The Intricate Dance of Prompt Complexity, Quality, Diversity and Consistency in T2I Models
Authors: Xiaofeng Zhang
Aaron Courville
Michal Drozdzal
Adriana Romero-Soriano
Venue: ICLR 2026 Poster (2026)
Keywords: text-to-image models
prompt complexity
synthetic data
Primary area: generative models

Abstract: Text-to-image (T2I) models offer great potential for creating virtually limitless synthetic data, a valuable resource compared to fixed and finite real datasets. Previous works evaluate the utility of synthetic data from T2I models on three key desiderata: quality, diversity, and consistency. While prompt engineering is the primary means of interacting with T2I models, the systematic impact of prompt complexity on these critical utility axes remains underexplored. In this paper, we first conduct synthetic experiments to motivate the difficulty of generalization w.r.t. prompt complexity and explain the observed difficulty with theoretical derivations. Then, we introduce a new evaluation framework that can compare the utility of real data and synthetic data, and present a comprehensive analysis of how prompt complexity influences the utility of synthetic data generated by commonly used T2I models. We conduct our study across diverse datasets, including CC12M, ImageNet-1k, and DCI, and evaluate different inference-time intervention methods. Our synthetic experiments show that generalizing to more general conditions is harder than the other way round, since the former needs an estimated likelihood that is not learned by diffusion models. Our large-scale empirical experiments reveal that increasing prompt complexity results in lower conditional diversity and prompt consistency, while reducing the synthetic-to-real distribution shift, which aligns with the synthetic experiments. Moreover, current inference-time interventions can augment the diversity of the generations at the expense of moving outside the support of real data. Among those interventions, prompt expansion, by deliberately using a pre-trained language model as a likelihood estimator, consistently achieves the highest performance in both image diversity and aesthetics, even higher than that of real data. Combining advanced guidance interventions with prompt expansion results in the most appealing utility trade-offs of synthetic data.

Published as a conference paper at ICLR 2026
THE INTRICATE DANCE OF PROMPT COMPLEXITY, QUALITY, DIVERSITY, AND CONSISTENCY IN T2I MODELS
Zhang Xiaofeng1.24 Aaron Courville Michal Drozdzal Adriana Romero-Soriano- 133,44 Mila Québec AI Institute Université de Montréal 3 FAIR Meta Montréal
4 McGill University Canada CIFAR Al chair
x siaofeng. zhang@m! la. quebec
ABSTRACT
Text-to-image (T2I) models offer great potential for creating virtually limitless synthetic data, valuable resource compared to fixed and finite real datasets. Previous works evaluate the utility of synthetic data from T2I models on three key desiderata: quality, diversity, and consistency. While prompt engineering is the primary means of interacting with T2I models, the systematic impact of prompt complexity on these critical utility axes remains underexplored. In this paper, we first conduct synthetic experiments to motivate the difficulty of generalization w.r.f. prompt complexity and explain the observed difficulty with theoretical derivations. Then, we introduce new evaluation framework that can compare the utility of real data and synthetic data, and present a comprehensive analysis of how prompt complexity influences the utility of synthetic data generated by commonly used T2I models. We conduct our study across diverse datasets, including CC12M, ImageNet-Ik, and DCI, and evaluate different inference-ting intervention methods. Our synthetic experiments show that generalizing to more general conditions harder than the other way round, since the former needs an estimated likelihood that is not learned by diffusion models. Our large-scale empirical experiments reveal that increasing prompt complexity results in lower conditional diversity and prompt consistency, while reducing the synthetic-to-re distribution shift, which aligns with the synthetic experiments. Moreover, current inference-time interventions can augment the diversity of the generations at the expense of moving outside the support of real data. Among those interventions, prompt expansion, by deliberately using a pre-trained language model as likelihood estimator, consistently achieves the highest performance both image diversity and aesthetics, even higher than that of real data. Combining advanced guidance interventions with prompt expansion results in the most appealing utility trade-offs of synthetic data.
INTRODUCTION
Text-to-image (T2I) models have made significant progress in recent years, enabling the generation of high-quality images from textual deseriptions (Esser et al., 2024; Labs, 2024). These advance- ments have opened up new opportunities in several application domains, making it possible for the public to create images easily with their own words. For the research community, the success of T2I models has also unlocked the exploration of synthetic data generated by these models for downstream model training (Hemmat et al., 2024; -eemmattemmat et al., 2025; Tian et al., 2023; Fan et al., 2024; Dall' Asen et al., 2025) and model self relf-improvement (Yoon et al., 2024).
Recent studies have yielded in-depth analyses on the utility of synthetic data from offethe-shelf T2I models (Astolfi al., 2024; Hall al., 2024c; Lee et al., 2023). The utility of synthetic data from conditional image generative models, such as T2I, has been defined as a set of desiderata (Astolfi
!Code available utili prmpittyoommt__cmmpppp
httpr//poceeeeeereee/cccccccccic c_data_

---

Published as a conference paper at ICLR 2026
et al., 2024; DeViies et al., 2019), including synthetic image quality -i.e., aesthetics and realism- diversity, and conditional consistency -i.e., alignment with the conditioning prompt. Analyses of these desiderata have revealed that the impressive progress in image quality has come at the expense of generation diversity. As a result, the community has devoted significant work to devise inference- time interventions -e.g., prompt rewriting (Datta al., 2024) and advanced guidance approaches modifying standard classifier-free guidance (CFG) (Ho & Salimans, 2022) - that improve the diver- sity of synthetic data. Although these interventions focus on either changing the prompt conditioning or the flow of signals from the conditioning, the effect of the prompt content on the utility of syntheric data remains an open question. Prompts may be characterized by their complexity, defined as the amount of details contained in the prompt or the specificity of its concepts, and state-of-the-art T21 models have been shown to benefit from synthetic and descriptive captions, generated by vision lan- guage models (VLMs), during training to improve their generation performance ( (Betker et al., 2023). It is now common practice to train high performance T2I models on a combination of real and syn- thetic captions (Esser et al., 2024; Chen et al., 2023; Wu et al., 2025). Yet, these models are known to produce poor results when sampled out of their training distribution (Betker et al., 2023). Consid- ering the very descriptive and synthetic captions used to train T21 models, evaluating these models from the perspective of prompt complexity is crucial to better understand the models' performance.
Therefore, in this paper, we study the effect of prompt complexity the different utility axes of synthetic image data. We start by conducting synthetic experiments over mixtures of Gaussians, conditioning the generation process on prompts of varying complexities. We show that generalization across prompt complexities is already hard in this toy synthetic setting, especially when attempting to generalize more general prompts -i.e., prompts that are shorter or less specific than the ones used for training. The results from the synthetic setting motivate an in-depth evaluation of how prompt complexity influences different utility axes of synthetic data from T2I models. To do so, we propose novel evaluation framework that constructs prompts with different complexities, enabling the evaluation from a p complexity perspecitve over a wide range commonly used vision and dision-language datasets -i.e., CC12M (Changpinyo et al., 2021). ImageNet- Ik (Deng al., 2009), and Densely Captioned Images (DCI) (Urbanek et al., 2024). We use the resulting prompts to con- dition the generation process of state-of-the-art T21 models -i.e. LDMv1.5 (Rombach et al., 2022), LDM-XL (Podell et al., 2024), LDMv3.5M (Esser et al., 2024), LDMv3.5L. (Esser et al., 2024), Flux-schnell (Labs, 2024), and Infinity (Han et al., 2025) and collect synthetic images with several inference-time intervention methods including CFG (Ho & Salimans, 2022), condition-annealingg diffusion sampling (CADS) (Sadat et al., 2024), interval guidance (Kynkäänniemi adapted projected guidance (APG) (Sadat et al., 2025), and prompt expansion (Datta et al., 2024). Our analysis unlocks comparisons between the utility of real and synthetic data, which are beneficial to identify potential gaps between real and synthetic image distributions. To the best of our knowl- edge, we are the first to systematically evaluate the effect of prompt complexity on 721 generations.
al., 2024),
Our synthetic experiments show that generalizing to more general conditions is harder than general- izing to more fine-grained conditionings, since the former needs an estimated likelihood that is not learned by diffusion models. Our large-scale empirical evaluation enabled by the contributed frame- work further reveals interesting findings as follows: 1) The trend of utilities are non-linear, and es- pecially the aesthetic score exhibits a slope steeper towards shorter prompt lengths and more gradual for longer ones (Fig. 3c), showing an asymmetry of prompt length generalization. 2) Diversity does not collapse but plateaus as prompt length increases, as shown in Fig. 2c. This suggests inherent "lower bound of diversity" in T2I models. 3) Optimizing for reference-free metrics harms distribu- tional fidelity. As shown in Figs. and 13, prompt expansion degrades precision and density while newer models (e.g., LDMv3.5LL degrade in frechet distance with real data. This suggests a cautious usage of synthetic data generated from T2I models for downstream applications. 4) By combining advanced guidance methods (in particular APG) with prompt expansion, we can benefit from the ad- vantages of both approaches and achieve the most interesting trade-offs. Overall, our study suggests that prompt complexity is a crucial axis to consider when prompting T21 models, and requires more investigation especially when generating from very general prompts. In addition, diversity a key feature of real-world image distributions, which is still not properly captured by synthetic images from sfate-of-the-an T2I models when no explicit prompt expansion is conducted during inference.
2

---

Published as a conference paper at ICLR 2026
rigetat Wed
nige mhakd
(a) Training data
(b) cat using model trained with fine- grained prompts
(c) black dog using model trained
with general prompts
Figure I: Generalization to prompts of different complexities during inference. Ia shows the training data distribution. 1b presents the generated samples using the general prompt cat with the model trained with fine-grained prompts. Ic shows the generated samples using the fine- ane-grained prompt bl ack dog with the model trained with general prompts. is the classifier-free guidance scale. With w > 1, generalization towards more general prompts is harder this synthetic setting.
GENERALIZING TO GENERAL CONDITIONS IS HARD
In this section, evaluated on prompts of different complexities, and notably when evaluated on prompts that are outside of their training distribution. motivating the necessity of conducting systematic evaluations of T2I models from prompt complexity perspective in the following sections.
synthetic setting build intuition of how T21 models may perform when
Dataset and model. Let our data a mixture of four Gaussians (Figure la), where each Gaussian represents a different category -e.g., white dog. white cat, black dog, and black cat. We train two conditional U-Net models (Ronneherger et al., 2015; Rombach al., 2022) using two sets of conditional prompts: one model is trained with fine-grained prompts (white dog, white cat, black dog, and black cat); the other model is trained with general prompts (dog, cat, white and black). We train both models, including their vocabulary dictionary, from scratch using DDPM schedule (Ho et al., 2020). We perform inference in a generalization setting, -i.e., the model is trained with general prompts, inference is done with fine fine-grained prompts, and vice versa. We also employ classifier- free guidance (CFG) (Ho & Salimans, 2022) with a guidance seale w; = refers the conditional model, -i.e., using classifier-free guidance-, and to higher CFG guidance strengths. More details can be found in the Appendix A.
refers
Conditioning general prompts is analogous to applying OR operator on fine-grained prompts -egg. dog would be white dog OR black dog-, while conditioning on fine-grained prompts may be seen performing an AND operator among general prompts. Following Liu et al. (2022); Du et al. (2023), we have the following derivations. Given a general prompt cg that can be decomposed into various independent fine- -raigga prompts G. 21, ..., K), computing the score function sø (2rlc)) timestep requires both the score functions and the conditional likelihood pa (xx (c) of fine-grained ones. The latter is not available from diffusion model, as shown in Equation 1. However, given a fine-grained prompt & that is composed of several general concepts c t can be approximately estimated by the score functions of general prompts solely (e.g., white and dog), as shown in Equation 2. Derivations are in Appendix A.2.
Mathematical derivations.
M., the score function se se(xglor timestep
po po( (c)) ΣSe(1 p(a)(c))
OR operator: s0(x)((g)=
so so(
(I)
K}
learned by diffusion model
AND operator: s0(x)(e) 80(xx) +
Σ
(8o(x)1c)) so(x.))
(2)
VE (1.........M
3

