CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration
Authors: Seyed Amir Kasaei
Ali Aghayari
Arash Marioriyad
Niki Sepasian
Shayan Baghayi Nejad
MohammadAmin Fazli
Mahdieh Soleymani Baghshah
Mohammad Hossein Rohban
Venue: Accepted by TMLR ()

Abstract: Text-to-image diffusion models, such as Stable Diffusion, can produce high-quality and diverse images but often fail to achieve compositional alignment, particularly when prompts describe complex object relationships, attributes, or spatial arrangements. Recent inference-time approaches address this by optimizing or exploring the initial noise under the guidance of reward functions that score text–image alignment—without requiring model fine-tuning. While promising, each strategy has intrinsic limitations when used alone: optimization can stall due to poor initialization or unfavorable search trajectories, whereas exploration may require a prohibitively large number of samples to locate a satisfactory output. Our analysis further shows that neither single reward metrics nor ad-hoc combinations reliably capture all aspects of compositionality, leading to weak or inconsistent guidance. To overcome these challenges, we present Category-Aware Reward-based Initial Noise Optimization and EXploration (CARINOX), a unified framework that combines noise optimization and exploration with a principled reward selection procedure grounded in correlation with human judgments. Evaluations on two complementary benchmarks—covering diverse compositional challenges—show that CARINOX raises average alignment scores by +16% onT2I-CompBench++ and +11% on the HRS benchmark, consistently outperforming state-of-the-art optimization and exploration-based methods across all major categories, while preserving image quality and diversity.

Published in Transactions on Machine Learning Research (02/2026)
CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration
Scyed Amir Kasaci
I.kasacc@mmer
Department of Compuler Engineering
Sharif University of Technology
Ali Aghayari
ali wllaghhayrrihhtmmil.cco com
Department of Computer Engineering
Sharif University of Technology
Arash Marioriyad
anshmmarioripoa@@@mallcco
Department of Computer Engineering
Sharif University of Technology
Niki Sepasian
sepasian.nkki@@maatee
Depariment of Computer Engineering
Sharif University of Technology
Shayan Baghayi Nejad
shayernbahha&&@@@aaiicom com
Department of Computer Engineering Sharif University of Technology
Mohammad Amin Fazli
Juzli@shariff edu
Department of Computer Engineering Sharif University of Technology
Mahdieh Soleymani Baghshah
soleymani@sharif.commm edu
Department Computer Engineering Sharif University of Technology
Mohammad Hossein Rohban
rohban Aabban@sharifech
Department of Computer Engineering Sharif University of Technology
Reviewed OpenReview: https: https://openrevieuu
openreview. net/ forum? id= XB1 cuXHVOc
Abstract
Tlex--o-imaaee diffusion models, such as Stable Diffusion, can produce high-quality and di- verse images but often fail to achieve compositional aligmment, particularly when prompts describe complex object relationships, attributes, or spatial arrangements. Recent inference- time approaches address this by optimizing or exploring the initial noise under the guidance of reward functions that score text image alignment without requiring model fine-tuning, While promising, each strategy has intrinsic limitations when used alone: optimization can unfavorable search trajectories, whereas exploration may
stall due to poor initialization require a prohibitively large number of samples to locate satisfactory output. Our analysis further shows that. neither single reward metrics nor ad-hoc combinations reliably capture all aspects compositionality, leading to weak or inconsistent guidance. To overcome these challenges, we present Category-Aware Reward-based Initial Noise Optimization and EXploration (CARINOX), unified framework that combines noise optimization and exploration with a principled reward selection procedure grounded in correlation with

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Published in Transactions on Machine Learning Research (02/2026)
human judgments. Evaluations on two complementary benchmarks cerchmarrs--overinng covering diverse com- positional challenges- show that CARINOX raises average alignment scores by + +16% on 12J-CoomBBench++ and +11% on the HRS benchmark, consistently outperforming state- of-the-art optimization and exploration- based methods across all major categorics, while preserving image quality and diversity. The project page available at this URL
SD2.1
InitNO
SD-Turbo
ReNO
CARINOX
SDXL-Tarbo
R&NO
CARINOX
red rahbit and yellow rat
small lion and big horse
four lamps and four dogs
Qualitative results on T21- SenCChmpBench+++ showing that CARINOX faithfully captures compo- sitional details such as counts, spatial arrangements, and attribute bindings.
Figure 1:
1 Introduction
Podell et al. 2023 and DALL-E [Ramesh et al. 2022 have garnered substantial attention for their ability to synthesize high-quality images from natural language prompts through iterative denoising and cross-modal attention mechanisms. These models have been adopted in wide range of applications, including image editing
Text-to-image (T2T) diffusion models, such as Stable Diffusion (SD) (Rombach et al. 2022
Huang et al. 2024b Kawar et al.
2023 Liu et al. 2024a Mou al. 2024). , data augmentation [Li et al.
2024c Xiao et al. 2023 Peng et al. 2023a), medical imaging (Huang et al. 2024a Liet al. 2024b Khader
et al. 2023 Lin et al. 2024a), and marketing (Shilova et al. 2023 Yang et al. 2024h. Despite their versatility
and impressive generation capabilities, T21 diffusion models often exhibit notable failures in compositional
alignment (Huang et al.) 2025 Bakr et al.] 2023 Ghosh et al.) 2024). These failures manifest in various forms, including enlily omnission
(Chefer el al. 2023 Sueyoshi Matsubara 2024: Zhang el al. 2024 Liu
et al.
2022
Kim et al.] 2023),
incorrect attribute binding (Feng et al.) [202bb] Singh Zheng 2023 Rassin
et al.
2024 Wang ct al.
2024h, misrepresentation of spatial relationships (Zhang et al. 2024 Gokhale al.
2022
Chen et al. 2024). and numeracy errors (Binyamin et al. 2024 Zafar al. 2024 Kang et al.) 2023b
To address compositional generation failures, several studies have explored fine-tunine sre-tuning--base approaches. While effective, such methods are often computationally expensive and time-consuuing. In response, a range of inference-time time techniques has emerged, aiming to improve generation quality without modifying the underlying model. A similar trend has been observed in large language models (I.LMs), where recent work enhances reasoning capabilities by employing verifiers such as reward functions during inference rather than through fine-tuning. Within the T2I domain, subset of inference-ting methods focuses on leveraging the initial noise to improve alignment. These approaches fall into two main categories: optimization baased methods, such as ReNO (Eyring et al. 2024) and InitNo (Guo et al. 2024b), which iteratively refine the initial noise to maximize alignment based on a reward signal; and exploration basedd methods, including ImageSelect Karthik et al. ).23). SeedSelect (Samuel et al. 2024b), Seml (Mao et al. 2024), ParticleFilterin (Liu et al. 2024b), and RelibbieRnndonSeeds (Li et al. 2024a), which evaluate multiple noise samples and select the one yielding the best result. In both settings, reward functions guide the process by scoring how well each candidate image matches the input prompt.
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Despite recent progress, existing approaches face two critical challenges that we address in this work. First, both continuous noise optimization and discrete noise selection strategies suffer from inherent limitations when used in isolation. Optimization methods are sensitive to the choice of initial noise and may fail to align the generated image with the prompt due to poor initialization or unfavorable optimization trajectorics even when the starting image appears qualitatively plausible (see Figure 2a In contrast, exploration-bassed methods are limited by the nature of their search process: they typically sample from a fixed set of candidates and evaluate each independently, often requiring many trials to find a well-aligned output, particularly in the digh-iimensional latent space of diffusion models (see Figure 2b These limitations are analyzed in more detail in Section 3 Second, the choice of reward function is crucial for guiding generation, yet remains underexplored. Many existing works adopt commonly used metrics without accounting for the specific challenges of compositionality, such as spatial reasoning, entily binding, or numeracy. As a result, the reward signal may be weak or misaligned, reducing the effectiveness of both optimization and exploration.
To overcome these limitations, we propose CARINOX, a novel framework that integrates both noise op- timization and exploration strategies with a carefully selected reward function to improve compositional alignment in T21 generation. CARINOX addresses the shortcomings of existing methods by combining con- timuous optimization of initial noise with targeted discrete exploration strategy, effectively reducing the risk of poor optimization paths and the inefficiency of blind sampling. To support this process, we sys- tematically derive a robust combination of reward metrics through an empirical correlation study against human judgments, ensuring that the guidance used during generation is aligned with compositional quality. Through this design, CARINOX unifies the strengths of both optimization and exploration while grounding the reward function in principled, data-driven selection process tailored to compositional challenges.
We evaluate CARINOX two widely used benchmarks 121-CompBench++ Huang et al. (2025) and HRS (BakT et al. ) covering a broad spectrum of compositional challenges. Across both datasets, CARINOX consistently improves over the underlying backbones. On T2I- D22-CommBeecch++ it raises the average performance of SD-Turbo from 0.39 to 0.57, SDXL-Turbo from 0.41 to 0.57, and PixArt-a from 0.35 to 0.58, with the strongest gains in texture, numeracy, and spatial reasoning. On the HRS benchmarkk it further enhances all three backbones, delivering mean improvements of +0.18 on SD-Turbo, +0.16 on SDXL- Turbo, and +0.23 on PixArt-a, and setting new highs in creativity, style, and visual writing. Notably, these gains are achieved while preserving image quality and diversity, showing that CARINOX strengthens compositional alignment without compromising realism.
