way.\n\nA major innovation is the introduction of GCoT, which provides detailed breakdowns of image generation steps. These explicit reasoning paths deconstruct the semantic and compositional logic of the final image, offering powerful intermediate supervisory signals for training models to understand underlying structures and artistic choices.\n\nPRISM-Bench provides a multi-dimensional, fine-grained, and human-aligned evaluation, especially a challenging \"Long Text\" track that leverages GCoT captions.", "weaknesses": "1. The paper would be far more convincing if the authors had fine-tuned an existing open-source model on this new dataset and shown corresponding performance improvements on PRISM-Bench or other benchmarks. This is the most direct way to validate the curated dataset's value.\n\n2. The effectiveness of GCot is not evaluated. A comparison between models with and without GCoT on PRISM-Bench is encouraged.\n\n3. An analysis comparing the proposed VLM-based evaluators (GPT-4.1, Qwen2.5-VL-72B) against traditional metrics (e.g., CLIPScore, FID) would be valuable to validate PRISM-Bench as a superior evaluation standard.\n\n4. The abstract claims \"human-aligned assessment,\" but the paper provides insufficient details about the human-in-the-loop refinement process. Specifically, the background, expertise and exact guidelines for the human reviewers are not described. These details are necessary to verify the quality and reliability of the claimed human alignment.\n\n5. The paper would benefit from more qualitative visualizations of model outputs.", "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)