---

Published as a conference paper at ICLR 2026
Generalizing to general prompts is hard. We use forward KL-divergence (DxL) and Fréchet Distance (FD) to evaluate the generated samples. We also measure reference- sample diversity using Vendi score (Friedman & Dieng, 2023) (VS), following the empirical evaluation in section 4. On the one hand, Figure 1b shows the generated samples using the general prompt cat at inference time with the model trained with fine-grained prompts. When generalizing general prompts (OR operator), the model tends to generate samples towards the mean of the referenced distributions, In particular, when w > (a common practice in T2I models), the generated samples cover region where the training data density is very small, resulting in DKL = 23.78, FD 14.41, and VS 1.03 compared to VS 1.82. Yet, when w this phenomenon is reduced, achieving DRL 1.20, FD = 2.48, and VSgen 1.43 compared to VS 1.82. Since diffusion models only learn the score function (and not the likelihood weighting in Equation 1, they may naively add up the score function values of fine-graine conditions together when generalizing to general condition, hence weighting, and leading to generated samples that correspond to disregarding the likelihood--aase conditional training samples. On the other hand, Figure Ic shows the average of the the generated samples using fine-grained prompts at inference time with the model trained with general prompts only. In this case, observe that the model can leverage both general prompts and generate compositional outcomes in zero-shot manner. The generated samples achieve DKI. 1.51, FD = 6.64, and VSgen 2.04 compared to VS Equation 2 is similar to CFG. sx() + ((sg(zz)e) c)) s (zx)). when w ~ M. Empirically, using a larger w pushes the distributions further towards the fine- -eneggained conditional direction, reaching Daa. = 0.93. FD = 1.32, and VS gen = 1.33 compared to VS,.r 1.10 when w 3. We do not observe severe distributional shift and diversity reduction in this simple synthetic setting. We further discuss the relationship between synthetic settings and large-scale evaluations in Appendix A.3.
1.10, when using w 1. Note that
3 BENCHMARKING FRAMEWORK
We propose a new evaluation framework designed to evaluate the utility (focusing on quality, diver- sity, and consistency) of synthetic data generated by T2I models as a function of prompt complexity, comparing with that of real data. Comparing the diversity of synthetic images conditioned on a prompt to that of real images in a dataset is challenging. as image-caption pairs in existing datasets are fixed, making non-trivial to assemble a set of real images that matches a given prompt.
Given an image dataset X' C I x V, where I is the image set and y is the label set. Each datapoint X; E X, i 21, 2........ | is (I,, ys) where I; represents the image sample, and y; represents the associated label (either class or captions in our experiments). We first synthesize the images using captions with K different levels of complexities. The caption set associated with each complexity is noted as C+ k {1, 2..... K}. We then use T2I models to generate synthetic images I* from the caption sets C* Finally, we employ different evaluation functions f E F (I,I")
} x C* + R to evaluate the utilities of both synthetic and real data, where F is the set of evaluation functions con- sidered in our analysis and f is an evaluation function. In the following, we describe our framework, which consists of captioning, pairing, alignment, sampling, and generation steps, in detail.
This step creates captions of different complexities for each datapoint X,- Given the complexity level K that we want consider in our study, &i E 21,2,.... 12]}, k E 21, K} of increasing complexities from each datapoint (X yi)- Thus, we transform the original dataset X' into K different datasets 2 A E 21, K}. The dataset 8 contains all the images in I and each image I; is paired with a caption of complexity level-k; c.
Captioning.
create captions
Pairing. Given a certain complexity level & E 21, K} and a caption cf, E (1,2,.... (1)). we search for images that are semantically similar the caption of We note these images as a set Iik with elements 75th j {1, .., |2]]. We only keep the image sets I with cardinality 2 20. Thus, across complexity levels, the captions left after filtering are different. We denote the indices of the captions left each complexity as sets NT .?.....N
Alignment. Across complexities, the images left in each complexity USENZ 7 , (1, K} could be very different due to the filtering performed in the pairing step. This step
For the synthetic setting, there is pre-trained models evaluate the reference-free quality and consis- tency. -iveeieeeee and FD cover both the quality and consistency.
4

---

Published as a conference paper at ICLR 2026
MrD Lioks gubsace
LEPAAL
30
16
25
10--
35
10-
10-
20 promot lengths
medien prompr benghte
prompt complesity
(a) CC12M
(b) ImageNet-Ik
(c) DCI
Figure 2: Reference-free diversity metric. Diversity (Vendi) of LDMv1.5 and LDMv3.5L generations with CC12M and ImageNet-Ik 1k prompts when using: 1) vanilla guidance (CFG), 2) prompt expansion, and 3) advanced guidance methods, for which transparent markers correspond to different methods and the solid marker is the average over methods. Both advanced guidance methods and prompt expansion lead to improved diversity over the vanilla guidance. Prompt expansion from shorter captions longer DCI prompts. Diversity of all models first decreases then plateaus which is not observed within shorter prompt length ranges.
surpass the real data diversity. We further extend to much
aligns the image modality across complexities ensure comparability. Specifically, iteratively remove images that are not shared across complexities, iee. not in the set NNE(1......K) UiENY I'k This process is repeated until the following criteria is satisfied, NNE(1......K) USENp I = ULE(1......) Useve Ith We denote the indices of the captions left each complexity as sets NT, Nr.
Sampling. Following the alignment step, the number of remaining captions may still be too large for practical evaluation of T2I models. We randomly sample the same number captions per complexity in order to maximize semantic coverage in a consistent manner. This results in sets of indices of captions .V..N....... captions are then used as prompts to collect synthetic data from T2I models.
where [WEl the same Yk E (1,2,..., K). The sampled
Generation. For each prompt (caption) ct E Ng. k E 21,2.... K). we generate as many images as the cardinality of the smallest similar image set across different caption complexity Ngon = -min (E(...... [I ensuring representativeness. The generated image set for prompt ot is noted as 7is With the prompt conditional image sets for both real images and synthetic images, we comprehensively assess each axis of data utility using a suite of representative reference-fre free metries, which do not constrain the analysis to any predefined target data distribution. Our framework can also be readily extended to incorporate reference- baarence-based evaluation metrics as needed, as shown in section 4.
EVALUATION OF SYNTHETIC DATA GENERATED FROM T21 MODELS
4.1 EXPERIMENTAL SETUP
Datasets. We employ three datasets -CC12M, ImageNet-Ik, and DCI- evaluate the utility of synthetic data generated from T2I models. CC12M (Changpinyo et al., 2021) is a large-scale, enternet-crawles image-text dataset containing 12 million image-caption pairs, where we investigate the effect of increasing prompt detail and length on the utility axes of synthetic images. ImageNet- 1k (Deng et al., 2009) is a widely used, object-centric, curated dataset with 1,000 classes, where examine the effect of concept specificity on the utility axes. DCI (Urbanek et al., 2024) is large-scale, human-curated image-caption dataset featuring detailed, long captions for each image, where we further explore prompt complexity using very long prompts. More details are presented in Appendix I.
We stop at prompt length of 50, given the 77 token limit of text encoders of LDMv1.5. For LDMv33.5 models, we push the prompt length to 100, as shown in Figure 11, Appendix D.3.
5

---

Published as a conference paper at ICLR 2026
5.0
5.0
4.8-
4.8
quains
LUMPULS adlitioal dasce
4.84
4.6
4.2
4.0
4.0
40
modian pro longtts
complccomstiitt
prompt lengths
(a) CC12M
(b) ImageNet-Ik
(c) DCT
Figure 3: Reference-free quality metric. Quality (aesthetic) of LDMv1.5 and LDMv3.51.
generations with CC12M and ImageNet-Ik prompts when using: 1) vanilla guidance (CFG), 2) prompt expansion, and 3) advanced guidance methods, for which transparent markers correspond to different methods and the solid marker is the average over methods. Advanced guidance methods slightly impair the quality while prompt expansion increases it. We further extend to much longer DCI prompts. Quality of all models first increases then decreases which is not observed within shorter prompt ranges.
Models. We select LDMv1.5 (Rombach et al., 2022) and LDMv3.5L. (Esser et al., 2024) models for the presentation of results. We also include the evaluation results from LDMv3.5M (Esser al., 2024) and LDM-XL (Podell et al., 2024) in Appendix D. LDMv1.5 is the early high- perforerforinn T2I model using the latent diffusion model (LDM) architecture (Rombach LDMv3.5L is its latest successor, employing a rectified flow model (Liu et al., 2023). These models representative of current open-source T2I models and illustrate the trend of model improvement over time. We consider the following guidance techniques: CFG (Ho & Salimans, 2022) (referred vanilla guidance), and advanced guidance approaches including CADS (Sadat et al., 2024), interval guidance (Kynkäänniemi et al., 2024), and APG (Sadat et al., 2025). We also explore the effect of explicitly expanding prompts by adding information, using large language models to expand each caption to Ngen different captions with maximum thirty words. Additionally, we report evaluation results of Flux-schnell (Labs, 2024) and Infinity (Han et al., 2025) to cover more model types, to which advanced guidance methods are not applicable. Considering the limited inference-time interventions, we present the results for these two models in Appendix F. More implementation details are presented in Appendix C.
al., 2022), while
Metrics. We use the aesthetic score (discus0434 & Goswami, 2023) to assess image quality, the Vendi score (Friedman & Dieng, 2023) to measure diversity, and the Davidsonian Scene Graph (DSG) score (Cho et al., 2024) to evaluate prompt consistency. Our human evaluations in Ap- pendix E confirm the validity of the automatic metrics. We also report widely used reference-based metrics, including FDD (Fréchet distance using DINOv2 (Oquab et al., 2023)) (Stein et al., 2023), precision (Kynkäänniemi et al., 2019), density and coverage (Nacem et al., 2020).
4.2 UTILITY AXES MEASURED BY REFERENCE- METRICS
Diversity. We present results in Figure 2. Our evaluation findings empirically verify that the diversity of synthetic data decreases as we increase prompt complexity for all models and interventions considered (Figures 3a and 3b). As increasing the complexity (either by adding details in CC12M or increasing the class specificity in ImageNet- -kk constrains the generation process, the degree of freedom of the model is reduced. In terms of inference-time interventions, both advanced guidance methods and prompt expansion lead to higher diversity than vanilla guidance. Note that prompt expansion can be considered as explicit way sample the likelihood of more fine-grained prompts (Equation 1) using pre-trained language model (Betker et al., 2023), which effectively increases the diversity. It is possible obtain higher diversity than the one captured by the real data when the prompt complexity is low, but this comes at the cost of consistency (as indicated in Fig- ure 4). In CC12M, the diversity gap between vanilla guidance and real data slightly decreases with the increase of prompt complexity, indicating that the diversity for more general prompts is harder
Unless otherwise specified, we use the DINOv2 (Oquah et al., 2023) feature space to compute these met- rics, since has been found correlate well with human judgement (Hall et al., 2024a; Stein al., 2023).

---

Published as a conference paper at ICLR 2026
LOP
0.85-
0.65
perco upastor putarcs
0.92
0.90
0.75
0.00
0.90
00.00
0.88
0..00
0.75
0..00
0.26
0.55
0.50
0.82
0.45
modian promot langes
prompt compleatty
prompt lengths
(a) CC12M
(b) ImageNet-Ik
(c) DCI
Reference-free consistency metric. Consistency (DSG) metrics of LDMv1.55 and LDMv3.5L generations with CC12M and ImageNet-IK prompts when using: 1) vanilla guidance (CFG), 2) prompt expansion, and 3) advanced guidance methods, for which transparent markers correspond to different methods and the solid marker is the average over methods. Both advanced guidance methods and prompt expansion lead to lower consistency scores compared to vanilla guidance. We further extend to much longer DCI prompts. Consistency of all models decreases when the prompt lengths increases, which is the same as in the shorter prompt ranges.
Figure 4:
to be captured, aligning with our synthetic experimental results (Figure lb). However, ImageNet- lk exhibits a different pattern. This may be due to the construction of the real data prompt-image pairs, general prompts can cover more subcategories than the ones in ImageNet- 1k. For example, the general prompt (complexity=1) st stringed instrument rument can contain mandolin (covered by T2I models as shown in Figure 20, Appendix H). but this class is not part of the ImageNet- Ik dataset, leading to an underestimation of the real data diversity. We further push this evaluation towards much longer prompts using DCI dataset. We observe that the diversity decreases and then plateaus as we continuously increase the prompt length, indicating that the models may not be able follow all the constraints added in the prompts (also reflected in the consistency measurements in Figure 4c). For most LDM models, the plateau starts at a prompt length of ~ 30 words. However, the plateau region of LDMv1.5 seems to occur earlier than its successor models.
Quality. Results across datasets and inference- -irrenceetimeeeeee intervention methods are shown in Figure 3. In CC12M and ImageNet- 1k datasets, where we use relatively shorter prompts, the aesthetics of the synthetic data remains relatively stable across prompt complexities, especially for the most recent model (LDMv3.5L).. Further, advanced guidance methods lead to slightly lower performance than vanilla guidance. This is by contrast to prompt expansion, which consistently exhibits higher aes- thetics than vanilla guidance. Compared to real data, synthetic data exhibits higher or competitive aesthetic quality. This is perhaps unsurprising given the aesthetics finetuning performed on the most recent models (LDMv3.5L). When we evaluate on the DCI dataset using much longer prompts, image aesthetics first increase and then start to gradually decrease for all models. The sharp slope observed towards more general prompts and the gradual decrease observed towards more fine-grain fine-grained prompts appear aligned with the observation of our synthetic experiments that generalization to longer and fine- fine-grained (higher complexity) prompts is easier than generalization to more general (lower complexity) ones, especially when leveraging CFG.
function of prompt length
Consistency. Figure 4 shows the results across datasets and inference-time intervention methods. We observe that the prompt consistency decreases as we increase prompt complexity for all the cases considered. This suggests that T2I models struggle to incorporate the increasing amount of details (objects, attributes, and relations among objects) required by longer prompts, and to faithfully generate very specific concepts. Real data also shows decrease in consistency across complexities, which is due to the image-text pairing process. The experiments on DCI dataset further consolidate this observation. We present the 95% confidence interval of these metrics in Appendix G.3, confirming the statistical significance of the observed trends. The findings connecting different utility axes and prompt complexity are visually captured in Figures 19 and 20 in Appendix H.
4.3 UTILITY AXES MEASURED BY REFERENCE-BASED METRICS
Reference-free metrics are suitable for evaluating the utility of synthetic data in the wild with prompts only. Yet, in our analysis, the prompts extracted from existing datasets, containing
7