2 Related Works
Research on compositional generation in T21 diffusion models can be grouped into two families: fine-tuning methods, which update model paramcters, and inference-time methods, which enhance alignment without additional training. Fine-tuning either modifies the denoiser or the text encoder. Denoiser-level updates
adapt the UNet or add auxiliary modules for spatial control and attribute binding Sun et al. (2023 Jiang
et al. 22024) Guo et al. (2024a); Zhang et al. (2023) ; Mou et al. (2023), but demand extra compute and risk overfitting. Encoder-level fine-tuning instead adjusts the conditioning space with lightweight projections or
causal refinements over frozen CL.IP embeddings Zarei et al. 22025) , offering better generalization but weaker
handling of spatial errors. Inference-time methods operate at different stages of generation. Prompt-level rewriling with lexical search or LLM feedback improves attributes and personalization Yu et al. 2024) He
et al. (2025), though often costly and verbose. Simbedding-lever adjustments refine frozen encoders to control object counts, attributes, or relations Zafar et al. (2024): Deckers et al. (2024), but are sensitive to hyperparameters. Finally, noise- and latent-level methods exploit the strong influence of initialization, forming the basis for optimization and exploration strategies detailed in the next subsections.
2.1 Inference-Tinee Latent-Space Search and Optimization
Discrete Noise Exploration. From this category, ImageSelect Karthik et al.) (2023) and SeedSelect
Samuel et al. (024b) scarch over candidate sceds, choosing the one best matching a scoring hcuristic (c.g.. CLIP similarity). SemI Mao et al.) (2024) biases selection toward noise vectors empirically linked to stronger object binding, exploiting "lucky" seeds as reproducible advantages. ParticleFiltering L.u et al. (0024b
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instead performs sequential resampling during reverse diffusion, discarding low-scoring partial generations and retaining promising ones. Relatedly, recent inference-time scaling methods formulate reward align- ment as Sequential Monte Carlo (SMC) sampling with a population of particles and sequential resampling: DAS Kim et al. (202bb) performs test-time alignment of diffusion models via SMC-basod sampling to im- prove reward while mitigating over-oplimization, Kim et al. (2025a) extend particle-based scaling to flow models by injecting stochasticity and reallocating compute via rollover budget forcing, and V-Sampler Yoon et al. (025)) improves SMC-based aligument in score models by initializing particles from a reward-aware posterior rather than Gaussian prior. These methods are fully training- free and turn stochastic seed choice into systematic search, but incur high computational cost, depend on potentially noisy scoring signals, and remain insufficient when base models exhibit severe binding failures.
Continuous Initial Noise Optimization. These methods mainly instead refine the initial noise itera- tively using reward signals from the final generated image, directly enforcing compositional constraints test time. InitNO Guo et al. (024b) optimizes noise with an allention-aware objective that penalizes missing ob- jects and concept mixing, steering sampling away from indduiitn regions. ReNO Eyring et al. (2024) extends this to multi-roward optimization, ascending gradients of preference models (c.g.. text alignment or detection scores) with respect to noise, improving counting, co-occurrence, and attribute binding. Beyond these, DOODL Wallace et al. (2023) performs end-to-end optimization of the initial diffusion latent by back- propagating losses defined on the final generated image through the denoising process. Related source-latent optimization has also been explored for flow-based generators: D-Flow Ben-Hamu al. (2024) differentiates through flow-based generalion to optimize the source noise under arbitrary costs, and ORIGEN Min el al. (2025) applies reward-guided updates of the initial latent in one-step flow model to enforce 3D orientation grounding. These methods provide strong, training- free gains and flexibly integrate new constraints, but add inference overhead from iterative scoring/ hatpg/bbabppppaattionn remain sensitive to reward design (risk of reward hacking), and rely on external scorer quality.
Continuous Latent Optimization. The category of methods refine noisy latent codes using loss func- tions on intermediate cross-attention maps, encouraging concept preservation and disentanglement during denoising. Attend-and--xeite Chefer ct al. (2023 mitigates neglect by amplifying subject-token activations, while Divide&Bind Li et al. (2023c) adds attendance and binding losses for multi-entity prompts and at- bobpuee-ojject pairing. Predicated Diffusion (Sueyoshi & Matsubara (2024) encodes prompt semantics as predicate-logic propositions and treats attention maps fuzzy prodicates, enabling diffcrentiable objectives for complex relations. Attention Regulation Zhang et al. (2024) formulates cross-attention control con- strained optimization that suppresses dominant tokens and boosts under-attended ones, and A-STAR Agar- wal et al. (2023) combines attention segregation (reducing token overlap) with retention (preserving salience across timestops). Collectively, these training-free methods improve semantic fidelity, recall, and binding by targeting the altention interface, but add inference overhead and are sensitive to hyperparameters balancing faithfulness, diversity, and runtime.
2.2 Reward Models for Text-Image Alignment
Compositional alignment rewards assess how well a generated image matches a text prompt in terms of objects, attributes, and spatial relations. Embedding-basee methods are widely used, with CLIPScore com- puting similarity between CL.IP embeddings of text and image Hessel et al. (2021). Extensions include HPS, which fine-tunes CLIP on preference data to better match human judgments Wu et al. (202)), and PickScore, which adapts CLIP-H with preference supervision for closer correlation with human rankings Kirstain et al.) (2023). Moreover, ImageReward trains a standalone reward model on human evaluations to capture prompt relevance and perceptual quality Xu et al. (2024). Complemcntary to these, VQA-based methods assess alignment by testing whether the image supports answers to prompt-derived questions: TIFA generates structured QA pairs and checks them with a pretrained VQA model Hu et al. (2023), while VQAScore
applics a similar principle and achieves higher correlation with human judgments Lin ct al. (2024b). Other
approaches in this family, such as DA, DSG, and B- B-VQA [Singh Zheng (2023): Cho et al. (2023): Huang
et al. 2023), also rely on question- answer correctness to provide compositional faithfulness scores.
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3 Analysis of Optimization and Exploration Limitations
Bamike Deretiee
Her Utteaton
Seed 23
Feed IE4
Seed 201
Need ed4
Neel 353
grean beok
table
two printers and one computer
rectangstar
bi gertiia and small moue
girafic on the top af sirpiane
girafic
top of an airpiane
and microphonon
plastic cup and wooden knife
(a) optimization
(b) exploration
Figure 2: Limitations optimization (a) and exploration (b) when applied in isolation. Optimization often fails to capture attributes or relations despite refinement, while exploration struggles reliably recover all prompt elements even with multiple seeds.
To analyze the limitations of optimization and exploration in isolation, conducted controlled experiments with ReNO (Eyring et al. 2024) (optimization) and ImageSelect (Karthik et al. 2023) (exploration), both applied to Stable Diffusion Turbo and guided by a single reward function (ImageReward), A subset of T21-CompBench++ prompts was used to cover diverse compositional challenges.
Optimization--assed Methods. Optimization refines a single noise vector but often fails to reach correct compositions when initialization is poor. As shown in Figure 2a objects may be missing (rows 1 and 3, the blue book and the mouse are absent), attributes may be incorrect (row 2, the rug is generated with a rounded rather than rectangular shape), spatial relations may be wrong (row 4, the giralse appears in front of rather than on top of the airplane), and counts may be violated (row 5, the correct mumber of shrimp and microphones is not produced). These cases illustrate how optimization alone can stagnate or diverge, leaving key compositional constraints unsatisfied.
exploration-based Methods. Exploration samples multiple seeds and selects the highest-rewarder out- put, but due to the sparsity of well-aligned solutions in the noise space, many candidates remain incorrect. As illustrated in Figure 2b typical failures include missing objects (row 1, no blue cellphone; row 3, missing mouse), incorrect counts (row 2, two printers are generated but the required compuler is missing), violated spatial relations (row 4, giraffe misplaced in front of rather than on top of the airplane), and incorrect attributes (row 5, knife texture not faithfully captured). While cxploration improves diversity compared to optimization alone, random sampling without refinement rarely guarantees accurate compositional align- ment.
Overall, these findings highlight that optimization and exploration in isolation are insufficient for robust compositional alignment, motivating their integration in our unified framework.
4 CARINOX: Reward-Guided Initial Noise Optimization and Exploration
We introduce CARINOX, a framework that enhances compositional alignment in texl-to-image diffusion models through inference- time guidance. The approach integrates two key components: (i) a unified strategy that combines noise exploration with gradient- based noise optimization, and (ii) corrclation-driven selection of reward functions. This design enables CARINOX to more effectively navigate and refine the initial noise space, leading to generations that more reliably capture complex compositional specifications.
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Optimization
Exploration
hover berkgncc bina
I+
Initial
Modal
Fy
1² = 2
A A.C.ip(V, -)
Figure 3: Overview of the CARINOX framework. (a) Optimization: An initial noise refined through iterative updates guided by multiple reward functions, with per-reward gradient clipping and latent. regular- ization ensuring stable alignment with the prompt. (b) Exploration: Several noise candidates are sampled and independently optimized, and the final image is chosen via best-of-N selection, combining exploration diversity with optimization precision.
4.1 Unifying Initial Noise Optimization & Exploration
Improving compositional alignment at inference time can be approached in two ways. Noise optimization iteratively refines a single noise vector based on reward signals, while noise exploration samples multiple candidates to increase diversity the search space. Each strategy has clear strengths but also limitations, as discussed in Soction 3 In CARINOX, we combine these approaches into unified pipclinc: exploration broadens the search over initializations, and optimization refines each candidate under a fixed combination of compositional reward functions. The following subsections describe how these components are realized and integrated into our framework.
4.1.1 Gradient-Basse Initial Noise Optimization
We formulate the optimization of the initial noise vector e as a continuous search process aimed at improving alignment between generated images and textual prompts. Applying gradient-based optimization directly malli--sepp diffusion models is problematic because the gradient signal must pass through many sequential denoising steps, often leading to vanishing or exploding gradients and significantly increasing computational cost (Eyring al.] 2024). In contrast, single-step diffusion models generate the image in one forward pass, allowing gradients from the reward function to propagate cleanly and without degradation. This setup not only eliminates the instability caused by long gradient chains but also makes optimization more efficient, as each iteration requires only one denoising step (see Appendix B for details on single-step models). To further enhance stability and avoid drift into aut-of-distributian regions the latent space, we employ two safeguards: per-reward gradient clipping to prevent any single metric from dominating the update, and latent space regularization to keep e consistent with the model's prior. These properties make single-step diffusion ideal choice for our framework, enabling stable and efficient reward-guided refinement of e.