---

Published as a conference paper at ICLR 2026
both images and texts, therefore enabling the use of reference- metries for evaluation too. The CC12M results are presented in Figures 5. We leave the ImageNet-lk results in Figure 13 in Appendix G.I. These figures reveal that prompt complexity increases, there is an overall tendency for synthetic data to improve its precision, density and coverage in both CC12M and ImageNet- 1k datasets, suggesting that more detailed captions, grounded on the real images, help generate data that lies within the support of the reference dataset. This also aligns with our synthetic experimental results showing that generalizing to general (lower complexity) prompts results in mode concentration and larger distributional gap (Figure lb).
Figures 5 and 13 also show that advanced guidance methods and prompt expansion lead overall better FDD than vanilla guidance. When it comes to coverage, prompt expansion also exhibits high performance compared to the vanilla guidance in most cases, although its benefits are less pronounced for higher complexity prompts. The improvements in coverage come, in both cases, at the expense of precision and density. This is perhaps expected as 1) prompt expansion may include details via pre-trained language models that are not present in the real images, therefore deviating the generation process from the reference data"; and 2) advanced guidance approaches may push the generation process outside of the real data manifold, resulting in reduced precision w.E. the vanilla guidance. The drop in precision and density shows the trade-off of creativity and fidelity to the real data distribution, emphasizing cautious usage of T2I models for different downstream applications. We give a discussion in Appendix G.2.
Interestingly, 1DMv1.5 model has better overall performance (lower FDD) compared to LDMv3.5L. model across different guidance methods, prompt expansion, and prompt lengths. However, as shown in section 4.2, LDMv3.5L performs better than LDMv1.5 on reference-free quality and consistency metrics, only falling short in diversity, as shown in Figures 2a, 3a, and 4a. This suggests that the diversity is a key characteristic of real-world image distributions. Thus, synthetic data from models that fail to capture the diverse nature of the real world may lead to lower general performance in real-world applications even though the models advance the state-of-the-art generation quality in reference-free settings. Similar findings may be observed for the ImageNet- Ik dataset, presented in Figures 2b, 3b, 4b, and 13.
4.4 COMBINING PROMPT EXPANSION AND ADVANCED GUIDANCE METHODS
Figure 6 shows the effect of combining prompt expansion and advanced guidance methods on the 3 utility axes of synthetic data, when using prompts from CC12M. We observe that the prompt expansion and advanced guidance methods can be combined to further boost the diversity of synthetic images. We note that in this case, Interval guidance appears to slightly suffer from aesthetic quality and shows considerably lower prompt consistency than both CFG and APG for prompts lengths higher than 1. Finally, when contrasting with real data, we observe that the diversity of the real data may be surpassed by that of synthetic data across prompt complexities, but this again comes at the cost of consistency, A closer look at advanced guidance methods and prompt expansion is provided in Appendix G.4.
RELATED WORK
Improving the synthetic images utility. Recent studies have yielded in-depth analyses on the utility of synthetic data from off-the-shelf T21 models (Astolfi al., 2024; Hall al., 2024c; Lee et al., 2023). and have revealed that the impressive progress in image quality has come at the expense of generation diversity. Inference-time interventions have been introduced to improve over classifier free guidance (CFG) sampling (Ho & Salimans, 2022). These techniques involve either prompt rewriting (Datta et al., 2024), or alternative guidance signals. For example, APG (Sadat et al., 2025) proposes to adaptively adjust the guidance scale across different directions, CADS (Sa- dat et al., 2024) implements an annealing strategy that gradually uses less noisier guidance signal during the sampling process, and Interval-guidance (Kynkäänniemi et al., 2024) introduces simpler approach by disabling the conditional guidance at the beginning of the sampling process. Auto gitto-guidance (Karras et al., 2024) uses bad-version of the model to guide the generations;
"This can be understond as mis-alignment hetween the likelihood estimation from the pre-trained language model and from the real image dataset.
8

---

Published as a conference paper at ICLR 2026
600
0.45
portt eipisdoonn pulcance
0.8
500
0.70
dannlyy dlno02
0.35
RD0
greczoon
0.65
0.6
coverage
0.30
300
0.60
LENGEES
200
0.55
0.4
0.15
5
median prompt lingths
median prompt lengths
median prompt lengths
medaa prompt lengths
(a) FDD
(b) Precision
(c) Density
(d) Coverage
Figure 5: Reference- utility metrics of synthetic data using CC12M prompts. FDD, precision, density and coverage for LDMv1.5 and LDMv3.5L. generations with: (1) vanilla guidance (CFG), (2) prompt expansion, and (3) advanced guidance methods, for which transparent markers correspond to different methods and the solid marker is the average over methods. Both advanced guidance methods and prompt expansion lead to better FDD. Although prompt expansion improves coverage and advanced guidance methods match coverage for LDMv3.5, they both sacrifice precision and density. LDMv11.5 has thus better overall performance (lower FDD) than LDMv3.5L..
4
13-
1775
100
60
3735-
215
64
105
angin
(a) Diversity
(b) Quality
(c) Consistency
(d) FDD
Figure 6: Effeec of combining prompt expansion and guidance methods on the utility of synthetic data from LDMv3.5L. using CC12M prompts. This can further boost the diversity of synthetic images, with comparable quality, consistency, and FDD (expecially with APG).
however, in the context of open-source models, it is not trivial how to obtain ver-iorso of the models that would provide useful guidance signals. Chamfer guidance (Dall' Asen et al., 2025) leverages few examples of real images to guide the sampling process towards high-utility samples.
Evaluation of T2I synthetic data. Numerous evaluation metrics have been proposed to assess the performance of T2I models, covering the quality, diversity, consistency utility axes. Many met- rics need reference set of real data to compare with. For example, Fréchet Inception Distance (FID) (Heusel et al., 2017) has been widely used as an overall metric and measures the distance between the feature distributions of synthetic and real images. Precision and recall (Kynkäänniemi et al., 2019), followed by density and coverage (Naeem et al., 2020) were proposed to evaluate the realism and diversity of synthetic images. To evaluate T2I models in the wild, researchers have introduced several reference-free metrics. When it comes to quality, aesthetics score (Schuhmann, 2022: discus0434 Goswami. 2023) has been built on top of CLIP (Radford et al., 2021) esti- mate how visually appealing synthetic images are. For diversity, Vendi score (Friedman & Dieng, 2023) was designed to evaluate the diversity of a set of images. Moreover, in Tang et al. (2024), human-calibrated perceptual variability measure was introduced and then used to investigate the influence of linguistic features in the T2I generation process. This work focuses on the diversity axes exclusively, does not contrast results with real data, and does not consider any sampling inter- vention beyond vanilla guidance. For prompt-image consistency, CLIPscore (Hessel et al., 2021) has been used to measure the alignment between the generated image and the input text prompt. However, CLIPscore has been shown to be correlated with object numerosity (Ahmadi & Agrawal, 2024) and unable to handle detailed descriptions (Zhang et al., 2024). VQAscore (Lin al., 2024),
9

---

Published as a conference paper at ICLR 2026
TIFA (Hu et al., 2023) and DSG (Cho et al., 2024) further leverage visual question answering (VQA) models for image-promp consistency. Among them, TIFA and DSG decompose the captions into several granular questions and compute accuracy scores based on the answers to each question. Be- yond these single-axis utility metrics, several analyses have been performed on '21 models from a multi-objective perspective (Astolfi et al., 2024; Lee et al., 2023; Hall et al., 2024b). Our study com- plements previous work by assessing the utility of synthetic data as a function on prompt complexity.
6 CONCLUSION
Conclusion. this paper, we proposed a new evaluation framework and presented an in-depth analysis of the utiliiyy of synthetic data as function of prompt complexity, including synthetic and large-scale real data evaluations. Our synthetic experiments show that generalizing to more general conditions is harder than the other way round. Our study suggests that prompt complexity is crucial axis to consider when sampling from T2I models, and requires more investigation when generating from very general prompts. Our findings include 1) the trend of utilities are non-linear, showing an asymmetry of prompt length generalization. 2) diversity does not collapse but plateaus as prompt length increases, suggesting for reference-fres metrics harms distributional fidelity, 4) combining guidance methods with prompt expansion achieve the most interesting trade-offs in utilities. In addition, diversity is key feature of real-world image distributions, which is still not properly captured by synthetic images from the state-of-the-art T21 models when no explicit prompt expansion is conducted during inference.
inherent "lower bound of diversity" in T2I models, 3) optimizing
Limitations. For datasets with long captions, our framework needs to use large and diverse enough image datasets to build paired image sets, which is not easy to get. Since DCI dataset has only 7805 image-caption pairs, we did not compare the utilities of synthetic images with real data.
REPRODUCIBILITY STATEMENT
We detailed our experimental setups, including datasets, hyperparameters, training and inference settings, and computing resources, in both main texts (Section 2 and 4.1) and Appendices (Ap- pendix A. I and C). We provided full derivations of our theoretical results in Appendix A.2. All the datasets used in our paper are open-source and can be downloaded following the instructions given by dataset providers. We detailed the licences of datasets and packages used in Appendix C.5. We provided detailed descriptions of our evaluation framework in Section and Appendix B, and our anonymous codes supplementary materials for reviewing.
ACKNOWLEDGMENT
AC's work was supported by the Institute of Information & Communications Technology Planning & Evaluation (ITP) grant funded by the Korean Government (MSIT) (No. RS-2024-00457882,
National AI Research Lab Project) and his own CIFAR Canadian AI Chair. XZ is supported by the AIM program at Meta and the EDI in Research Scholarship of Mila. The authors thank Oscar Mañas, Andrei Nicolicioiu, and Nicola Dall' Asen for insightful discussions regarding the evaluation pipeline; Avery HW Ryoo for helpful suggestions prompting language models; and Nicolas Beltran- Velez and Felix Friedrich for constructive feedback during the rebuttal.
REFERENCES
Saba Ahmadi and Aishwarya Agrawal. An examination of the robustness of reference-free image captioning evaluation metrics. In Findings of the Association for Computational Linguistics: FACI. 2024, pp. 196-208, 2024.
Reyhane Askari-Hemmal Mohammad Pezeshki, Elvis Dohmatob, Florian Bordes, Pietro Astolfi, Melissa Hall, Jakob Verbeek, Michal Drozdzal, and Adriana Nomero-Soriana Improving the scaling laws of synthetic data with deliberate practice. arXiv preprint arXiv:2502. /5588, 2025.
10

---

Published as a conference paper at ICLR 2026
Pietro Astolfi, Marlene Careil, Melissa Hall, Oscar Mañas, Matthew Muckley, Jakob Verbeek, Adri- ana Romero Soriano, and Michal Drozdzal. Consisency--diverittyyreell realism pareto fronts of condi- tional image generative models. arXiv preprint arXiv:2406. 10429, 2024.
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. hltps://cdn. openai. com/papers/dallle-. pdf. 2(3):8, 2023.
Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12M: Pushing web- scale image-text pre-training to recognize long-tail visual concepts. In CVPR, 2021.
Junsong Chen, Jincheng Yu, Chongjian Ge. Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart-o: Fast training of diffusion transformer for photore- alistic text-to-image synthesis. arXiv preprint arXiv: 20t0:330000022, 2023.
Jaemin Cho, Yushi Hu, Jason Michael Baldridge, Roopal Garg, Peter Anderson, Ranjay Krishna, Mohit Bansal, Jordi cont-,uset, and Su Wang. Davidsonian scene graph: Improving reliability in fine tineggriiedd evaluation for text-to-image generation. In The Twelfth International Conference Learning Representations, 2024.
Nicola Dall' Asen, Xiaofeng Zhang, Reyhane Askari Hemmat, Melissa Hall, Jakob Verbeek, Adriana Comero-Soriang and Michal Drozdzal. Increasing the utility of synthetic images through chamfer guidance. arXiv preprint arXiv:2508. 10631, 2025.
Siddhartha Datta, Alexander Ku, Deepak Ramachandran, and Peter Anderson. Prompt expansion for adaptive text-to-image generation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers), August 2024.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hi- erarchical image database. In 2009 IEEE conference on computer vision and pattern recognition,
pp. 248-255. Icee, 2009.
Terrance Deiries, Adriana Romero, Luis Pineda, Graham W Taylor, and Michal Drozdzal. On the evaluation of conditional gans. arXiv preprint 1rXiv:1907.08/75, 2019..
discus0434 and Sayan Goswami. aesthetic-prediicoo--2555 Siglip-based aesthetic score predictor.
https: http//ggtttttttt http:/acccccooccccso//////////e. /aettt/eeeeeeeeeeteeeeeeee ic-peed ctor-v2-53 2023.
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Sussman Grathwohl. Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and meme. In International con- ference on machine learning, pp. 8489-8510. PMLR, 2023.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference on machine learning, 2024.
Lijie Fan, Kaifeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, and Yonglong Tian. Scaling laws of synthetic images for model training... for now. In Proceedings of the IEEF/CVF Confer- ence on Computer Vision and Pattern Recognition, pp. 7382-7392, 2024.
Christiane Fellbaum. WordNet: An electronic lexical database. MIT press, 1998.
Dan Friedman and Adji Bousso Dieng. The vendi score: A diversity evaluation metric for machine learning. Transactions on Machine Learning Research, 2023. ISSN 2835-8856.
Melissa Hall, Samuel J Bell, Candace Ross, Adina Williams, Michal Drozdzal, and Adriana Romero Soriano. Towards geographic inclusion in the evaluation of text-to-image models. In Proceedings the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 585-601, 2024a.
Melissa Hall, Osear Mañas, Reyhane Askari, Mark Ibrahim, Candace Ross, Pietro Astolfi, Tariq Berrada Ifriqi, Marton Havasi, Yohann Benchetrit, Karen Ulirich, Carolina Braga, Abhishek Chamalia, Maeve Ryan, Mike Rabbat, Michal Drozdzal, Jakob Verbeek, and Adriana Romero So- riano. Evalgim: A library for evaluating generative image models, 2024b.
11

---

Published as a conference paper at ICLR 2026
Melissa Hall, Candace Ross, Adina Williams, Nicolas Carion, Michal Drozdzal, and Adriana Nomero-Sorianee Dig in: Evaluating disparities in image generations with indicators for geo- graphic diversity. Transactions on Machine Learning Research, 2024c.
Jian Han, Jinlai Liu, Yi Jiang. Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, and Xiaob- ing Liu. Infinity: Scaling bitwise autoregressive modeling for high-resolution image synthesis. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 15733-15744, 2025.
Reyhane Askari Hemmat, Mohammad Pezeshki, Florian Bordes, Michal Drozdzal, and Adriana Romero-Soriano. Feedback-guide data synthesis for imbalanced classification. Transactions Machine Learning Research, 2024. ISSN 2835-8856.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. CLIPScore: A reference-free evaluation metric for image captioning. In Proceedings of the 202/ Conference Empirical Methods in Natural Language Processing, pp. 7514-7528, Online and Punta Cana, Dominican Republic, November 2021, Association for Computational Linguistics.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NewrlPS 202/ Workshop on Deep Generative Models and Downstream Applications, 2022.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851 2020.
Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with Ilm for enhanced semantic alignment. arXiv preprint a5Xiv:2403.05135, 2024.
Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith. Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question swering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp- 20406 20417, 2023.
Tero Karras, Miik Aittala, Tuomas Kynkäänniemi, Jaakko Lehtinen, Timo Aila, and Samuli Laine. Guiding diffusion model with bad version of itself. Advances in Neural Information Processing Systems, 37:52996-53021, 2024.
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Improved precision and recall metric for assessing generative models. Advances in neural information processing systems, 32, 2019.
Tuomas Kynkäänniemi,, Miia Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen. Applying guidance in limited interval improves sample and distribution quality in diffusion models. In The eighth Annual Conference on Neural Information Processing Systems, 2024.
Black Forest Labs. Flux.
com/blackccccccc htrp:thhwbcccomollcccooooo-----e forest-labs/fluas 2024.
Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta, Yunzhi Zhang, Deepak Narayanan, Hannah Teufel, Marco Bellagente, et al. Holistic evaluation of text- to-image models. Advances Neural Information Processing Systems,
2023.
Zhiqiu Lin, Deepak Pathak, Baiqi Li, Jiayao Li, Xide Xia, Graham Neubig, Pengchuan Zhang, and Deva Ramanan. Evaluating text-to-visual generation with image-to-text generation. arXiv preprint arXiv:2404.0129/ 2024.
Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum. Compositional visual generation with composable diffusion models. In European conference on computer vision, pp- 423-439. Springer, 2022.
12

---

Published as a conference paper at ICLR 2026
Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning generate and transfer data with rectified flow. In The Eleventh International Conference on Learning Repre- sentations, 2023.
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo. Reliable fidelity and diversity metrics for generative models. In International conference on machine learning. pp. 7176-7185. PMLR, 2020.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07130 2023.
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. In The Twelfth International Conference on Learning Representations, 2024.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In International conference machine learning, pp. 8748-8763. PmLR, 2021.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedi- cal image segmentation. In International Conference on Medical image computing and computer- assisted intervention, pp. 234 241. Springer, 2015.
Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, and Romann M. Weber. CADS: Unleashing the diversity of diffusion models through aadition--anealle sampling. In The Twelfth International Conference Learning Representations, 2024.
Seyedmorteza Sadat, Otmar Hilliges, and Romann M Weber. Eliminating oversaturation and arti- facts of high guidance scales in diffusion models. In The Thirteenth International Conference on Learning Representations, 2025.
Christoph Schuhmann. Imprrveed-essthetcc-prrdiorr Clip+mlp aesthetic score predictor. https: //gi thub. com/ httm/christoppioo www.sissshhhhhhhhssssccoeo iimpr//eddddd/iee -opprooedddteedtttt.... besthetic-prente c-predi ctor, 2022.
George Stein, Jesse Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Ross, Valentin Villecroze, Zhaoyan Liu, Anthony I. Caterini, Eric Taylor, and Gabriel Loaiza-Ganem. Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. Advances in Neural Information Processing Systems, 36:3732-3784 2023.
Raphael Tang, Crystina Zhang, Lixinyu Xu, Yao Lu, Wenyan Li, Pontus Stenetorp, Jimmy Lin, and Ferhan Türe. Words worth thousand pictures: Measuring and understanding perceptual variability in text-to-image generation. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 5441-5454, 2024.
Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, et al. Gemma 3 technical report. arXiv preprint arXiv:2503. 19786, 2025.
Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang. and Dilip Krishnan. Stablerep: Synthetic images from text-to-image models make strong visual representation learners. Advances in Neural Information Processing Systems, 36:48382-48444 48402, 2023.
Jack Urbanek, Florian Bordes, Pietro Astolfi, Mary Williamson, Vasu Sharma, and Adriana Romero- Soriano. A picture is worth more than 77 text tokens: Evaluating elip-style models dense cap- tions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 26700-26709 June 2024.
13

---

Published as a conference paper at ICLR 2026
Chenfei Wu, Jiahao Li, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kun Yan, Sheng- ming Yin, Shuai Bai, Xiao Xu, Yilei Chen, et al. Qwen-image technical report. arXiv preprint arXiv:2508.02324, 2025.
Youngseok Yoon, Dainong Hu, lain Weissburg, Yao Qin, and Haewon Jeong. Model collapse in the self-consuming chain of diffusion finetuning: A novel perspective from quantitative trait model- ing. arXiv preprint arXiv:240..17993, 2024.
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF international conference computer vision, pp. 11975-11986, 2023.
Beichen Zhang, Pan Zhang, Xiaoyi Dong, Yuhang Zang, and Jiaqi Wang. Long-clip: Unlocking the long-text capability of clip. In European Conference Computer Vision, pp. 310-325. Springer, 2024.
14

---

Published as a conference paper at ICLR 2026
Appendix
Details on the synthetic experiments
17
A.1
Experimental settings
17
A.2
Derivations
17
A.3 Relationship with the real T2I settings
18
A.3.1 On how this affects the "OR harder than AND" statement
18
A.3.2 On explaining CC12M vs. ImageNet behaviors
19
Details on the benchmarking framework
19
B.1
Captioning step
19
B.2
Pairing step
19
B.3 Alignment step
19
B.4 Evaluation metries
19
Experimental details
20
C.I Dataset details
20
C.2 Guidance methods' hyperparameters
21
C,3 Prompt expansion
C,4 Compute resources
22
C.5 Licenses for existing assets
22
C.6 New assets
22
Results for LDM-XI and L.DMv3.5M
22
D.I Utility axes measured by reference-frve metrics
23
D.2 Utility axes measured by iiterence-based metrics
................................
23
D.3 DCI results
25
E Human evaluation
25
E.1 Vendi score
25
E.2 DSG score
26
Additional models
27
Additional results
27
G.I Reference-based metrics using ImageNet-Ik 1k class labels
27
G.2 Trade-off of creativity and fidelity when using inference time interventions
28
G.3 Statistical significance of the trends
29
15

---

Published as a conference paper at ICLR 2026
G.4 Closer look models and guidances
29
Additional qualitative samples
32
16

---

Published as a conference paper at ICLR 2026
A DETAILS ON THE SYNTHETIC EXPERIMENTS
A.1 EXPERIMENTAL SETTINGS
We use mixture of four Gaussians as training distribution. Each Gaussian is in each quadrant respectively. The means for each Gaussian is [-3, 3], [-3, -31, [3, 3]. and [3, -3]. The covariance for each Gaussian is [[0.7,0].(0.007]]. We hypothetically link each Gaussian to a conditional prompt, Le, white cat for the first quadrant, white dog for the second quadrant, bl black dog for the third quadrant and bl ack cat for the fourth quadrant.
We train conditional U-Net-based diffusion model with DDPM schedule using two types of condi- tional prompts: one is fine- tnee-graieed with white cat, white dog, black cat, black dog, each corresponds to a Gaussian in one quadrant; the other is general with cat, dog, white, and black, each corresponds to two Gaussians.
Following Rombach et al. (2022): Esser et al. (2024), we add pooled conditional information together with the timestep embedding, and also incorporate the token-wise conditional information into the U-Net with an Attention mechanism. To get the pooled conditional information, we use a linear layer, while to get the token-wise embedding, we initialize a vocabulary dictionary from scratch and train it together with the diffusion model. We train the model with batch size 512, Adam optimizer, learning rate le-4 with linear warmup and exponential decay (7 = 0.99), training epochs 250 in total. All training and inference are done using single NVIDIA V100 32GB GPU.
During training, we adopt a 50% probability of dropping the conditional information, enabling the classifier-fns free guidance (CFG) (Ho & Salimans, 2022) during inference. We use the ancestral sam- pler in DDPM (Ho et al., 2020) with 1000 steps for inference. We conduct the inference in a generalization setting, i.e., if the model is trained with general prompts, the inference is done with fine-grained prompts, and vice versa. We use forward Kullback- -ulbbeck-Leibler divergence and Fréchet Distance to evaluate the generated samples. We sample 10,000 data points for evaluation.
A.2 DERIVATIONS
From general conditions to fine-grained conditions. Given conditional diffusion model with
parameters trained on general conditions, timestep E (1, 2, T), independent general condi- tions c E N, we have access to their score functions sp( v., log pe(xzlcg). Supposing
that a fine-grained condition er comprises of several general conditions cg. (1.2.... K}, we have
(o(x) (o( (KE(1,2.....) %))
(3)
po (z og. Eg...n Kk)
(4)
polch. cg. ok
po (x() II K) Po (o(cla
(5)
I1 K3 po po(ch)
(x()x) [1EE(1......)) po(oole))po(e))
po(a.)
(6)
IIIE(L.....K) K} po pe(c))
po(sroc())
= (p((1)
II
(7)
po(
(E(1,2.....K)
se(2)(e)) Va, log po (x)lc))
(8)
Va, log (2(e) +V
Σ (E(1,2...,k))
(logre(zalc))- (o)))
(9)
= +
Σ
(so(2) (so(2rlc)-sole so())))
(10)
(E(1.2....K)
17

---

Published as a conference paper at ICLR 2026
From fine-grained conditions to general conditions. Given conditional diffusion model with parameters 9 trained on fine-grained conditions, timestep t E {1.2,... ..., T). independent fine- grained conditions G. E N, we have access to their score functions so (z) V.. log (x(x)(c) Supposing that a general condition Cg can be decomposed into different fine-grained conditions E (1,2, M) we have
po(x)log)=
=
E
Po Po(2,1a)
(11)
(0(1)((g)= Va, log
Σ
(x(
(12)
(E........M
Vn,
Σ
(o(x)lcf)
(13)
EE po(ssl())
(E11,2.....Mf
Σ
Va.Do(Eelc))
(14)
E (n))o()))))) 1E(1.2.....))
E
(po(valc)) logro
(15)
EjE(L.....M) Po(L.IG)
(E(..2....M)
po (c)
Σ
se (xelc))
(16)
ESEE ()(elc()))))))
(E...2....MM)
A.3 RELATIONSHIP WITH THE REAL T2I SETTINGS
We use the synthetic experiment as a motivation to emphasize the importance of studying the axis of prompt complexity the diffusion flow model setting. The synthetic experiments clearly show an asymmetry of difficulty w.r.t. conditioning complexity when the probabilistic assumptions hold.
The conditional independence assumption synthetic setting derivation is a simplification. Real- world text encoders (e.g., CLIP/TS) produce entangled representations where concepts (e.g., "yel- low" and "banana") can be highly correlated and content dependent. However, we believe this distinction does not invalidate our conclusion regarding the asymmetry of difficulty between gener- alizing to general ("OR") vs. specific ("AND") prompts.
A.3.1 ON HOW THIS AFFECTS THE "OR IS HARDER THAN AND" STATEMENT
AND Operator (Generalizing longer prompts):
In our toy example (Ideal Case): The "black" and "dog" concepts are independent. Their guidance vectors are orthogonal. Our Equation (2) is a probabilistically sound formula. and the naive sum works perfectly, as shown by the KL/FD scores (KL divergence of 0.93 and Frechet distance of 1.32).
The violation of the assumption doesn't t make this operator "harder" in the sense of being impracti- cal, but degrades heuristic, probably leading some failure modes. For example, the guidance vectors for "white" and "dog" are non-orthogonal if they are correlated as captured by the CLIP/ / 'S model. Our Equa- tion (2), by naively adding these vectors, leads to an over mageniiiiiiittoon of this shared, correlated signal, potentially leading to some artifacts (low diversity, ovtrrsaturation, etc.) in the generation.
changes Equation (2) from probabilistic law into practical but imperfect
OR Operator (Generalizing shorter prompts):
This is already hard in theory as we explain in Eq. in our manuscript, where the score function for "OR" operator is intractable in diffusion models. The fact that tokens in real-world prompts not perfectly "mutually exclusive" adds another layer of intractability, making this "OR" generalization more difficult.
Given the theoretical analysis and not over-interpret our empirical results, some our empirical results also verifies this asymmetry. For aesthetic quality with CC12M and DCI settings (Fig. 3, (a)
18

---

Published as a conference paper at ICLR 2026
and (c)), observe sharper decrease towards shorter prompts compared longer prompts, which empirically supports our statement.
A.3.2 ON EXPLAINING CC12M VS. IMAGENET BEHAVIORS
Our theoretical analysis also helps explain some empirical divergences in results of CC12M and Im- ageNet. CC12M relies composition of tokens (similar to the "AND" and "OR" operator logic), where we see similar trends as the synthetic experiments. ImageNet experiments rely on speci- ficity (using semantically richer tokens rather than more tokens) and the ImageNet hierarchy does not strictly follow the combinatorial logic of these mathematical derivations.
B DETAILS ON THE BENCHMARKING FRAMEWORK
B. CAPTIONING STEP
We Gemma3 (Team et al., 2025) model (gemma-33 1appmma----1biii version) conduct the captioning of images from datasets with general captions (e.g., CC12M (Changpinyo et al., 2021)). The sys- tem prompt You strictly follow the user's s instructions. The user prompts used for captioning to different complexities are follows:
1. Mention the main subjects in the image with only word. Do not use specific identifiers, such as names. Format the caption such: <<CAPTION>>>
2. Write short caption with or less words. Use only one adjective. Do not use specific identifiers, such as names. Format the caption as such: <<CAPTION>>.
3. Write caption with 6 or less words, describing at most two main foreground subjects. Do not use specific identifiers, such as names. Format the caption as such: <<CAPTION>>>
4. Write caption with main foreground subjects, as well as the background. Do not use specific identifiers, such as names. Format the caption as such: <<CAPTION>>,
less words, describing most two
B.2 PAIRING STEP
For datasets with general captions, we use the SigLip model (Zhai et al., 2023) to embed both images and texts. Our implementation uses the OpenClip library and the SigLip version ViT-S0400M 14 111---iiiIP--888
B.3 ALIGNMENT STEP
We present the alignment step in Algorithm 1.
B.4 EVALUATION METRICS
To compute evaluation metries, we use the following libraries:
1. Reference-bacee metrics: dgm-eval (Stein et al., 2023)
2. Vendi score: Londi-Scores (Friedman & Dieng, 2023)
3. Aesthetic score: asthhetii--predcccoo-vv... (discus0434 Goswami, 2023)
4. DSG score: Eval-GIM (Hall et 2024b)
'nttps://gi whubb.ccom. foundat ens/ caans//ppncccll
19