We aim to refine the initial noise vector e so that the final generated image aligns more closely with the textual description. The idea is to treat the noise set of optimizable parameters, updated iteratively in the direction that increases reward function measuring text-image alignment.
Noise Refinement via Gradient Ascent
Formally, given a prompt p and an initial noise sample e, the generative diffusion model Go maps them to an output image I in a single forward denoising step as / = GG(
The quality of this image is evaluated using a compasite reward function R(I.p), p). which aggregates several pre-selected reward metrics R.. Each metric measures different aspect of alignment, such as object cor-
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rectness, attribute binding, or spatial relationships. We sum these metric scores to form the optimization objective:
E" = arg max R(1.p). p):
(1)
R(I,P) Σ (T,)
(2)
where X are the fixed weights assigned to each reward function, set to 1 for all rewards in our implementation.
To adjust e, we compute the gradient the reward function with respect to the noise vector. This is achieved via the chain rule, first diflerentiating the reward with respect to the generated image and then propagaling this signal backward through the generative model to the noise space:
PR(L.))
01
V.R = 01
(3)
dE
Finally, we update the noise vector using gradient ascent:
((+1) = ( + V.R.
(4)
where 7 is the learning rate controlling the step size of the update. This iterative process moves the noise toward configurations that, when decoded by the model, yield images that more closely match the intended compositional structure described in the prompt.
Gradient Clipping with Mulki-Backward Optimization. Different reward components can produce gradients with vastly different magnitudes, which can destabilize the optimization one metric dominates the update direction. To address this, we adopt mulli-buckward optimization strategy, in which the gradient of each reward component is computed separately and clipped before aggregation. This ensures that all metrics contribute in a balanced way, regardless of their natural scale.
Formally, for each reward R,, the gradient with respect to the noise vector e is computed as:
RRi(I,P)
V.R.
dc
We then apply f2-norm gradient clipping with a maximum norm T = 0.01. If the gradient's fg-norm exceeds T, is rescaled proportionally so that:
Rvllll ST.
(6)
This prevents excessively large updates from any single reward component while preserving their relative direction. After clipping, the gradients from all rewards are aggregated into single update direction:
V.R ΣIV.R.
where X; are the fixed weights assigned to cach reward function, set to for all rewards in our implementation. We additionally report an ablation on adaptive reward weighting in Appendix F This procedure ensures that no single reward term overwhelms the optimization, allowing for stable and balanced gradient-based updates (see Appendix E for experimental analysis).
Regularization for Latent Space Consistency. During optimization, the noise vector can drift far from the distribution it was originally sampled from, namely the standard normal prior (o0, 7). If this happens, the denoiser may receive out-of-distribution inputs, which can degrade image quality and reduce alignment. To prevent such drift, we add a regularization term that encourages to remain statistically consistent with the prior distribution.
Following the approach of NAO (Samuel et al. 2024a and ReNO Eyring al. 2024), we do not simply enforce fixed norm constraint. Instead, maximize the log-likelihood of the noise vector's norm under
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the assumption that it follows x" distribution (the distribution of the norm of d-dimensional Gaussian vector). This leads to the regularization function:
K(e) = (d - log([E|]) ||E|
(8)
The final optimization objective combines the main reward function with this regularization term:
C =
(9)
where " controls the trade-off between maximizing reward and preserving distributional consistency. This rogularization constrains the search to noise vectors that are statistically consistent with the model's training distribution, avoiding drift into regions where the denoiser produces unreliable outputs.
4.1.2 Noise Exploration for Robust Initialization
Gradient-based optimization inherently sensitive to the quality of its starting point. If the initial noise vector lies in a region of the latent space that is poorly aligned with the prompt, the optimization process may converge to suboptimal solution or fail to capture the intended composition entirely. This sensitivity is especially problematic in reward landscapes that are highly non-convex, where poor initialization can trap the optimization in local optima.
We add a noise exploration stage that increases the diversity of starting points by drawing N candidates [e ~ (), Each candidate then refined independently with gradient- basod optimization, producing optimized vectors (ci,...,65 } from which the final output. selected.
Best-of-N Selection. After refinement, each optimized noise vector et is decoded by the generative model to produce images (I,....,NN) I,), where I, = Go p). The final output is selected as the image with the highest composite reward:
I" = arg max R p).
(10)
practice, we set as balance between efficiency and performance, with ablation results reported in Section
This selection strategy offers two key benefits. First, it introduces diversity through exploration, ensuring that even if some seeds start far from promising regions, others may lead to stronger alignments. Second, it complements this exploration with precision through oplimization, each seed undergoes gradient-based refinement before evaluation. Together, these aspects reduce sensitivity to suboptimal noise initializations and consistently yield high-quality, prompt-consistent results.
4.2 Gurield Reward Combination Selection
Reward functions serve as evaluators both optimization and exploration, determining whether noise ad- justments lead to genuine improvements in text-image alignment. By capturing aspects such as semantic accuracy, altribute binding, and spatial relations, they ensure that optimization emphasizes perceptually meaningful changes. Given their central role, reward models must be chosen carefully rather than by ad hoc or popular defaults.
To guide this choice, we conducted a systematic correlation study on the T21-CompBench++ dataset (Hangg
let al., 2023), which provides curated prompts, generated images, and human evaluation scores. We tested five embedding-
metrics (PickScore (Kirstain al. 2023), CL.ISScore (Hessel al. 2021). HPS (Wu et al.) 2023). ImageReward (Xu al.] (024),, BLIP-2 [Li et 2023a)), five based metrics (B-
VQA (Huang al. 2023). DA Score (Singh & Zheng
2023 TIFA (Hu al.] 2023 DSG Cho et. al.

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2023
VQA Score (Lin al. 2024b and two image-only metrics (CLIP-IQA (Wang et al. 2023 Aes-
thetie Score (Schuhmann et al., 2022 Spearman rank correlation was used to assess alignment with human preference annotations across compositional categories (Table 5
The study yielded several insights Kasaci et al. [2025). No single metric was consistently optimal: per- formance varied across attributes, spatial relations, and numeracy. CLIISeore, despite widespread use, never ranked among the top metrics, underscoring its weakness as standalone reward. VQA-based metrics showed strong compositional reasoning but were not uniformly superior across categories. Embedding baabeedding-ased based metrics such HPS and ImageReward frequently appeared among the top performers, showing correlations comparable to VQA-basodd scores. As expected, image-only metrics correlated poorly with human judgments.
To construct a reliable reward set, we applied a lop-3 frequency unalysis, counting how often each metric ranked among the three most human-aligned each category (Table 7 This identified HPS, ImageReward, DA Score, and VQA Score as the most consistently effective. We therefore fix this combination as the unified reward set for CARINOX, ensuring balanced coverage of both global semanlic aligument and fine- grained compositional accuracy. Complete correlation tables and per-category breakdowns are provided in Appendix
Model
Color
Shape
Texture
2D Spatial 1
3D Spatial
Numeracy
Non-Spatial
Complex
Mean 1
(1) SD-Turbo
0.47
0.37
0.45
0.48
0.39
0.45
0.59
0.46
0.46
(1) ReNO
0.63
0.59
(1) CARINO
0.69
0.66
0.78
0.71
0.61
0.63
0.76
0.51
0.67
(1) + CARINOX
0.78
0.74
0.89
0.80
0.65
0.71
0.80
0.62
0.75
(2) SDXI--Turb
0.61
0.61
0.75
0.60
0.69
0.44
0.71
0.47
0.62
(2) ReNO
0.70
CARINO
0.79
0.77
0.82
0.75
0.76
0.64
0.78
0.61
0.74
(2) + CARINOX
0.86
0.85
0.87
0.78
0.78
0.66
0.79
0.71
0.79
Table 1: Human evaluation on T2I- Conch showing that CARINOX achieves the highest align-
ment across categories and backbones. Best values are in bold, second-best are underlined.
5 Experiments & Results
5.1 Experimental Setup
We evaluate CARINOX through a series of experiments designed assess both compositional alignment and broader generation quality. Human evaluation (Section 5.2) provides direct judgments of alignment quality across multiple backbones. Automated evaluation on T21- Te1-CompBenchh+44 (Section 5.3 measures
performance across diverse compositional categories, while the HRS benchmark (Section 5.4 extends this analysis to higher-level aspects such as creativity, style, and visual writing.
For clarity, we compare three variants of our approach: CARINX, which applies our fixed reward combina- tion for best-of-N N exploration; CARINO, which performs initial noise optimization with our reward-guided pipeline; and CA CARINOX, the full method that integrates both exploration and optimization. Together, these benchmarks and variants provide comprehensive view of how our framework compares baselines and stale-of-the-art (SOTA) methods.
5.2 Human-Centered Assessment of Text-Image Alignment
We conducted human study to directly assess the effectiveness of proposed variants in improv- ing text-image alignment. A set of 200 prompts covering all cight compositional categorics of T2I- CompBench+++ was used with two backbones, SD-Turbo and SDXL-Turbo. For each prompt, human an- notators rated the generated images on a 0-3 scale, where scores reflected whether all objects were present, attributes were correctly rendered, and relations such as size, color, numeracy, and spatial layout were faith- fully captured. Scores were averaged across raters, normalized [0, 1]. and reported in Table Full details of the protocol are provided in Appendix G

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Table 1 reports the results. On SD-Turbo the baseline achieves mean score of 0.46, which rises to 0.62 with ReNO Our initial noise optimization variant, CARINO, further improves alignment to 0.67, while the full method, CARINOX, reaches 0.75. The largest margins appear in texture and 2D spatial categories, where CARINOX nearly doubles the baseline. On SDXL-Turbo, the mcan improves from 0.62 for the backbone to 0.69 with ReNO, 0.74 with CARINO, and 0.79 with CARINOX. Here, the si rongest gains occur in color and shape, with additional improvements 3D spatial reasoning and complex prompts.