---

Published as a conference paper at ICLR 2026
Algorithm 1 Alignment step
Input: 7* {1, 2,,,,.., Kj.i EP: Ngen.
2: breaksignal +- False
3: while not breaksignal do
4: CommonlmagessBefore + Kef1....... UsENT I'k
(K) do 5: for jE E
6: for / E N77 do
7:
7 <- 7 Commonnmmageeeelore
8:
if |ZV| < gee then
9:
del from A?
end if
11: end for
12: end for
13: CommonlmagesAfter + Aket12...... Usevp I'
breaksignal < LettCConmoolllagsBBBrrr == (e((CommoolmagesAftee)
15:
end while
return 7 E (1,
C EXPERIMENTAL DETAILS
C.I DATASET DETAILS
CC12M. We investigate the effect of increasing prompt detail and length on the utility axes of syn- thetic data. Following the procedure described in Section 3, we generate captions for randomly sampled one million images at four different complexity levels, each corresponding to distinct complexity level. Lower complexity prompts are shorter and contain fewer details about the image. We follow the procedure in Section 3 and sample 5,000 captions per complexity for generation. As reference, many widely used evaluation prompt sets contain approximately 1,000 prompts (Cho al.. 2024; Hu al., 2024). We produce 20 images per prompt, resulting in 100,000 generated images for each prompt complexity. The statistics for different caption complexities are presented in Table 1. We use c# to represent complexity level of the prompts. We report the caption lengths statistics across complexities, and the number of real images retrieved using the 5,000 selected cap- tions per complexity.
Table 1: Statistics on word lengths different prompt complexities
c3
c4
Avg # word Std # word Median # word # real images covered
1.0007
2.1539
4.9094
7.5410
0.0501
0.3621
0.7032
0.8849
1.0000
2,0000
5.0000
8.0000
61,334
57,925
55,075
46,066
ImageNet-lk. We examine the effect of concept specificity on the utility axes of synthetic data. We follow the procedure in Section 3 and extract hyponym relations to vary the specificity of ImageNet- 1K class labels. For example, the class s berian husky (highest specificity) has the parent class sled dog and grandparent class working dog (lowest specificity). Since these class la- bels possess intrinsic semantic complexity, To generate images, we use the prompt "Image of <CLASS>", We utilize all 1,000 ImageNet- 1k classes and generate 50 images per class, resulting in 50,000 images, matching the size of the ImageNet- evaluation set.
class-label specificity as proxy for complexity.
DCL. We further explore prompt complexity using very long prompts. DCI's detailed captions sist of multiple sentences, densely describing various aspects each image. We construct prompts described in follow the procedure in Section 3, i.e., progressively concatenating sentences each
20

---

Published as a conference paper at ICLR 2026
caption. We cap the maximum prompt length at 100 words. We sample 1,000 detailed captions and generate 5,398 prompts of varying complexity. For each prompt, we generate 50 images.
C.2 GUIDANCE METHODS' HYPERPARAMETERS
Euler discrete sampling for all guidance methods and sample 28 steps. The timestep scale for all models is from O to I. To find hyperparamers for our setting, we start from the original papers' setting (when the information is available for the models used in our paper) and then empirically test the generation based on randomly selected set of 10 prompts. Since many methods are tested on flass-condiiioall models, the hyperparameters are not always directly applicable to our setting. Empirically, we find that we should keep more conditional information to achieve enough image-prompt consistency compared to class-conditional models.
For inference,
APG (Sadat et al., 2025). This guidance method has three hyperparameters: effect of the parallel component 7), effect of the rescaling threshold r, and effect of the momentum strength B. We follow the original paper and set 0 for all settings. Other hyperparameters are presented in Table 2.
Table 2: Hyperparameters for APG guidance method.
params
LDMv1.5
LDM-XL
LDMv3.5M
LDMv3.5L
7.5
15
10
10
-0.75
-0.5
-0.5
-0.5
Interval (Kynkäänniemi et al., 2024). This guidance method has two hyperparameters: Th and Thi- The conditional information is only applied between No and Thi. The hyperparameters used in our analyses are presented in Table
Table 3: Hyperparameters for interval guidance method.
params
LDMv1.5
LDM-XL
LDMv3.5M
LDMv3.5L
Tio
0.08
0.08
0.3
0.3
0.81
0.81
0.95
0.95
CADS (Sadat et al., 2024). This guidance method has two main hyperparameters to control how the conditional information is annealed: тi and T2, where the guidance scale is set 0 for time steps between and 1.0, then linearly increases between time steps and from 0.0 to 1.0, and is kept 1.0 between time steps 0.0 and 71. In addition, CADS also has hyperparameters for noise scale and for mixing factor d. We keep & 1 for all settings following the original paper. We present the hyperparameters' choice of CADS for different models Table 4. Some the 72 values are larger than 1.0, which means that we always keep some conditional information during sampling. This can help achieve better image-prone prompt consistency.
Table 4: Hyperparameters for CADS guidance method.
params
LDMv1.5
LDM-XI
LDMv3.5M
LDMv3.51
T1
0.8
0.6
0.85
0.85
1.3
1.0
1.25
1.25
0.1
0.3
0.3
0.3
C.3 PROMPT EXPANSION
We use the Gemma3 model (Team et al., 2025) to expand the prompts. - version. The prompt used for expansion follows:
We use the
21

---

Published as a conference paper at ICLR 2026
Table 5: Licences for all the models, datasets and libraries used in our paper.
Asset
Type
Licence
CC12M (Changpinyo al., 2021)
Dataset
Freely for any purpose
Imagenet-Ik (Deng et al., 2009)
Dataset
Custom non-commercial license
DCI (Urbanek et al., 2024)
Dataset
CC BY-NC 4.0 and SA- IB dataset Licnese 8
LDMv3.5L (Esser al., 2024)
Model
Stability Community License
LDMv3.5M (Esser et al., 2024)
Model
Stability Community License
LDM-XL (Podell et al., 2024)
Model
CreativeML Open RAIL++-M License
L (Rombach et al., 2022)
Model
CrcativeML Open RAIL-M License
Gemma3 (Team et al., 2025)
Model
Gemma License
SigLip (Zhai al., 2023)
Model
Apache-2.0
Openclip" 9
Library
Openclip License
Vendi-Score (Friedman & Dieng, 2023)
Library
MIT License
DGM-eval (Stein et al., 2023)
Library
MIT License
Eval-GIM (Hall et al., 2024b)
Library
CC BY-NC AGPL-3.0
Aesthetic-prediicto--..55 (discus0434 & Goswami, 2023)
Library
system prompt: The user will give sentence that is caption for an image. Based on the sentence, you will generate 20 different sentences with around 30 words, that don't violate the original meaning, but with more possible ible details in that image. You will return the sentences in list format.
user prompt: The sentence given is: "CAPTION'
C.4 COMPUTE RESOURCES
We use our internal cluster to conduct all the experiments. The captioning, pairing and alignment steps are conducted on NVIDIA V100 GPUs with 32GB memory. The image generation is con- ducted on NVIDIA H100 GPUs with 80GB memory. All the models are run 16-bit precision and can fit into one GPU. The generation time for 100,000 images is around 175 GPU hours for LDMv3.5L (Esser et al., 2024), 80 GPU hours for LDMv3.5M. (Esser et al., 2024), 40 GPU hours for LDM-XL (Podell al., 2024) and 4 GPU hours for LDMv1.5.
C.5 LICENSES FOR EXISTING ASSETS
We list licenses for all the models, datasets and libraries used in paper in Table 5.
C.6 NEW ASSETS
Our code is under License CC BY-NC
D RESULTS FOR LDM-XL AND LDMv3.5M
In this section, we present results for LDM-XI. (Podell al., 2024) and LDMv3.5M (Esser al., 2024) models on CC12M dataset (Changpinyo et al., 2021) and ImageNet-Ik dataset (Deng et al., 2009), complementing the results the main paper.
License-1-ov-e
Phttps://github com/mi http://mmnaannaaaccca//pepeoonlo
/hhtpps://g thub. com/m i ott 1e
foundat
readme
22

---

Published as a conference paper at ICLR 2026
DATA LONAL
0.90
pustince
0.85
consistency
diverstty
10
quallty
4.0
0.80
4.0
0.75
4.4
0.70
2
median promot lengths
median prompt engths
median prompt lengths
(a) Diversitiy
(b) Quality
(c) Consistency
Figure 7: Reference-free utility metries of synthetic data using CC12M prompts. Diversity (Vendi), quality (aesthetics) and consistency (DSG) metrics LDM-XL and LDMv3.5M gener- ations when using: 1) vanilla guidance (CFG). 2) prompt expansion, and 3) advanced guidance methods, for which transparent markers correspond to different methods and the solid marker the average methods. Both advanced guidance methods and prompt expansion lead to improved diversity over the vanilla guidance. Prompt expansion from shorter captions can surpass the real data diversity. Prompt expansion leads quality improvements, whereas advanced guidance meth- ods slightly reduces quality w.r.f. vanilla guidance.
D.1 UTILITY AXES MEASURED BY REFERENCE-FREE METRICS
We measure the utility of synthetic data terms of quality (aesthetics), diversity (Vendi) and prompt consistency (DSG) with reference-free metrics. We present results using the prompts from CC12M in Figure and ImageNet-Ik Figure The results show the same trend as presented in Sec- tion 4.2.
25.0
22.5
LOBBUS SM
0.92
20.0
whith cuidince
4.8
0.90
17.5
consteiency
0.88
15.0
4.4
0.86
12.5
0.84
10.0
0.82
3.8
2
prompt complexity
grompt complexity
prompt complexity
(a) Diversity
(b) Quality
(c) Consistency
Figure 8: Reference-free utility metrics of synthetic data using ImageNet-Il 1k class labels. Di- versity (Vendi), quality (aesthetics) and consistency (DSG) metrics for LDM-XL and LDMv3.5M generations with: 1) vanilla guidance (CFG), 2) prompt expansion, and advanced guidance meth- ods, for which transparent markers correspond to different methods and the solid marker is the average over methods. Prompt complexity shows specificity of the class label used for genera- tion. Both advanced guidance methods and prompt expansion lead to higher diversity, but advanced guidance methods lead to slightly lower quality. Prompt expansion and advanced guidances with shorter captions yield higher diversity compared to the real data. Both prompt expansion and advanced guidances have negative impact on consistency.
D.2 UTILITY AXES MEASURED BY REFERENCE-BASED METRICS
Reference-free metrics are suitable for evaluating the utility of synthetic data the wild. our analysis, the prompts extracted from existing dataset, containing both images and texts, and
23

---

Published as a conference paper at ICLR 2026
Lowe RL
SL
proved oquasinn pusdance
0.75
STOTES COUAD
0.45
600
preccssion dinov2
donsity airov2
0.8
coverage dinww
0.40
500
0.35
0.7
0.65
0.30
0.6
0.60
300
LOHALL
LOWAL
200
NOAANONC gROSTOM
guibazz
0.4
acbreced puitnncss
2
1 2
median prompt lengths
median prompt lengths
median pongttengths
median prompt lengths
(a) FDD
(b) Precision
(c) Density
(d) Coverage
Figure 9: Reference-based utility metries of synthetic data using CC12M prompts. Fréchet distance (FDD), precision, density and coverage in the DINOv2 feature space for LDM-XL and LDMv3.5M generations with: (1) vanilla guidance (CFG), (2) prompt expansion, and (3) advanced guidance methods, for which transparent markers correspond to different methods and the solid marker is the average over methods. Prompt expansion lead to better FDD for both models. Both prompt expansion and advanced guidances sacrifice precision and density.
700
0.35
0.650
LOWNS.5M
2M
LOMOO
prompt equraion
statot opuns
promst scsamion adhenced gundances
promot ecaumsion edhentod paldanoo
600
accenced za dariow
precison dinov2
0.600
denaty dinev2
coverage airov2
64 L.inoo
500
0.575
400
300
0.525
0.15
0.500
0.10
200
2 prompt camplexity
2
2
prompt complexity
prompt comp exity
prompt complexity
(a) FDD
(b) Precision
(c) Density
(d) Coverage
Figure 10: Reference-based utility metries of synthetic data using ImageNet-1k class labels. FDD, precision, density, and coverage metrics in the DINOv2 feature space for LDM-XLL and LDMv3.5M with: 1) vanilla guidance (CFG), 2) prompt expansion, and 3) advanced guidance methods, for which transparent markers correspond to different methods and the solid marker is the average over methods. Both advanced guidance methods and prompt expansion lead to better FDD, and improve or match the coverage of vanilla guidance, while sacrificing precision and den- sity in most cases. For LDMv3.5M, we see a more prominent effect of advanced guidance methods and prompt expansion than for LDM-XL. Advanced guidance methods do not change the trend of the metrics w.r.f prompt complexity, while the prompt expansion shapes the trend of the metrics differently.
so enabling the use of reference-based metrics for evaluation. Unless otherwise specified, we use the DINOv2 (Oquab al., 2023) feature space to compute these metrics, since it has been found to correlate well with human judgement (Hall et al., 2024a; Stein et al., 2023). The CC12M and ImageNet- 1k results presented in Figures 9 and 10, respectively.
The results are similar to those for LDMv1.5 ..5 and LDMv3.5L L. presented in Section 4.3. It is worth noting that employed more aggressive hyperparameters of advanced guidance methods for LDM- XL model as shown in Tables 2, 3 and 4. This leads to larger differences for reference- based metrics since the generated images can be out side of the support of the reference dataset.
24