Overall, these results confirm that both of our variants enhance human- perreiivd compositional alignment, with CARINOX consistently delivering the most substantial improvements across backbones.
Model
Color
Shape
Texture
2D Spatial f
3D Spatial
N Numeracy
Non-Spatial f Complex
Mean f
SD v1.4
0.3765
0.3576
0.4156
0.1246
0.3030
0.4461
0.3079
0.3050
0.3299
SD v2.1
0.5065
0:4221
0.4922
0.1341
0,3230
0.4579
0.3127
0.3356
0.3731
SDXL
0.5879
0.4687
0.5299
0.2133
0.3586
0,4963
0.31119
0.3237
0.4114
PixAAtt-r-ft
0.6690
0.1927
0.6177
0.20661
0.3991
0.5058
0.3197
0.3133
0.1168
DALLEE
0.7785
0.6206
0.7036
0.2866
0.3744
0.5880
0.3003
0.3773
0.5036
Strustured + SD v2.1
0,4990
0,4218
0.4900
0. 0.1386
0.1224
0,4550
0.3111
0,3355
0.3717
Attu-Exu Exut SD v2.1
0,6400
0.4517
0.5963
0.1455
0.3222
0.4550
0.3111
0.3355
0.4072
IniNNO + SD v2.1
0.7038
0:1691
0.5212
0,2022
0.3521
0,1892
0.3105
0.3571
0,4258
(1) SD-Tubbo
0.5252
0.4414
0.4888
0.1881
0.3112
0.4914
0.3095
0.3349
0.3866
(1) Pick A Pic
0.5871
0.48-12
0.5146
0.1501
0.3559
0,5157
0.3123
0.3768
0.4156
(1) + ImageSSllcct
0.6800
0.5172
0.3775
0.2317
0.3373
0.5027
0.3136
0.3725
0.4416
(1) ReNO
0.7800
0.6200
0.7500
0.2200
0,3800
0.5700
0.3200
0,1800
0.5150
(1) CARINX
0.7476
0.5661
0.6216
0.2366
0.3421
0.5295
0.3126
0.3841
0.4675
(1) CARINO
0,8519
0.7336
0.8013
0.21337
0.3920
0.5903
0.32669
0,19016
01.5542
(1) + CARINOX
0.8633
0.7609
0.8229
0.2588
0.4155
0.6248
0.3372
0.5041
0.57334
(2) STXXI-Turbo
0.5059
0.4038
0.5472
0,2303
0.3612
0.4863
0.3114
0.3430
0.4009
(2) Pick A Pie
0.6532
0.4803
0.6176
0.2679
0.3959
0.5492
0.3122
0.3741
0.4563
(2) ImageSelect
0.7389
0.5257
0.6590
0,2426
0,3838
0.5398
0.3115
0.38812
0.4724
(2) ReNO
0,7800
0,6000
0.740D
0,2600
0,3901
0,5600
0.3100
0,4700
0.5137
(2) CARINX
0.7890
0.5708
0.7068
0.2663
0.3984
0.5423
0.3129
0.3932
0.4975
(2) CARINO
0.8492
0.7203
0.7977
0,2858
0.4069
0.5835
0.3141
0.4859
0.5554
+ CARINOX
0.8697
0.7482
0,8270
0.3010
0.1117
0.3992
0.3232
0,1922
0.3715
33) PixArt-u DMD
0.1145
0.3187
0.3667
0. 0:2213
0.31-11
0.1896
0.3061
0.3:66
0.3547
(3) Pick A Pie
0.4475
0.3690
0.4658
0. 1987
0.3704
0.5393
0.3082
0.3555
0.3818
(3) ImaggeSeleet
0.5361
0.4406
0.5148
0.1878
0.1747
0.5453
0.3091
0.3634
0.40900
(3) ReNO
0.6400
0.5700
0.7200
0.2500
0.3900
0.5600
0.31:00
0.4600
0.4875
33) CARINX
0.5966
0.1855
0.5643
0.2348
0.3697
0.5-663
0.3100
0.3806
0.4360
CARINO
0.8260
0.7528
0.7967
0,2620
0.4031
0.6144
0.31.46
0.4782
0.5560
CARINOX
0.8545
0.7721
0.8076
0.3272
0.4164
0.6295
0.3256
0.4878
0. 0.5776
Table 2: Quantitative evaluation T21- 121-CompBench++ across eight compositional categories using three different backbones. Results are reported for baseline models, SOTA methods, and our variants. CARINOX achieves the strongest overall alignment, consistently surpassing both optimization- and exploration-based baselines. Best values are in bold, and second-best. are underlined.
5.3 Category-Level Compositional Benchmarking - Tol-ChmmBench++++
We further benchmark our approach against a broad set of SOTA methods on T21-CompBench-ee which evaluates eight compositional categories using specialized evaluators for each dimension. Since different met- rics are used across categories (e.g., CLIP for non-spatial relations), the absolute ranges vary, but the compar- isons remain consistent across methods. Our baselines cover multiple methodological families: (i) multi-step open-source diffusion backbones (SD v1.4, SD v2.1, SDXL, PixArt-a), (ii) single-step backbones (SD-Turbo, SDXL-Turbo, PixArt-a DMD), (iii) recent commercial systems such as DALL E 3, (iv) attention-bassed inference-time control methods (Structured Diffusion Feng et al (2023b) Attend-and lirend-andd-Ed -nd-tx Chefeer et al.
(2023) ), (v) gradient-based initial noise optimization approaches (InitNO
Guo et al. (2024b), RcNO Eyring 2023), ImageSelect Karthik et al. 2023 We report results for three variants of our method: CARINX (exploration only), CARINO (opti-
et al. 2024t), ), and (vi) exploration strategies (Pick-a-Pic Kirstain et al.
mization only), and CARINOX (combined).
As summarized in Table 2 CARINOX consistently achieves the highest overall performance across all three backbones. On SD-Turbo, it improves the mean score from 0.39 to 0.57, outperforming ReNO (0.52) and
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significantly surpassing exploration-basse methods such as Pick-a-Pic (0.42) and ImageSelect (0.44). The gains are most pronounced in texture and numeracy, while stable improvements are also observed in 2D and 3D spatial reasoning, On SDXL-Turbo, CARINOX raises the mean from 0.41 to 0.57, again outperforming all basclines, with particularly strong results in texture and complex categories. Finally, on PixArt-a: CARINOX achieves the highest mean score of 0.58, with notable advantages 2D spatial (0.33 vs. 0.22 for the backbone) and numeracy (0.63 vs. 0.49).
Importantly, CARINO and CARINX also provide consistent improvements when used independently: CAR- INX surpasses existing exploration methods, while CARINO establishes optimization. Together, the full CARINOX pipeline further amplifies these gains, outperforming commercial systems such as DALL 3 and advancing the SOTA across compositional categories.
strong baseline for noise
Model
Creativity
Style
Size 1
Visual Writing
Mean f
SD-Turbo
0.4914
0.2370
0.2118
0.1890
0.2823
Pic
0.4950
0.1974
(1) ImageSelect
0.5319
0.2831
0.2483
0.1872
0.3127
(I) ReNO
0.5333
0.3407
0.2614
0.2838
0.3548
0.5354
0.3033
0.2025
0.3249
CARINO
0.6105
0.4647
0.2634
0.3739
0.4281
(1) CARINOX
0.6216
0.4975
0.3006
031329
0.1639
SDXI-Turbo
0.5093
0.2526
0.2443
0,2589
0.3158
Pick A Pic
0.5154
0.2810
0.2620
0.2965
0.3387
(2) ImageSelect
0.5298
0.3106
0. 0,2696
0.3074
0.3544
(2) ReNO
0.5451
0.3091
0. 0.2567
0.3273
0.3746
CARINX
0.5326
0.3406
0. 0.2725
0.3215
0.3668
(2) CARINO
0.5913
0.4502
0.2704
0.3080
0.4275
(2) CARINOX
0.6248
0.4907
0.3070
0. 0.4699
0.4731
0.4775
0.2552
0.1677
0.2535
+ Pick Á Pie
0.5150
0.2749
0.1967
0.1265
0.2783
+ SmlegtSeloct
0.5026
0.2532
0. 1.1958
0.1412
0.2814
+ ReNO
0.1982
CARINX
0.5289
0. 0.2838
0.1993
0.1377
0.2949
+ CARINO
0.6431
0.5076
0. 0,2565
0.4112
0.45-46
+ CARINOX
Table 3: Evaluation on the HRS benchmark across three backbones and variants of our exploration and optimization methods. CARINOX achieves the strongest overall performance, with best scores in bold and second-best underlined.
5.4 Beyond Compositionality Expressive Evaluation HRS
The HRS benchmark extends evaluation beyond strict compositionality to creativity, artistic style, object size, and visual writing. These dimensions test whether a model can balance alignment with expressiveness and stylistic fidelity.
Table 3 shows that CARINOX consistently outperforms both the backbones and competing methods. On SD-Turbo, it raises the mean score from 0.28 0.46, mainly through large gains in crcativity and visual writing, where baseline models are especially weak. On SDXL-Turbo, CARINOX delivers the strongest overall balance, improving all four dimensions simultaneously and setting new best results in size and visual writing. On PixArt-a, it again achieves the top mean (0.48), driven by clear advantages in creativity and style while also improving visual writing.
These results demonstrate that CARINOX is not only effective at resolving compositional failures but also en- hances higher-level aspects of generation such as artistic quality and written content. Importantly, CARINO and CARINX each provide gains on their own, but their integration in CARINOX consistently produces the most robust. improvements.