---

Published as a conference paper at ICLR 2026
LDW51..
0.9
vend score
30-
LDM XL
L.
wectieii soore
4.8
0..
LOVAAL
LOWNO SL
LDWNS 3M
4.6
0.7
444-
LDW
0.6
0.5
0
20
40
60
80
100
20
30
50
prompg enghh
promot ength
(a) Diversity
(h) Quality
(c) Consistency
Figure 11: Reference-free utility metrics of synthetic data using DCI prompts of increasing complexity. For models, diversity decreases at first and then plateaus, while consistency score continuously decreases with the increase of the prompt length. The quality of LDMv3.5 decreases as prompt complexity increases.
D.3
DCI RESULTS
DCI dataset opens the possibility to evaluate the utilityy of synthetic data for notably longer prompts. We perform reference-free evaluations on synthetic data generated from DCI prompts by binning the prompts into different length chunks and computing the average diversity, aesthetic and consistency scores for each bin. We present the results Figure 11 "I We observe that the diversity decreases and then plateaus a prompt length of 30 words. However, the plateau region LDMv1.5 1.5 seems to occur earlier than for its successor models. However, the consistency tends decrease length. As the prompt becomes increasing detailed, the consistency metric keeps assessing whether all the information is captured, emphasizing the limitations of current T2I models to follow very long and detailed prompts. Interestingly, for the most recent LDMv3.5 models, image aesthetics also seems to slightly decrease as a function of prompt length.
we increase the prompt length. For most LDM models, the plateau starts at
increase the prompt
E HUMAN EVALUATION
Grounding our automatic metries with human evaluation is crucial for validating our findings. We conducted human evaluations, specifically designed to assess whether our automatic diversity metric (Vendi score) and consistency metric (DSG score) aligns with human perception.
E.
VENDI SCORE
We employed two-alternative forced-choice (2AFC) task. In each trial, annotators were shown two image mosaics (16 images each), generated from prompts two different complexity levels. They asked: "Which set of images is more diverse?" Images were generated from CC12M grounded prompts of different complexities using the LDMv3.51. model. For each trial, we randomly sampled two prompts, each from a distinct complexity level to generate the mosaies for comparison. The study involved 9 annotators, who collectively provided 900 pairwise comparison annotations, ensuring comprehensive coverage of all complexity level pairs. We calculated the win rate for lower- complexity prompts against higher-complexity prompts. A win rate greater than 0,50 indicates that, on average, humans perceive images from lower-complexity prompts as more diverse. The results are summarized in Table 6.
Our analysis yields two key findings that validate our approach:
1. Human perception aligns with our main findings. In all pairwise comparisons, the win rate for the lowpl-compyyxity prompts was all above 0.50 (c.g., 0.620 for complexity I vs. 3; 0.794 for 0 vs. 3). This proves that humans consistently perceive the sets of images generated from simpler prompts as more diverse, mirroring the trend identified by our automatic metrics.
2. Vendi score strongly correlates with human preference. There is a clear positive corre- lation between the magnitude of the Vendi score difference (values in parentheses) and the human-annotated win rate. For instance, the largest Vendi score difference (3.00, between
"I DMV1.5 and LDM-XT. models stop prompt length of 50, given the 77 token limit of their text encoder.
25

---

Published as a conference paper at ICLR 2026
levels 0 and 3) corresponds to the most decisive human judgment (0.794 win rate), while the smallest difference (0.605, between levels and 3) corresponds to judgment barely above chance (0.531 win rate). This provides strong evidence that the Vendi score used in our work a meaningful and valid proxy for the degree of human-perceived diversity.
Table 6: Human evaluation results for diversity. Values represent the win rate of images generated from prompts of the row complexity level being judged more diverse than those from the column level. Values in parentheses show the corresponding Vendi score difference between the two levels.
Complexity level 2
3
0.589 (1.63)
0.645 (2.40)
0.794 (3.00)
2
0.563 (0.77)
0.620 (1.37)
3
0.531 (0.61)
E.2 DSG SCORE
the largest model the series released in 2025) decomposes the single complex prompt (e.g.. "A white dog playing the grassy field.") into set of simple, atomic questions (e.g.. "Is there dog?", "Is the dog white?", "Is the field grassy?"....) (2) VQA: The VQA model (default as DSG score paper) then only required to answer these simple, unit-level yes/no questions, task it can perform more reliably compared to answering a single long complex question.
DSG uses a two-stage process: (1) Decomposition: An LLM Biiimma---2B--mman
To further validate automatic evaluated results with DSG score, conduct a human evaluation for both stages.
We give the guideline how the prompt is decomposed into atomic questions along with ample of decomposition presented to human annotators. Given a prompt, we present the prompt along with the decomposed questions generated by LLM to human evaluators, and ask them to check whether each question is a valid question according to decomposition rules. We ask 4 human annotators, and each of them annotates 25 prompts, yielding 100 sets of answers in total. Among all the decomposed questions, the percentage of correctly decomposed questions is 95.54%, which effectively demonstrates the validity of the decomposed questions using LLM automatic generation.
Given the generated images from the original long prompt, we ask the human annotators to answer each decomposed question generated by LLM from the original long prompt. We ask 10 annotators and each evaluates 50 generated images using DCI prompts and gather in total 500 evaluation results. The human evaluation small compared the large-scale automatic evaluation, but shows the same decreasing trend and strong correlation between human evaluation results and automatic VLM results. Table shows the human evaluation results for Stage 2.
Table 7: DSG metrics computed using human evaluation and automatic VLM
# words
10
13
18
23
28
35
41
50
# samples
58
54
66
61
65
66
65
75
Human DSG
0.92
0.90
0.85
0.87
0.83
0.82
0.81
0.81
VQA DSG
0.95
0.89
0.81
0.86
0.85
0.81
0.79
0.82
Pearson corr 0.89; R2 0.68
26

---

Published as a conference paper at ICLR 2026
OAR wieel spaniies gaber
CAD
Hhus srord
12
30
consistancy
0.85
que
0.90
4.4
Racoche
0.70
median prompt
medien prompt lengths
median
lenghs
(a) Diversitiy
(h) Quality
(c) Consistency
Figure 12: Reference-free utility metrics of synthetic data using CC12M prompts. Diversity (Vendi), quality (aesthetics) and consistency (DSG) metrics of Infinity and Flux-schnell generations when using: 1) vanilla guidance (CFG) and 2) prompt expansion. Prompt expansion leads to im- proved diversity over the vanilla guidance. Prompt expansion from shorter captions can surpass the real data diversity. Prompt expansion leads to quality improvements. Infinity model with vanilla guidance has very low diversity in generations, while prompt expansion effectively recover the diversity.
F ADDITIONAL MODELS
Our selection of models cover latent diffusion models and flow models with chronological perspec- tive. We can compare different guidance methods over all these models selected. However, there are some other models that may also raise interests for the research community, e-g., guidance distilled models that uses no classifier-free guidance, and visual autoregreessive models. We present the util- ity of the synthetic data generated from FLUX-schnell (Labs, 2024) distilled diffusion model) and Infinity (Han et al., 2025) (a visual autoregressive model) using CC12M prompts in Figure 12. We include vanilla guidance and prompt expansion for both models.
The Infinity model has the lowest diversity compared to the LDM model series. The LDMv3.5L model has a diversity metric of 4.4 as the lowest, while Infinity can only effectively generate different images on average across prompt lengths. Prompt expansion boosts the diversity metric for all the complexities. Similar to the LDM model series, the diversity of the synthetic images generated using level-1 prompts surpass that of the real data. Interestingly. the decrease of the diversity along the prompt complexity axis is more pronounced than the LDMv1.5 and LDMv3.5L models as shown in Figure 2a. This indicates that the prompt may put autoregressive model, e.g., Infinity, when exploring the image space compared to diffusion or flow models. Nevertheless, the Infinity model shows good quality that surpasses the real data and the LDMv1.5 model. is on par with or a bit lower terms of aesthetic quality compared to the LDMv3.5L model. Consistency-wise, the Infinity achieves the best compared to all the LDM model series. This may also correlate with the low diversity of the Infinity model as the model is more faithful to the prompt and thus limit the exploration in the image space.
constraints
visual
As for the FLUX-schnell model, it performs similarly to the LDMv3.5L model on three utility axes, with a small but noticeable drop utility, especially on the axes of diversity and consistency. The gap tends to become larger when the prompt complexity increases.
G ADDITIONAL. RESULTS
G. REFERENCE-BASED METRICS USING IMAGENET-IK LK CLASS LABELS
Figure 13 shows the reference-based metrics using ImageNet- 1k class labels. The findings are sim- ilar with the CC12M dataset, as presented in section 4.3.
27

---

Published as a conference paper at ICLR 2026
LEPALS padaecte
L0M05
LDWNLLS
700
peancl pudlenca pullance
0.30
000
coverags dinov2
0.25
84 1.droov2
500
0.60
precttion
0.20
400
300
0.50
0.10
200
prompt complexity
prompt
complexity
complexity
(a) FDD
(b) Precision
(c) Density
(d) Coverage
Figure 13: Reference-based utility metries of synthetic data using ImageNet-1k class labels. FDD, precision, density, and coverage metrics in the DINOv2 feature space for LDMv1.5 and LDMv3.5L with: 1) vanilla guidance (CFG), 2) prompt expansion, and 3) advanced guidance meth- ods, for which transparent markers correspond to different methods and the solid marker is the average over methods. Both advanced guidance methods and prompt expansion lead to better FDD, and improve match the coverage of vanilla guidance, while sacrificing precision and density in most cases. For LDMv3351, we see a more prominent effect of advanced guidance methods and prompt expansion than for LDMv1.5. Advanced guidance methods do not change the trend of the metrics w.r.f. prompt complexity, while the prompt expansion shapes the trend of the metrics differ- ently.
G.2 TRADE-OFF OF CREATIVITY AND FIDELITY WHEN USING INFERENCE-TIME INTERVENTIONS
Figs. and 13 show that using prompt expansion and advanced guidance will result in a drop in precision and density, revealing under-explored but important trade-off between creativity and fidelity. We suggest cautious usage of T2I models when applying to different downstream applica- tions. We provide discussion over this trade-off in the following.
Beneficial Scenarios:
1. Creative ideation and co-creation. Beyond simple augmentation, prompt expansion acts as exploratory partner for artists or designers. It uses the LLM's "imagination" to brain- storm novel, high- -estheet compositions that diverge from the dataset's common tropes.
2. Combating prompt-level mode collapse. For simple prompts (e.g., a dog). T2I models can default to a typical representation (e.g., golden retriever). Prompt expansion explicitly forces intra-class diversity by generating set of specific instances, preventing the model from settling on a single, average output.
3. Training for OOD Robustness: "hallucinations" create data that is plausible but statistically in the reference set, making it useful source for training downstream models that robust to distribbutt contexts.
Detrimental Scenarios:
1. Bias amplification and stereotype entrenchment: This critical risk we will add. The LLM's prior, used for expansion, is not neutral. It can inject societal biases (e.g., related to gender or race for prompts like "a doctor" or CEO"), amplifying stereotypes by hard- coding them into the prompt before the T21 model ever runs.
2. Loss of user intent and control. When a user desires generality (e.g., "a simple icon of bird"), prompt expansion's 's forced specificity is a failure respect the user's intent. overrides their desire for an "average" or "iconic" representation.
28