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5.5 Qualitative Results
Figures and illustrate that baseline models (SD2.1, TnitNO, SD-Turbo, SDXI-TTubo) often miss core compositional requirements, and while ReNO improves alignment, it still produces frequent errors. In con- trast, CARINOX consistently generates images that better match prompls across diverse settings: on T21- +ompBench++ it respects relative sizes, attributes, and counts (e.g., "a dog smaller than chair, "four lamps and four dogs"), while on HRS it produces clearer text rendering, coherent styles, and more expressive compositions. These results highlight the robustness of CARINOX over both baselines and ReNO.
SD2.1
IniINO
SD-Turbo
ReNO
CARINOX
SDXL-Turbb
ReNO
CARINOX
dog and chair, deg smaller than the chair.
FLYFIY
sign written fly!
FLV
LIY
WIY
FLY FY VEP TN
+ FLLY... FLLY
FLYY! ! FLY.
a banana and cat, banana bigger than the cat.
Figure 4: Qualitative results on the HRS benchmark, where CARINOX produces coherent, visually expres- sive outputs with accurate style and text rendering.
6 Ablation
6.1 Effect of Iterations and Seeds
Figure 5 presents the ablation study on the number of optimization iterations and seeds. Increasing the number of iterations improves alignment scores consistently up to about 50, after which the gains plateau and in some categories even decline slightly. Similarly, increasing the number of seeds enhances performance by enlarging the exploration space, but the benefit saturates beyond roughly 5 seeds.
Based on these results, we set the default configuration of CARINOX to 50 optimization iterations and 5 seeds. This choice provides an effective balance between computational efficiency and alignment performance, capturing most the achievable improvement without incurring unnecessary cost.
6.2 Evaluation of Individual Reward Functions
To better understand the contribution of each reward function, we analyze the effect of applying them individually within our noise optimization pipeline using SD-Turbo on T2T-CompBench+ We consider four reward models: HPS, ImageReward, DA Score, and VQA Score. Each component applied separately to guide optimization, allowing us to assess its influence different compositional categories.
The results in Table A indicate that the rewards complement cach other rather than excelling universally. DA Score achieves solid improvements in color and shape but is less consistent across other categories. ImageReward provides balanced gains, performing well in texture and spatial reasoning. HPS particularly effective numeracy and 2D spatial categories, while VQA Score contributes moderately but lags behind the other metrics in overall mean performance. Crucially, integrating all four into CARINO yields the highest mean score (0.55), 0.15 improvement over the SD-Turbo baseline and above any single reward.
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Ablation Study Number iterations
Ablation Study Number Seeds
Canegories
Categories
0.45
5000
0.45
0.30
0.15
0.15
1N1T
110
120
:40
150
150
170 190
1100
Seads
(a)
(b)
Figure 5: Effect of optimization iterations (a) and exploration soeds (b) on T2l-CompBench++ Performance improves with more iterations and seeds but saturates beyond 50 iterations and seeds, motivating their CARINOX defaults for balanced efficiency and alignment.
These results confirm that no individual reward is sufficient on its own, and that strategically combining complementary signals leads to stronger and more reliable alignment improvements across compositional
challenges.
Method
Color
Shape
Texture
2D Spatial
3D Spatial
Numeracy
Non-Spatial
Complex
Mean
SD-Turbo
0.35
0.44
0.57
0.17
0.30
0.49
0130
0.41
0.40
+ HPS
0.69
0.60
0.71
0.27
0.40
0.61
0.30
0.11
0.50
+ ImageReward
0.80
0.63
0.72
0.20
0.39
0.60
0.31
0.11
DA Score
0.86
0.81
0.79
0.23
0.31
0.53
0.30
0.45
0.53
+ VQA Score
0.70
0.53
0.67
0.21
0.35
0.59
0.30
0.40
0.17
+ CARINO
0.85
0.73
0.80
0.24
0.39
0.59
0.33
0.49
0.55
Table 4: Effect of individual reward components on SD-Turbo evaluated on ToI-CompBench+ Single rewards might get category-specific gains, while the full CARINO combination achieves the best overall mean and the most balanced improvements across categories.
7 Conclusion
We presented CARINOX, an inference-time framework that unifies initial noise exploration with gradient- based optimization to improve compositional text-to-image generation. By refining multiple seeds in parallel and selecting the best candidate through a correlation-guider reward combination, CARINOX effectively balances diversity with precision. The framework incorporates gradient clipping to prevent reward dominance and latent regularization to maintain distributional consistency, enabling stable refinement without sacri- ficing realism. Extensive experiments on T2I- Pe21-CompBench+44 and HRS demonstrate that CARINOX consistently outperforms baselines and prior inference-time approaches, achieving more reliable composi- tional alignment and higher perceptual quality. These results underscore the potential of optimizing initial noise scalable path toward robust inference-ting time scaling for diffusion models.
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Appendix
A Future Work
While CARINOX demonstrates strong improvements in compositional alignment, several directions remain open for exploration.
First, although we focused on a carefully selected set of reward functions, future work may incorporate richer or domain-specific reward models, inchuding those trained on human preference datasets beyond composi- tional aligument, or multimodal evaluators capable of handling more abstract properties such as style and creativity.
Second, CARINOX currently applies reward feedback within one-step generative backbone. Extending the framework to mulli-slep diffussio models would allow gradients to propagate across the full denoising trajectory, potentially unlocking finer-grained control over alignment.
Finally, we envision combining advanced reward models with our asploration-optimizatti pipeline a more general reinforeement learning-slpe style framework, where both reward definitions and update strategies co-evolve to optimize compositional alignment. Together, these directions could make CARINOX not only more robust but also more broadly applicable to future generations of text-lo-image systems.
Preliminaries: One-Step Diffusion Models
Diffusion models have become a fundamental approach for text-to-image (T2)) generation, leveraging a stochastic denoising process to progressively refine an initial noise sample into a coherent image (Ho et al. 2020 Rombach et al. 2022). Given a textual prompt p. a diffusion based generative model Gg, parameterized by 0, synthesizes image Xo by starting from sampled noise 2o -N(0, T) and applying learned transformation such that:
Go p) Xo-
(11)
The goal of training is to optimize 0 such that the generated image xo semantically aligned with p.
B.1 From Multi-Step to One-Step Diffusion
Standard diffusion models follow a multi-step denoising process, where an image at timestep follows the stochastic transition:
(12) where 0g and o, are time-dependent scaling factors such that a, decreases while a, increases over time. The reverse process reconstructs Xo by progressively removing noise through a learned score function. However, this stepwise reconstruction makes inference computationally expensive.
= 0g + o.Zo. E [O.
To mitigate this, one-step diffusion models aim to approximate the full denoising trajectory in a single function evaluation by learning direct mapping from the initial noise the final image:
Xo = So p).
(13)
This transformation climinates the need for iterative refinement, significantly reducing inference time while maintaining generative quality.
B.2 Training and Optimization
One-step diffusion models are typically trained by distilling the multi-step diffusion process into single-step model. This involves minimizing reconstruction loss that ensures fo approximates the multi-step generative process:
(())
Exx (xx()) a.0 (00.1) [I]fo(zo. p) xol1?].
(14)
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This objective encourages fo to reconstruct high-quality images directly from noise while preserving the semantic content dictated by p.
One-step diffiusin models provide an efficient framework for direct noise optimization. By enabling gradient- based refinements of Zo, they serve as the foundation for our proposed reward- driven initial noise optimization framework, which is described the following section.
C Correlation Study of Evaluation Metrics
C.1 Evaluation Metrics
A range of metrics have been proposed for evaluating text-image alignment, each targeting different aspects of the correspondence. They can be grouped into three categories: (1) smbedding-busee which rely on rep- resentations or preference models; (2) pontent-based, which use structured reasoning to assess compositional
properties; and (3) image-only, which measure perceptual quality independently of text.
Metric
Color
Shape
Texture
2D Spatial
Non-Spatial
Camplex
3D Spatial
Numeracy
CLIP [Hessel et sl.
2021
0.282
0.291
0.535
0.389
0.439
0.276
0:315
0.223
PickScone (Kisstan
al. 2023
0.263
0.270
0.516
0.299
0.432
0.167
0.139
0.337
HPS [Wu et al] 2123
0.219
0.410
0.601
0.410
0.535
0,270
0.416
0.171
Image Reward
2024
0.580
0.520
0.734
0.394
0.512
0.424
0.401
BLIP2 [L
2023g
0.250
0.287
0.546
0.369
0.353
0.235
0.416
0.366
Aesthetic Schuhmann
2022
0.056
0.195
0.078
0.136
0.061
0.051
0.128
0.036
CLIP-IQA (Wang 2023
2125
0,092
0.078
-0,001
0.088
0.082
0.027
0.098
0.068
B-VQA [Huang et al.
0.610
0.388
0.690
0.255
0.371
0.372
0.330
0.444
DA Score (Siggh & Zhong
2023
0.772
0.463
0.711
0.318
0.153
0.488
0.297
0.462
TIFA Hu al. 2023
0.684
0.336
0.423
0,311
0.351
0.519
0.195
0.526
DSG el al 2023
0.599
0.388
0.628
0.328
0.470
0.411
0.427
0.409
VQA Score Lin al. 2024b
0.678
0.405
0.701
0.495
0.339
Table 5: Spearman correlation evaluation metrics with human scores across compositional categories on + The highest value each category is shown in bold, and the second-highest is underlined.
Metric
Color
Shape
Texture
2D Spatial
Non-Spatial
Complex
3D Spatial
Numeracy
CLIP [Hessel
2021
0.208
0.211
0.392
0.287
0.341
0.201
0.221
0.151
PickSeore Kirrsagn
2023
0.193
0.192
01373
0.229
0.341
0.122
0.100
0.241
IPS Wir ot al 2123
0.157
0.326
0.441
0.315
0.428
0.201
0.305
0.346
Image Heward Xn el al.
2024
0.434
0.388
0.549
0.310
(14088
0.313
0.294
0.349
BLIP2 [i ct al. 2023g
0.179
0,203
0.389
0,286
0,290
0.170
0.303
0.261
Aesthetic Schmhmann et
2022
0.039
0.138
0.054
0.104
0.047
0.037
0.083
0.026
CLIP-IQA WWang al. 20255
0.065
0.055
-0.002
0.068
0.063
0.018
0.068
0.045
B-VQA [Tnang ot al.
0.156
0.279
0.512
0.195
0.293
0.267
0.231
0.322
DA Score (Sing Zhong
0.603
0.337
0.531
0.217
0.357
0.364
0.206
0.317
TIFA [1u
2023
0.559
0.246
0.329
0.286
0.292
0.405
0.155
0.400
DSG Clu el
2123
0.499
0.303
0.503
0.292
0.408
0.325
0.355
0.363
VQA Score Lin et al.
2024b
0.512
0.292
0.516
0.422
0.390
0.481
0.213
0.352
Table 6: Kendall's correlation of evaluation metrics with human scores across compositional categorics Bol-ChmpBench++ The highest value in each category is shown in bold, and the second-highest is underlined.
Eabedding-based Metrics Embedding based metrics evaluate alignment by comparing text-im text-image rep- resentations in shared multimodal space by leveraging models trained on human preferences. A common bascline is CLIPScore HHssel et al.} 2021). which measures cosine similarity between CLIP embeddings. Preference-supervisee variants include HPS (Wu et al.) 2023). which fine-tunes CLIP on human compar- isons, and PickScore [Kirstain et al.) 2023). which learns from pairwise preference judgments. BLIP (Li ct al. 2023al follows the enbedding-ssmilaric approach, comparing captions generated from images with the input text. Extending this idea, ImageReward (Xu et al., (2024) adds reward head trained on ranked human preference data, capturing both textual relevance and perceptual quality.
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Metric
Color
Shape
Texture
2D Spatial
Non-Spatial
Complex
3D Spatial
Numeracy
Total
CLIP (Hesee) et al.
2021
PickScore [Krstain et al.
2022
D
HPS [Wu 2023
ImageReward
2024
6
BLIP2
Li al.
2023a
1
Aesthotic
Schaloramm ot al. 2022
0
CLIP-IQA
Wang es al
2023
0
B- B-VQA
Huang et al
2023
0
DA Score
Singh & Zheng 2023
1
TIFA
Hu
3
DSG
Cho
2023
VQA Score
Lin
2021b
6
Table 7: Top-3 presence of cach metric across various compositional categories. A v indicates the metric is among the lop 3 in that calegory. The last column shows the total number of calegories where the metric appears in the top 3 based on spearman correlation.
Content-based (VQA-based) Metrics VQA-based metrics assess compositional aligument by casting text- text-image consistency as a question answering task. Questions derived from the prompt are posed to a pretrained VQA model, with scores based on the correctness its responses. VQAScore (Lin et al. 2024b) generates yes/no nn questions from the text, while TIFA (Hu et al.) 2023) uses structured templates to cover objects, attributes, and relations. Variants target specific aspects: DA Score (Singh & Zheng 2023) asks entity attribute questions to test binding, DSG (Cho al. 2023) converts the text into scene graph verify entitics and relations, and B- BQA (Huang et al.; 2023) decomposes the text into abject-attribute pairs, querying each with BLIP-VQA and combining the probabilities.
Image-only Metrics Image-only metrics assess perceptual quality independently of the prompt, providing complementary signals of realism and aesthetics. CLIP-IQA (Wang et al. 2023) predicts image quality by regressing CLIP embeddings against human quality annotations, while the Aesthetic Score (Schuhmann ct al. 2022 estimates aesthetic value from large-scale human ratings.
C.2
Experimental Setting
Our analysis is based on 121-CompBench++1 (Huang et al. 2025), which provides curated prompts across attributes (color, shape, texture), spatial relations (2D and 3D), non-spatial relations, complex prompts, and numeracy. Each prompt is paired with images from multiple lext-to-image models and annotated with human evaluation scores. All resources (prompts, images, and scores) come from the benchmark; contribution is to analyze how evaluation metrics align with these annotations using outputs from SD v1.4,
SD v2, Structured Diffusion (Feng et al.) 2023b). Composable Diffusion [Luu ct al.
2022 Attend-and-Excii
Chefer et al. 2023 and GORS (Huang et al.
2023
We evaluate five embedding based metrics (PickScore Kirstain et al. 2023). CLIPScore (Hessel et al.
202]
HPS Wu et al. 2023), ImageReward (Xu et al.) 2024). BLIP-2 (Li al. 2023a two image-only metries (CLIP-IQA (Wang et al. 2023). Aesthetic Score (Schuhmann et al., 2022)), ), and five VQA-based metrics
(B-VQA (Haang et al. 2023 DA Score (Singh & Zheng 2023), TIFA (Hu et al. 2023 DSG (Cho et all, 2023), VQA Score (Lin et al. 2024b) ), covering embedding similarity, perceptual quality, and VQA-style reasoning.
C.3 Correlation Analysis of Evaluation Metrics
We assess the reliability of reward models by correlating their scores on T2I- T2I-CompBench++4 generations with human evaluations (Section C.2). Spearman correlations, reported in Table 5 serve as the main measure, while Kendall's results provided in Table for completeness.
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Per-Categan Careoryy Breakdown of Correlation Results Table 5 highlights that the strongest correlations differ substantially across categories, indicating that no single metric dominales overall. In the attribute group, DA Score leads on color (TIFA second), while ImageReward ranks highest on shape and texture (DA Score second). For relational cascs, VQA Score performs best on 2D spatial (HPS second), whereas DSG leads in 3D spatial (HPS and BLIP-2 second). Non-spatial relations are best captured by HPS, followed by IewaeReward. In complex prompts, VQA Score shows the strongest alignment, with TIFA socond, and numeracy, TIFA ranks first with ImageReward next. Across all categories, image-only metrics (CLIP-IQA, Aesthetic) remain consistently weak, underscoring their limited value for compositional alignment.
Broader Insights on Metric Performance Several broader insights emerge from these results. First, no single metric achieves strong and consistent correlation across all compositional categories, indicating that
reliance on a single signal insufficient. Sccond, despite its widespread use (Rombach al. 2022 Nichol
et al. 2021 Ruiz et al. 2023 Brooks et 2023 Kumari al.) 2023: Kang al., 2023a Chefer et al,
2023 Podell et al.) 2023; Chen al. 2023 Li et al. 2023b Nguyen & Tran 2024), CLIP never ranks among the top metrics, underscoring its limitations a standalone measure. Third, embedding based metrics, particularly ImageReward and HPS, frequently appear among the strongest. Fourth, while VQA-based metries are competitive, they are not uniformly superior and are occasionally outperformed by embedding- based approaches. Finally, image-only metrics such as CLIP-IQA and Acsthetic remain consistently weak, as expected since they do not assess text image aligument.
Iteration 10
Iteration 30
Iteration 50
Swee 42
Spee
Seed
Figure 6: Iterative refinement for the prompt "a train on the bottom of a horse." Five different seeds are optimized in parallel, and by iteration 50, outputs converge toward coherent compositions. The best image is then selected using our reward scores.
D Results of Iterative Noise Refinement
Figure ( illustrates how the proposed noise refinement progressively improves alignment between the gen- crated images and the input prompt. Starting from diverse initial sceds, the early itcrations often produce incomplete or ambiguous compositions. As optimization advances, the structure of the scene becomes clearer: the horse and train appear more consistently, their spatial relations stabilize, and extraneous artifacts are
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reduced. By iteration 50, the outputs across different seeds converge toward coherent and faithful realizations of the prompt, while still preserving diversity in style and background.
In practice, the framework generates multiple refined candidates in parallel and selects the best image using our reward combination. This best-of-N selection ensures that the final output not only reflects consistent alignment but also represents the strongest candidate among diverse refinements.
E Effect of Multi-Clip on Multi-Backward Optimization
In our oplimization pipeline, each reward metric contribules gradients that guide noise refinement. However, the magnitudes of these gradients can vary drastically. Without proper regulation, a dominant reward can overpower the others, pushing the optimization toward solutions that satisfy alignment objectives but sacrifice realism. To address this, we apply Multi-Clip: a mechauism that clips the gradient of each reward independently before aggregation, ensuring balanced updates across all metrics.
Iter 0
Iter10
Iter30
Iter50
CARINOX wooe
black dog and a brown cat
CARINOX
CARINOX wib
red apple and i green kivi
CARINOX
CARINOX WHO WIO GC
bilre car and red cup
CARINOX
Figure 7: Effect of Multi-Clip on Multi-Backward Optimization. Without gradient clipping (top), dominant rewards distort updates: in "black dog and brown cat" the animals appear waxy and anatomically implausible, and in "red apple and green kiwi" the fruit exhibits unnatural texture, shading, and saturation. With Multi-Clip (bottom), each reward is balanced, preventing distributional drift and producing outputs that are both compositionally faithful and photo-realistie.
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Figure 7 highlights the consequences of omitting this step. In the first example, the prompt "a black dog and brown cat"" leads to strong aligmment of color and entities, but without clipping, the outputs drift toward unrealistic, wary, and anatomically implausible animals. In the second case, the prompt "a red apple and a green kiwi" suffers a similar failure: although the objects and colors are correct, the fruit becomes highly unnatural in testure, shading, and saburation, far from realistic depictions. The third example with "a blue car and a red cup" shows the same pattern-object are recognizable but appear distorted or cartoonish.
With Multi-Clip enabled, these issues resolved. Each reward's gradient is scaled to contribute com- parably, which stabilizes optimization, prevents distributional drift, and yields outputs that are both com- positionally correct and visually realistic. In practice, this mechanism is essential for maintaining balance between aligument fidelity and photo-realism, ensuring robust improvements across diverse prompts.