---

Published as a conference paper at ICLR 2026
G.3 STATISTICAL SIGNIFICANCE OF THE TRENDS
To ensure the statistical significnace of the observed trends, we compute the mean and 95% con- fidence interval of the reference-free metrics across complexities using 5,000 randomly sampled CC12M prompts. The results are shown in Table 8, 9, and 10 for diversity, quality, and consistency respectively. The confidence intervals (95% CT) are narrow, indicating high stability in the measure- ment. Notably, the intervals between consecutive complexity levels are non-overlapping, confirming that the main trends at each complexity are statistically significant.
Table 8: 95% Confidence Interval for Diversity using CC12M prompts
complexity
2
LDMv1.5 CFG
9.228=0.094
7.649+0.079
6.95740.067
6.483+0.059
APG
10.163#0.093
8.701
7.921#0.070
7.496+0.06;
CADS
11.112+0.095
9.557+0.08
8,496#0.072
7.902+0.06
Interval
11.934+0.089
10.378+0.082
9.314+0.070
8.839+0.00
+ Prompt expansion, CFG
15.074+0.089
12.977#0.097
10.882+0.092
9.725+0.081
L.Mv335LL CFG
5.597+0.053
5.493+0.044
4.29440.032
3.966+0.02
+ APG
8.978=0.090
7.331=0.079
6.089#0.065
5.302+0.054
CADS
9.864+0.108
7.690+0.099
5.954+0.067
5.21440.056
+ Interval
10.707+0.088
9.463+0.086
7.668+0.073
6.788+0.064
Prompt expansion, CFG
14.67 10.098
12.114+0.107
9.140:0.100
7.672+0.083
Table 9: 95% Confidence Interval for Quality using CC12M prompts
complexity
2
3
4
LDMv1.5 + CFG
4.085#0.011
4.297+0.013
4.335+0.012
4.357=0.012
+ APG
4.035#0.011
4.213+0.012
4.214=0.011
4.233=0.011
+ CADS
4.013+0.011
4.195:0.012
4.23940.011
4.254+0.011
+ Interval
4.017#0.009
4.163+0.012
4.204=0.011
4.213+0.011
+ Prompt expansion, CFG
4.645#0.009
4.620+0.009
4.472+0.010
4.415=0.010
LDMv3.5L + CFG
5.011
5.155+0.012
5.016+0.013
4.981+0.013
+ APG
5.071
5.179+0.010
5.01240.012
4.967#0.012
+ CADS
5.019=0.010
5.118+0.011
4.977+0.012
4.940=0.013
+ Interval
5,042+0.008
5.10110.009
4.987#0.009
4.924#0.011
+ Prompt expansion, CFG
5.238+0.008
5.22940.009
5.110+0.010
5.072+0.010
Table 10: 95% Confidence Interval for Consistency using CC12M prompts
complexity
3
LDMv1.5 CFG
0.9026+0.0026
0.815340.0029
0.7630+0.00% 0.05
0.7175+0.0022
APG
0.8853+0.0023
0.801 18001=000030
0.7520+0.0024
0.7036=0.000
CADS
0.8724+0.0029
0.7928+0.0033
0.7438+0.00% 000026
0.6981#0.0023
Interval
0.8573+0.0030
0.7788+0.003
0.731320.000 0006
0.6822+0.0022
+ Prompt expansion, CFG
0,8072+0.00 00034
0.7130+0.003
0.6902+0.00 00028
0.6502+0.002
LDMv3.5L CFG
0.9235+0.000 00023
0.840440.00 8802280.0028
0.8001#0.0022
0.7622+0.002
+ APG
0.9063+0.00% 00.05
0.8270#0.0007
0.7983+0.0022 0002
0.7606+0.000 1001
CADS
0.8546+0.0031
0.788140.00
0.7636+0.0025
0.7251#0.0022
Interval
0.8677+0.0029
0.7901
0.7715+0.000 0025
0.7378+0.0022
Prompt expansion, CFG
0.847340.0031
0.749440.0033
0.7432+0.0022
0.7083+0.0023
G.4
CLOSER LOOK MODELS AND GUIDANCES
29

---

Published as a conference paper at ICLR 2026
DATA
12
LDM635
LOVALL
0.50
-8. LONKEL
LD563.5M
LDM,3
10
4.5
0.55
2.6
LOM-3L
LOMOO SN
0.50-
LOMMAN1
4.4
0.75
5 median prampt length
2
5
median prompt length
med an prompt length
(a) Diversity
(b) Quality
(c) Consistency
Figure 14: Reference-free utility metries of synthetic data from different LDM models using CC12M prompts. We show Vendi score, aesthetic score, and DSG score using different LDM models with CFG. We see a decrease of diversity across model release time i.e. more recent models tend to have lower diversity. Interestingly, LDMv3.5L model shows less diversity than LDMv3.5M model. When it comes to image quality (aesthetics score). LDMv3.51 3.5L exhibits the best performance, followed by LDM-XL and LDMv3.50 3.M.. LDMv1.5 shows the worst aesthetic score, which is however similar to that of the dataset. When it comes to amage-prompt consistency, all models show relatively high DSG scores. However, LDMv1.5's consistency drops more sharply than the consistency of other models.
A closer look at diffferent models. Figure 14 presents reference-free metrics (Vendi, aesthetic, and DSG scores) comparing different LDM models on the CC12M dataset when using CFG guid- ance. We observe that Vendi score and aesthetics score follow opposite model ranking trends, with LDMv3.5L showing the best aesthetic quality and the lowest diversity performance. As for image- prompt consistency, we observe that LDMv1.5 model falls short from other models by a large mar- gin.
Figure 15 presents effrrence-bssed metries comparing the same models. I.MM33.5M model works surprisingly well in terms of FDD compared to LDMv3.5L, considering that they both show similar reference-free diversity and that LDMv3.5M 5M has a lower aesthetic quality than LDMv3.55 LL. Looking into precision, density and coverage, we can better understand why this happens. LDMv3.5M model shows similar precision and higher density than LDMv3.5L. while also showing higher coverage, leading to overall better FDD. Our results on CC12M dataset show that LDMv3.5M may better represent the real data distribution than LDMv3.5L.
A closer look at advanced guidance methods. We now shift our focus to study the effect of differ- ent advanced guidance approaches on the utility axes of synthetic data. Figure 16 presents the results of reference-free metrics from LDMv3.5L model using CC12M prompts of increasing complexity. All advanced guidance methods lead to substantially higher diversity than the baseline CFG (pre- viously referred to vanilla guidance), with stable aesthetic quality across different approaches. This is observed consistently across different prompt complexities. It is worth noting that APG leads to the lowest consistency drops w.r.l. CFG. APG primarily addresses the over-saturation problem in synthetic images by reducing the perpendicular direction guidance but still uses the conditional in- formation at all inference steps, which might help the method achieve higher consistency than other the advanced guidance methods. However, CADS and Interval are more aggressive in removing the conditional information during the generation process, leading to overall lower consistencies across prompt complexities. As for reference- based metrics, all advanced guidance methods lead to better FDD. These results suggest that the advanced guidance methods may effectively improve the diversity of synthetic images while maintaining stable quality and sufficiently good consistency, without sacrificing the overall reference- based metrie (FDD). Finally, when contrasting with real data utility, observe that: 1) None of the advanced guidance techniques bridges the gap between the diversity resulting from vanilla CFG guidance and the real data diversity for LDMv3.5L model. 2) All advanced guidance techniques have similar aesthetics, which is in all cases superior to the
30

---

Published as a conference paper at ICLR 2026
LEM
LOWYLS
0.775
LEPAL5 LEW-L
LC04
LOWYLS
0.45
LOMAAASN
LUMRE 3M
0.750
500
prectson on Sheva
0.725
dencty adhoovz
0.35
600-
0.700
0.675
8.2
2.30
0.450
0.6
200
0.625
0.20
LEW-OL
LOBKCL.M
2
medan pronpc kngtth
median promot length
median pronpe ength
median prompt length
(a) FDD
(b) Precision
(c) Density
(d) Coverage
Figure 15: Reference-based utility metries of synthetic data from different LDM models using CC12M prompts. FDD, precision, density, and coverage metrics in the DINOv2 feature space for different LDM models when using CFG guidance scale 5. For FDD, LDMv1.5 shows the best performance. Interestingly, LDMv3.5M has better FDD than L.Mv3.5L. As for precision and density, more recent models tend to have better performances. LDMv3.5M and LDMv1..5 models show similar coverage, with LDM-XI -XL slightly falling behind, and LDMv3.55 5L presenting the lowest coverage.
0.30
DATK
500
CRO
CPG
ATG
12
APG
5.0-
0.85
500-
ScSthettc
DATA CFR
450
yorr
APQ
4.6
0:75
350-
100
0.70
250-
prompt length
median promptt lngghh
median promot ength
promet ength
(a) Diversity
(b) Quality
(c) Consistency
(d) FDD
Figure 16: Effect of guidance methods on the utility of synthetic data from LDMv3.5L using CC12M prompts. Diversity (Vendi), quality (aesthetics), consistency (DSG), and FD in DINOv2 feature space. All advanced guidance methods lead to higher diversity than the CFG baseline. APG has the smallest negative effect on consistency compared to CADS and Interval guidance. All ad- vanced guidance methods lead to better FDD.
one of the real data. 3) Among advanced guidance methods, APG appears to sacrifice prompt-image consistency the least.
A closer look at guidance scales. In this paragraph, study how guidance scale affects the utility of synthetic data. We present diversity (Vendi score) and aesthetic quality for LDMv1.5 and LDMv3.5L with guidance scales 3.0, 5.0, 7.0, and 9.0 Figure 17. Perhaps unsurprisingly, we observe that increasing the guidance scale results in decreasing the diversity of synthetic images. However, the diversity captured by Vendi score increases for LDMv3.5L. model when using very large guidance scale (e.g., 9.0 in Figure 17b). We hypothesize that this increase in Vendi score is due to the oversaturation in the synthetic images and the color range exceeding the color jittering range employed in the data augmentation process of DINOv2, making the DINOv2 features color- sensitive. For different models and different metrics, the sensitivity to the guidance scale varies. For example, LDMv1.5 is more sensitive towards diversity while LDMv3.5L. is more sensitive towards aesthetic quality as the guidance scale changes. Interestingly. LDMv1.. 1.5 has better aesthetic quality with higher guidance scales while L.Mv33.5 1. shows the opposite trend.
31

---

Published as a conference paper at ICLR 2026
DASA
5.4
5-6
5-
5.2-
12
12
7.0
9.0
9.0
5.0
vord din
10-
Sesthetic
4.0
4.5
9.6
6-
4.2
4,2
4.0
3.8
3.8
2
median prompt length
modian prompt
(a) Diversity LDMv1.5
(b) Diversity L.MMv3.51.
(c) Quality
(d) Quality
LDMv1.5
LDMv3.51.
Figure 17: Effect of guidance scale on diversity (Vendi) and quality (aesthetic) of synthetic images from CC12M prompts. Metrics are computed in the DINOv2 feature space. For LDMv1.5 the diversity decreases as increase the guidance scale, while for LDMv3.5L, the diversity first decreases and then increases again when using the highest guidance scale. LDMv1.5 shows better aesthetic quality when increasing the guidance scale while LDMv3.51 shows the opposite trend.
0.95
700
DSJA
DATA
DATA
DATA
5.5
600
0.85
0.85
500
00 0.80
0.75
75
300
200
0.65
0.65
2
5
1 2
median promot length
median prompt length
med an prompt length
median an prompt length
(a)
Consistency
(b)
Consistency
(c) FDD
(d) FDD LDMv3.5L
LDMv1.5
LDMv3.5L
LDMv1.5
Figure 18: Effect guidance scale on image-prompt consistency (DSG) and FDD of syn- thetic images from CC12M prompts. DSG score is not sensitive to guidance scale, especially for LDMv3.5L model. For LDMv1.5 model, the consistency slightly increases when employing larger guidance scales. As for FDD, both LDMv1.5 and LDMv3.5L models exhibit increase of FDD when generating images with larger guidance scale.
We also depict consistency and FDD metrics using different guidance scales in Figure 18. Image- prompt consistency does not appear too sensitive to guidance scale, especially for LDMv3.5L. For LDMv1.55, the consistency slightly increases when employing larger guidance scales. As for FDD, both LDMv115 and LDMv3.5L exhibit increases of FDD when generating images with larger guid- ance scales.
H ADDITIONAL QUALITATIVE SAMPLES
In Figure 19, we show qualitative samples for LDMv3.5L model using vanilla guidance (CFG), CADS, and prompt expansion with prompts extracted from CC12M. Notably, observe that as we increase prompt complexity, the generations appear closer to the real data and exhibit lower diversity, slightly lower prompt consistency, and similar image aesthetics. Moreover, inference-time
32

---

Published as a conference paper at ICLR 2026
yea data
15.87
15.49
14.04
8.47
CFG
8.72
6.07
4.90
4.37
CADS guidance
11.87
10.43
7.03
5.51
prompi pommpeee expansion CFG
5.73
17.12
15.68
11.79
(d) Golden domes and
(a) Building.
(b) Grand building.
(c) Grand building, or- buildings against a clear nate domes.
sky.
prompt complexity increasing
Figure 19: Synthetic images across prompt complexity (row-wise) and sampling methods (column-wise). The number below each mosaic corresponds the diversity metric (Vendi score) of that set of images. Diversity decreases both visually and quantitatively as increase prompt complexity. Advanced sampling methods (CADS and prompt expansion) improve the diversity of synthetic data. Image quality is not significantly affected by the prompt complexity, but prompt consistency decreases as we increase prompt complexity. CFG: classifier-free free guidance.
interventions and their combinations (rows 3, 4) result in synthetic data with higher diversity than the CFG baseline (row 2).
We show qualitative samples in Figure 20 for LDMy3.5L model using vanilla guidance (CFG), CADS, and prompt expansion with class labels extracted from ImageNet- According to Word- Net (Fellbaum, 1998), "stringed inst rument includes harp, violin, cello, guitar, piano, etc., ''guitar'' includes acoustic guitar and electric guitar.
We also show more qualitative samples in Figures 21 to 28 using all sampling intervention methods, generated by all four T2I models (1.DMv1.55 LDM-XI., LDMv3.5M., and LDMv3.55.), complement-
33

---

Published as a conference paper at ICLR 2026
real data
19.32
14.55
13.21
CPG
5.78
5.68
4.19
CADS guidance
7.04
6.70
6.30
prompt expansion + CFG
12.65
7.92
10.55 (b) guitar
(a) stringed rument
(c)
electric
guitar
prompt complexity increasing
Figure 20: Synthetic images across prompt complexity (row-wise) and sampling methods (column-wise) using prompts from ImageNet-Ik. The number below each mosaic corresponds to the diversity metric (Vendi score) of that set of images. Diversity decreases both visually and quantitatively as we increase prompt complexity. Advanced sampling methods (CADS and prompt expansion) improve the diversity of synthetic data. Image quality is not significantly affected by the prompt complexity, but prompt consistency decreases as we increase prompt complexity. CFG: classifier-fre free guidance.
ing the results in Figure 19. We observe qualitatively that prompt expansion and advanced guidance methods lead more diverse images than the vanilla guidance.
34