To provide a quantitative view of why Multi-Clip is needed, Table g reports both the mean and standard deviation of the per-reward gradient norms across iterations. Two patterns are consistent: (i) the scales remain mismalched throughout the trajectory (e.g., VQA stays the largest even at the end: Iter 50 mean 4.03 vs. HPS 0.22 and DA 0.16), so aggregation can remain dominated by subset of rewards beyond the first few steps; and (ii) the standard deviations are large especially early (e.g., Iter 1: VQA std 112. 12, IewgeReward std indd)aiindi high variability and occasional extreme gradients that can abruptly steer the update direction. Multi-Clip addresses both by clipping each reward gradient independently before aggregation, preventing persistent or sporadic domination by any single reward.
VQA Score
Image-Rewrrd
HPS
DA Score
Iter
Mean
Std
Mean
Std
Mean
Std
Mean
Std
39.09
112.12
11.52
72.67
2.37
3.36
6.27
15.19
15.25
71.11
2.79
11.84
0.77
1.08
1.37
3.28
20
8.70
31.43
1.36
4.71
0.45
0.57
0.64
1.70
30
6.36
30.54
0.82
5.85
0.32
0.39
0.33
0.92
40
4.85
14.26
0.51
1.77
0.26
0.29
0.21
0.54
4.03
10.64
0.39
1.89
0.22
0.29
0.16
0.53
Table 8: Mean and standard deviation of per-reward gradient norms iterations (averaged over prompts/seods used in the Multi-Clip analysis).
Reee at selected optimization
F Adaptive Reward Weighting (Ablation)
We include the following experiment as an ablation to assess whether replacing uniform reward weights (A:=1) with category-aware correlation-based weights improves optimization.
While a fixed combination of reward models provides stable optimization framework, evaluate variant that assigns weights to each reward function based on its measured correlation with human preferences within each T21-CompBench++ compositional category. The weight for cach reward R, is computed as:
4ps
Wi Σsps"
(15)
where Pr denotes the correlation coefficient of R, with human evaluations for the corresponding category (Table p.. The weights are normalized to sum to 4 to keep the overall reward scale comparable the uniform- weight setting. In this ablation, we use the ground-truth category labels provided by T22l-CompBench-4 to select the sppegory-specific weights.
Table g shows that adaptive weighting yiclds only marginal changes relative to uniform weighting. On SD-Turbo, AW slightly improves the mean score (0.5542 + 0.5595), while on SDXI-Turbo it is essentially unchanged (0.5554 + 0.5539), and on PixArt-a DMD it is again marginal (0.5560 - 0.5582). Overall, these results suggest that the uniform weighting used in the main method is already a strong and robust default, and that correlation-based category-awars reweighting provides limited additional benefit under our current setup.
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Model
Color 1
Shape
Texture
2D Spatial 1
3D Spatial f
Numeracy 1
Non-Spatial f
Complex
Mean f
(1) SD-Tubo
0.5252
0.4434
0.4888
0.1881
0.3112
0.4914
0.3005
0.3349
0.3806
(1) CARINO
0.8519
0.7336
0.5043
0.2437
0.3920
0.5903
0.3269
0.4906
0.5542
(1) CARINO (AW)
0,8608
0.7430
0.8060
0.2411
0.4083
0.6836
0.3239
0.4896
0.5595
(2) SDXL, Turbo
SuXIoTTubo
0.3939
0,4038
0.5172
0,2303
0.3612
0.4563
0.3114
0,3130
0.4099
CARINO
0.8492
0.7203
0.7977
0.2858
0:4089
0.5835
0.3141
0,4859
0.5554
(3 CARINO (AW)
0.8437
0.7298
0,8042
0.2767
0.3989
0.5848
0.3138
0.4877
0.5639
PizArt-a DMD
0.4145
0.3487
0.3667
0.2213
0.3441
0.4896
0.3061
0.3486
0.3547
CARINO
0.7528
0.7967
0.2620
0,0031
0.6144
0.31.46
0,4782
0.5560
33 CARINO (AW)
0.8215
0.7662
0.8016
0.2770
0.4030
0,6008
0.3155
0.4813
0.5682
Table 9: Adaptive reward weighting (AW) ablation 121-CompBench+++ AW replaces uniform weights
with category-specific correlation-bast based weights, using the benchmark's ground-truth category labels to select weights for each prompt.
G Human Evaluation Protocol
To assess alignment quality from a human perspective, we designed four-level scoring scheme ranging from to 3:
Score O: None of the objects described in the prompt are generated.
Score 1: At least one object is present, but others are missing, severely deformed, or incorrectly generated.
Score All objects described in the prompt are present and recognizable, but attributes or relations (e.g., color, size, spatial layout, numeracy) may be incorrect incomplete.
Score 3:
The image is fully consistent with the prompt: all objects present, correctly rendered, and the specified attributes and relations are faithfully captured.
Seven annotators participated in the study, including four undorgraduate and three master's students. Each annotator provided with written instructions and example images corresponding to each score level to establish consistent evaluation standard. The prompts were sampled from all eight compositional categories of T21- Boncch+ and for each prompt, images from different methods were collected.
To avoid bias, images were presented randomized order, with no information about which method or backbone produced them. Each annotator independently rated every image, ensuring multiple judgments per sample. The raw scores were then averaged across raters and normalized to the range [o, 1] for reporting in the main paper. This protocol ensures both fairness and robustness of the human evaluation results.
Quality and Diversity Evaluation
Image quality and diversity remain central aspects of text-to-image iooo--mmgeeaaaa generation, alongside compositional alignment. We therefore report. Fréchet Inception Distance (FID) (FID))Heusel (Heusel al., (017]) Density, and Cover- age(Naeem et al. 2020) on the MS-COCO dataset (Lin et al. 2014). FID captures distributional distance from real images (lower is better), while Density and Coverage mcasure fidelity and diversity relative to the real distribution (higher is better).
Table [0 shows that CARINOX achieves competitive results on all three measures while providing substan- tial compositional improvements. In particular, Density and Coverage remain strong, confirming that the optimization framework preserves both realism and diversity of outputs. These results demonstrate that CARINOX delivers enhanced alignment without compromising overall generation quality.
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Model
FID (4)
Density (1)
Coverage (1)
SD v2.1
10.31
0.92
0.88
Attn-Exet
10.35
0.91
0.88
InitNO
7.38
0.93
0.91
SD-Tubbo
8.09
0.70
0.99
ReNO
10.12
0.97
0.99
CARINOX
12.93
0.91
0.97
Table 10: Quantilative comparison of quality and diversity between our proposed approach, CARINOX, and ReNO over the MS-COCO dataset. Lower FID values indicate better realism, while higher Density and Coverage values suggest better fidelity and divensity, respectively. While CARINOX provides a significant improvement compositional generation, the degradation in quality and diversity is minimal.
Time and Memory Usage Analysis
1.1 Measured Runtime and VRAM
We evaluate the computational efficiency of CARINOX by measuring runtime and VRAM usage on three backbones: PixArt-a, SD-Turbo, and SDXL-Turbo. All measurements are obtained on an NVIDIA H100 GPU.
Model
VRAM (GB)
Time (s)
SD-Turbo
ReNO
20
(1) CARINOX
33
60)
(2) SDXboTurrbo
0.25
ReNO
CARINOX
(3) PixArt-o DMD
21
0.12
(3) + ReNO
25
25
CARINOX
Table 11: Comparison computation time and VRAM usage CARINOX and ReNO over three different
backbones.
As shown in Table [1 both CARINOX and ReNO introduce additional overhead compared to the raw backbones, but differ in their requirements due to the complexity of their optimization pipelines. CARINOX incurs higher runtime and VRAM usage than ReNO due to its multi-reward optimization and best-of-N exploration. For example, on SD-Turbo, VRAM increases from 15 GB (ReNO) 33 GB (CARINOX), and runtime increases from 20s 60s. Similar trends hold on PixArt-a and SDXL-Turbo.
The additional overhead in CARINOX stems from two factors: (i) malti-reward optimization, where each iteration evaluates multiple reward models and backpropagates their gradients, and (ii) best-of-N explo- ration, where optimization is repeated independently for multiple noise seeds and the best result is selected. In particular, some of our rewards rely on heavier vision-language or VQA-style models, which are more demanding in both VRAM and runtime; using them jointly amplifies the per-iteration cost. Importantly, CARINOX modular with respect both components: future work provides reward models with lower memory Sarooryylateee /latency comparable quality reduced cost, our framework can directly benefit. by swapping in these scorers without changing the optimization procedure. Similarly, advances in seed exploration (e.g., more sample-efficient initialization search that achieves the same best-of-N gains with fewer candidates) would reduce the number of optimized seeds required, lowering total runtime proportionally.
While CARINOX is more resource- -nturceeittessvee, the demands remain within the range of modern GPUs and are justified by the substantial performance gains achieved across benchmarks. This analysis illustrates the trade-off between computational cost and alignment quality, highlighting the importance of efficient reward integration in inference-time optimization.
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1.2 NFE-Matched Comparison
To address concerns about unequal inference-time compute, we additionally report NFE-matched compar- isons, where NFE denotes the number of generator evaluations. For multi-step diffusion models, we set NFE = K where K is the number of denoising steps (we use K = 50 throughout). For single-step back- bones (SD-Turbo, ADXL-Turbo and PixArt-a DMD), a single forward generation corresponds to NFE = 1. Tterative noise-optimization methods (ReNO and CARINO) therefore use NFF = T, where T is the number of optimization iterations. For CARINOX, the total compute scales as NFE N x T, where N is the number of explored seeds and T is the number of optimization iterations per seed. Under a fixed budget of NFE = 50, we report CARINO (50 iterations) and compute-matches CARINOX configuration with N = 2 and T = 25 (2 x 25). We also include our main CARINOX setting (N = 5, T = 50, NFE 250) to illustrate the gains from additional inference-time scaling beyond the fixed-budget regime.