---

Published as a conference paper at ICLR 2026
Dataset
(b) Vanilla guidance
Prompt expansion
(d) CADS guidance
(c) Interval guidance
(f) APG guidance
Figure 21: Qualitative visuals for the prompt "Trees frame a peaceful building'
using the LDMv3.5L model with different sampling settings.
Dataset
Vanilla guidance
Prompt expansion
(d) CADS guidance
(c) Interval guidance
(f) APG guidance
Figure 22: Qualitative visuals for the prompt 'Trees frame a peaceful building using the LDMv1.5 model with different sampling settings.
35

---

Published as a conference paper at ICLR 2026
Dataset
(b) Vanilla guidance
Prompt expansion
(d) CADS guidance
(c) Interval guidance
(f) APG guidance
Figure 23: Qualitative visuals for the prompt TTress frame a peaceful building'
using the LDMv3.5M model with different sampling settings.
(a) Dataset
Vanilla guidance
Prompt expansion
(d) CADS guidance
(c) Interval guidance
(f) APG guidance
Figure 24: Qualitative visuals for the prompt 'Trees frame a peaceful building using the LDM-XL model with different sampling settings.
36

---

Published as a conference paper at ICLR 2026
(a) Dataset
Vanilla guidance
Prompt expansion
(d) CADS guidance
(c) Interval guidance
(f) APG guidance
Figure 25: Qualitative visuals for the prompt Cars and a grand building' ' using
the LDMy3.5L model with different sampling settings.
(a) Dataset
(b) Vanilla guidance
Prompt expansion
(d) CADS guidance
(c) Interval guidance
(f) APG guidance
Figure 26: Qualitative visuals for the prompt 'Cars and a grand building using the LDMv1.5 model with different sampling settings.
37

---

Published as a conference paper at ICLR 2026
(a) Dataset
Vanilla guidance
Prompt expansion
(d) CADS guidance
(c) Interval guidance
(f) APG guidance
Figure 27: Qualitative visuals for the prompt Carrs and a grand building' ' using the LDMy3.5M model with different sampling settings.
(a) Dataset
(b) Vanilla guidance
Prompt expansion
(d) CADS guidance
(c) Interval guidance
(f) APG guidance
Figure 28: Qualitative visuals for the prompt 'Cars and a grand building' using the LDM-XL model with different sampling settings.
38


Reviews:
[{"review_id": "uMwGgF430R", "reviewer": "Reviewer_QZqM", "summary": "This paper investigates how prompt complexity affects synthetic data utility from T2I models across quality, diversity, and consistency axes. The authors (1) conduct synthetic experiments on Gaussian mixtures with theoretical derivations, (2) introduce an evaluation framework comparing real vs. synthetic data, and (3) perform large-scale empirical analysis across CC12M, ImageNet-1k, and DCI datasets with multiple T2I models and inference-time interventions. Key findings: increasing prompt complexity reduces diversity and consistency but narrows the real-synthetic gap; prompt expansion consistently improves diversity and quality; combining advanced guidance with prompt expansion yields optimal trade-offs", "questions": "- **Can you characterize discarded images in the alignment step?** Specifically, do they have different mean aesthetic quality, object count, scene complexity, or semantic diversity compared to retained images? This would help assess whether the observed trends are artifacts of selection bias.\n- **On Theoretical Assumptions:** Could you discuss the validity of the conditional independence assumption (Appendix A.2) in the context of compositional text encoders like CLIP? How might the violation of this assumption, where concepts like \"red\" and \"car\" are highly correlated and compositionally represented, affect the interpretation that generalizing to general prompts is inherently \"harder\" than generalizing to fine-grained ones? Could this theory-practice gap also explain some of the divergent behaviors observed between the CC12M and ImageNet experiments?\n- **Can you provide confidence intervals or significance tests for main trends?** With 5,000 prompts per complexity, bootstrapped confidence intervals for Vendi, aesthetic, and FDD would strengthen claims about trends and help assess how statistically significant differences between adjacent complexity levels are.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Important and underexplored research question.** This is the first systematic study of how prompt complexity affects T2I synthetic data utility. Given the widespread use of T2I models for data generation and the common practice of training on synthetic captions, understanding this relationship is timely and valuable.\n    \n- **Well-designed evaluation framework.** The 5-step pipeline (captioning, pairing, alignment, sampling, generation) enables fair comparisons between real and synthetic data across prompt complexities, a non-trivial methodological contribution that could benefit future work.\n    \n- **Comprehensive empirical evaluation.** The study covers multiple datasets (CC12M, ImageNet-1k, DCI), models (LDMv1.5/XL/v3.5M/v3.5L, Flux, Infinity), inference methods (CFG, CADS, Interval, APG, prompt expansion), and metrics (reference-free: Vendi, aesthetic, DSG; reference-based: FDD, precision, density, coverage), providing thorough coverage.\n    \n- **Human validation strengthens metric choice.** The human evaluation (Appendix E) shows Vendi score strongly correlates with human-perceived diversity, validating the automatic diversity metric.\n    \n- **Illustrative toy example provides intuition.** The synthetic Gaussian mixture experiments (Section 2) with mathematical derivations (Appendix A.2) offer clear intuition for why generalizing to general prompts is harder, nicely complementing the empirical findings.", "weaknesses": "- **Independence assumptions in theoretical derivations may not hold for real text encoders.** The synthetic experiments (Section 2, Appendix A.2) derive score functions assuming conditional independence of text concepts. Specifically, Equations 5-7 assume that for fine-grained conditioning $c_f$ composed of general concepts ${c^i_g}$, we have $p(x_t, c^1_g, c^2_g, ..., c^K_g) = p(x_t) ∏_{i} p(c^i_g|x_t)$. However, real text encoders (CLIP, T5) do not treat concepts as independent: (1) they encode compositional phrases where \"white\" modifying \"dog\" produces entangled representations rather than separable \"white\" and \"dog\" signals, (2) they learn correlations from training data where certain concept combinations (e.g., \"white dog\") appear frequently, making $p(c^1_g|x_t, c^2_g) ≠ p(c^1_g|x_t)$, and (3) concept embeddings are context-dependent and non-orthogonal in the latent space. Similarly, the OR operator derivation (Equation 1) requires fine-grained categories to be exhaustive and mutually exclusive, which may not align with how models internally represent general concepts. The Gaussian mixture model provides valuable intuition, but the theoretical predictions should be interpreted cautiously when applied to real T2I models. The authors should discuss this gap and consider how it might affect the interpretation of their results. \n- **The alignment step lacks transparency and may introduce selection bias.** Algorithm 1 iteratively removes images not shared across complexities, but provides no analysis of what is discarded. Table 1 shows complexity-4 prompts cover only 46,066 images vs. 61,334 for complexity-1, a 25% reduction. What visual or semantic characteristics differentiate retained vs. discarded images? If alignment preferentially keeps images easier to describe with detailed prompts (e.g., clear objects vs. abstract scenes), this biases the evaluation set. Do discarded images have different mean aesthetic quality, diversity, or complexity than retained ones? Without this analysis, it is difficult to disentangle the genuine effects of prompt complexity from potential artifacts introduced by the evaluation pipeline itself.", "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": "lDqsnYtQLd", "reviewer": "Reviewer_N9uF", "summary": "The paper studies how prompt complexity (length/specificity) affects the quality, diversity, and prompt-consistency of images generated by text-to-image (T2I) models. It first uses a toy Gaussian setup with derivations to argue that generalizing from fine-grained training prompts to more general prompts is intrinsically harder (likelihood weighting is missing in standard diffusion), then introduces a benchmarking framework that builds multi-complexity prompts from CC12M, ImageNet-1k, and DCI and evaluates several open T2I models and inference-time interventions. Large-scale experiments show: increasing complexity reduces diversity and consistency, while aesthetics first rise then fall as prompts lengthen; prompt expansion improves diversity/aesthetics but may move generations off the real-data manifold; combining expansion with advanced guidance yields the best trade-offs.", "questions": "Your work studies semantic composition in prompts. A general question is, could LLM-derived semantics be used during training to improve robustness to complex or general prompts? \n\nAlso please see the weaknesses part.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Writing & clarity. The paper is clearly written and easy to follow, with a logical flow (synthetic intuition → framework → large-scale evaluation), helpful figures, and explicit definitions of each utility axis and metric. Reproducibility details (datasets, hyperparameters, code note) are thorough.\n\n2. Comprehensive, reproducible evaluation framework. The authors propose a general framework (captioning → pairing → cross-complexity alignment → sampling → generation) that enables fair comparison between real and synthetic data across complexities, and they test multiple model families (LDM v1.5/v3.5, SDXL, etc.) and guidance strategies (CFG, CADS, Interval, APG) plus prompt expansion, with both reference-free (Vendi, aesthetics, DSG) and reference-based (FDD, precision/density/coverage) metrics—this breadth is valuable for practitioners.", "weaknesses": "1. Findings mostly expected; limited novelty. Many conclusions—e.g., higher prompt complexity reduces diversity/consistency, prompt expansion increases diversity but can harm faithfulness—align with common practitioner intuitions and prior observations.\n\n2. No concrete method to handle complexity. The work is primarily evaluative. It surfaces that general prompts are hard because diffusion lacks likelihood weighting and that expansion/guidance can trade off axes—but it stops short of proposing a principled, train-time solution.", "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": "QvEQfe8H6Y", "reviewer": "Reviewer_PuV8", "summary": "This paper presents a systematic analysis of how prompt complexity influences the quality, diversity, and consistency of synthetic data from text-to-image models. It establishes that increasing prompt complexity reduces diversity and consistency while improving the realism of generated images. The authors provide theoretical insight, showing that models struggle to generalize to general prompts, and introduce a comprehensive benchmarking framework for evaluation. Their large-scale empirical study identifies prompt expansion combined with advanced guidance like APG as the most effective approach for achieving optimal utility trade-offs in synthetic data generation.", "questions": "1.The authors rely on the DSG score for consistency evaluation. Could they clarify how they ensured this metric's validity for the very long, complex prompts from the DCI dataset? Providing a small-scale human evaluation to correlate with the DSG scores on these long prompts would help solidify the consistency findings.\n\n2.The authors note a drop in consistency for highly complex prompts. Could they comment on the semantic plausibility of these generations? Is the failure primarily in missing attributes, or also in generating globally incoherent scenes?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1.Introduces the novel problem of systematically evaluating \"prompt complexity\" in T2I models, combining theoretical insight (\"OR\" vs. \"AND\" generalization) with practical interventions.\n\n2.Methodologically rigorous, featuring a well-designed benchmarking framework, compelling synthetic experiments, and a large-scale, comprehensive evaluation across models and datasets.\n\n3.Exceptionally well-structured and clearly written, with a logical narrative flow and effective figures that make the complex study accessible and reproducible.\n\n4.Provides crucial guidance for synthetic data generation and model evaluation, revealing fundamental trade-offs and setting an important agenda for future T2I model development.", "weaknesses": "1.The paper relies on the DSG score for consistency evaluation but does not critically address its known limitations with very long, complex prompts (as in the DCI dataset). As prompt length increases, the VQA models underlying DSG may themselves fail, potentially conflating model inconsistency with VQA model error. A targeted analysis, such as a human evaluation on a subset of long prompts to verify DSG's correlation with human judgment in this regime, is needed to ensure the reported consistency drop is reliable.\n\n2.While prompt expansion is highlighted as a powerful intervention, the paper provides a limited discussion of its significant downside: it actively moves generations outside the support of the reference real data (as shown by reduced precision/density). This is a critical trade-off that is under-explored. The work would be improved by a deeper analysis of when this \"hallucination\" is beneficial (e.g., for data augmentation) versus detrimental (e.g., for faithful dataset replication), framing it not just as a boost in diversity but as a fundamental shift in the data distribution.", "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": "cm5PAOqkzG", "reviewer": "Reviewer_CGJq", "summary": "This paper analyzes how prompt complexity affects the quality, diversity, and text-image alignment of text-to-image (T2I) generations. Through synthetic and large-scale experiments, the authors show that generalizing to simpler (more general) prompts is inherently harder for diffusion models. Increasing prompt complexity reduces diversity and consistency but narrows the gap to real data. Among inference-time methods, prompt expansion using large language models to enrich prompts consistently improves image quality and diversity, and combining it with advanced guidance yields the best trade-offs.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- It is interesting to investigate the relationship between prompt complexity and the utility of generative models, including quality, diversity, and text-image alignment.\n\n- The paper provides several insightful observations, such as the generalization asymmetry between simple and complex prompts and the non-monotonic improvement of quality as complexity increases.\n\n- The authors propose an improved sampling strategy that combines prompt expansion with advanced diversity samplers (e.g., APG), based on the observation that moderately complex prompts can enhance generation quality.", "weaknesses": "- **Lack of new messages.** Apart from the findings on generalization asymmetry and non-monotonic quality trends, most conclusions (e.g., that LLM-based prompt expansion increases diversity) are fairly predictable or already known.\n\n- **Weak application value.** Except for the combined sampler (e.g., prompt expansion + APG), the study remains largely descriptive and provides limited guidance for improving model performance.\n\n- **Clarity and focus.** The paper covers a large number of datasets, models, and metrics, which sometimes dilutes the main narrative and makes it harder to pinpoint the central contribution.\n\n- **Limited theoretical depth.** Although the paper provides a toy Gaussian experiment and a theoretical discussion, the analysis remains relatively shallow and does not fully explain why diffusion models exhibit the observed asymmetry beyond intuitive reasoning.\n\n- **Evaluation bias toward LDM family.** The study mainly focuses on LDM variants, leaving uncertainty about whether the conclusions generalize to non-diffusion architectures or transformer-based T2I models.\n\n- **No user evaluation.** The work focuses entirely on metric-based analyses without human validation to confirm the practical implications of prompt complexity.", "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)