Model
NFE
Color
Shape
Texture
2D Spatial
3D Spatial
Numeracy 1 Non-Spatial
Complex
Mean
SD v SD v2.1
0.3576
0.3000
0.4461
0,3080
50
0.5085
0.4221
0.4022
0:1342
0.3230
0.4579
0.3127
0.3386
0.3734
SDXL
50
0.5879
0,4687
0.5259
0:2133
0.5566
0. 0.1968
0.5119
0.3237
0.4114
PixArt-a-ft
50
0,9600
0.4027
0.6477
0,2064
0.3001
0.5058
0.3197
0.3433
0.4468
Structured + SD v2.1
50
0.4930
0.2218
0,4900
0.1386
0.3224
0. 0,4550
0.5111
0.3355
0.3717
Attu-Exer 1
50
0.6400
0.4517
0.5063
0.1456
0.3222
0.4550
0.3111
0.3355
0.4072
IunNO + SD v2.1
50
0.7038
0.4094
0.5212
0,2027
0.3524
0,4802
0.3105
0.3574
0,4256
(1) SI--urbo
1
0.5252
0.4434
0.4888
0.1881
0.3112
0.4914
0.3095
0.3340
0.3856
(1) ReNO
50
0.5800
0,0500
0.5150
(1) + CARINO (7'55)
50
0.8519
0.7336
0.8013
0.2437
0.3020
0.5003
0.5269
0.4006
0.5642
(1)+ CARINOX (N-2. 7-25)
50
0,8385
0.7216
0,7910
0.2113
0.4015
0.6011
0.3264
0.1867
0.5531
(1) CARINOX (N=5, 7=50
250
0.8633
0.7600
0.8229
0,2588
0.4155
0.6248
0.3372
0.5041
0.5734
(2) STXL--Tubb
1
0.2839
0.4038
0.5472
0,2303
0.3612
0.4863
0.3114
0.3430
0,4009
(2) ReNO
0.6100
0.7400
0,5000
0.4700
0.5137
(2) + CARINO (7=50)
50
0,8192
0.7203
0. 7977
0,2858
0,4069
0.5835
0.3141
0.1850
0.5554
(9) CARINOX (N-2, 7-25)
50
0.8430
0.7141
0. 7838
0:2951
0.4076
0. 0.5803
0.3138
0.4880
0.3531
(2) CARINOX (5=5, 7=50)
250
0.7682
0,4117
0,5992
0.1922
0.3715
(3) PisArt-a DMD
0.4145
0.3487
0.3067
0,2213
0.3441
0.4896
0.3061
0.3466
0.3547
(3) ReNO
50
0.6400
0.5200
0.7200
0,2500
0,3900
0.5000
0.3100
0.4600
0.4877
(3) CARING (7-50)
50
0.8290
0.7528
706067
0,2620
0.4031
0.6144
0.3146
0.4782
0.5500
(3) CARINOX (V-2 T-25)
50
0.8175
0.7653
0.7803
0,2989
0,4019
0.5060
0.3139
D. 0.40997
0.0022
(3) CARINOX (5-5, 7-50)
250
0.8545
0.7721
0.8076
0.4164
0.3256
0.4879
0.3776
Table 12: NFE-matched evaluation on 121-CompBBnch++ For multi-step diffusion models and their
inference-time variants, report results at K = 50 denoising steps (NFE = 50). For single-step back- bones, we report the raw generation reference (NFE- = 1), a fixed compute budget setting (NFE = 50), and our main inference-time scaling setting (NFE = 250).
Table 12 indicates that the benefits of our approach not tied to using a larger compute budget. In the NFE- = 50 regime, CARINO and CARINOX (N-2, T-25) are the top-performing entries among the methods reported: they outperform the multi-step diffusion backbones (SD //t/////////////i////// a-ft), the inference-time attention methods on SD v2.1 (Structured Diffusion and Attend-and and-Excite), the multi-stop noise optimizer InitNO, and the single-stop bascline RcNO. This shows that even when we match the evaluation budget to the standard 50-step setting, optimizing the single-step generator with our reward combination yields stronger compositional alignment than both multi-step generation and prior inference- time optimization strategies.
J Effect of Norm-Guided Regularization (Ablation)
We include this experiment as an ablation to isolate the impact of the norm-guided regularization term used in CARINOX. In this setting, we remove the regularization term from the optimization objective and keep all other components unchanged (same rewards, Multi-Clip, learning rate, number of iterations, and seed selection protocol).
Figure g visualizes the optimization trajectory at several iterations. Without regularization (top rows), the optimized samples progressively exhibit high-frequency noise, color saturation, and noticcable artifacts, indicating a distributional shift away from the gencrator's latent prior. This degradation becomes more pronounced at later iterations, even when the prompt-level attributes or entities appear partially satisfied.
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Iter50
CARINOX W/O rog
a green bench and a blue book
CARINOX
pink elephant and a brown girafle
CARINOX WO reg
CARINOX
Figure 8: Ablation of the norm-guided regularization term. We show intermediate results across optimization iterations for two prompts. Without regularization (top rows), optimization increasingly drifts loward noisy and artifact-prone outputs (distributional shift). With regularization (bottom rows), optimization remains visually stable while improving alignment.
In contrast, the full CARINOX setting (bottom rows) preserves visual realism throughout optimization while still improving compositional alignment, suggesting that the norm constraint plays a practical role in stabilizing optimization and preventing drift into unrealistic regions of the image manifold.
Pseudo code for Noise Optimization and Exploration
To provide a clearer understanding of our method, we present the pscudocode outlining the key steps of our initial noise optimization and seed exploration pipeline. This includes the gradient-based refinement of the initial noise using adaptive reward weighting, as well the best-of-N seed selection strategy.
Algorithm details the noise optimization process, where the initial noise iteratively refined based reward gradients while ensuring stability through multi-backward computation, gradient clipping, and la- tent space regularization. Furthermore, it describes the seed exploration approach, where multiple noise initializations are optimized in parallel, and the final selection is determined based on the highest reward score.
L Alternative Benchmark: GenEval
To further validate our method, we evaluate on the GenEval benchmark, which measures compositional alignment across categories such as sinll//multiobjeer generation, counting, color attribution, and spatial positioning. Results in Table 13 show that CARINOX achieves competitive or superior performance across both SD-Turbo and SDXT.TTrkee backbones. In particular, it delivers strong improvements in color
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Algorithm CARINOX: Reward-Guided Noise Optimization and Exploration
Require: p (prompt), G8 (One-Step T2I Model), SI...v (random seeds), RI... (reward functions), (-) (noise regularizer), T (iterations), "f (learning rate), (grad clip), (regularization strenghh) 1: Sample N initial noise vectors { ...,,,N) [N (0,,
> initialize Gaussian seeds
2: for = 1 to N do
> exploration across multiple seeds
3: Initialize best score R f- -00 for t= 0 to T do Generate image 1 Go(().)) Initialize V. 0 for j = 1 to M do
gradient ascent for each seed
loop reward models
8:
Evaluate reward r = P) Compute gradient V V [R (K(e))
10:
Clip gradient V +- GradClip(V!..))
11:
Accumulate V. <- V V
12:
end for
13:
Compute total reward R = Ej
composite reward for current step
14:
if R > R then R +
15:
b update best image for this seed
16:
end if
17: Update noise E 1+1
e + 17 V.
a gradient ascent step
18: end for
19: Store final image I; 1
20: end for
21: return |" arg max R(I:.)
return bost image across seeds
attribution and overall mean scores, matching or surpassing SOTA basclines including RcNO and large- scale commercial systems. These findings confirm that CARINOX generalizes well beyond the primary benchmarks used in the main paper.
Model
Single
Two 1 Counting
Colors f
Position 1
Color Attribution f Mean
0.98
0.85
SDXL.
0.98
0.74
0.39
0.85
0.15
0.23
0.56
DALL-E
0.94
0.66
0.49
0.77
(..00
0.19
0.53
DALL-E3
0.96
0.87
0.83
0.43
SD3 (SB)
0.98
0.71
0.40
Turbo
0.99
0.51
0.38
0.85
0.07
0.14
0.49
(1) ReNO
1.00
0.82
0.60
0.88
0.12
0.33
0.62
(1) + CARINO
1.00
0.84
0.53
0.85
0.12
0.40
0.62
(1) CARINOX
0.13
SDXL-Tubo
1.00
0.66
0.45
0.81
0.09
ReNO
1.00
0.84
0.68
0.90
0.13
0.35
0.65
(2) CARINO
1.00
0.86
0.65
0.88
0.10
0.43
0.65
(2) CARINOX
1.00
0.86
0.66
0.90
0.16
0.48
0.68
Table 13: Quantitative results on GenEval benchmark for different categorics using two different backbones. For each calegory, the best value is bold, and the second-best value is underlined.
M Additional Qualitative Examples
To complement the main results, provide additional qualitative comparisons between CARINOX and baseline methods. These examples further demonstrate the robustness of our approach across diverse com- positional prompts, highlighting its ability to preserve both alignment fidelity and visual quality.
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Color Samples
SD-Turbo
*CARINOX
SDXL-Turbo
CARINOX
PixArt-a
*CARINOX
llue syllnder and
hlue gylinder and
red upple und
pollow baninas
rod
hiuee cup and brown olephant
yyllon frig und
browm hear
brown bond and
red bicycle and a heinee
yellow flower
a hluc duck and
groen apple and sulfcaue
apple balcc hnckpack
Figure 9: Qualitative examples for color. CARINOX adheres closely to specified colors and object color bindings.
Shape Samples
SD-Turbo
CARINOX
SDXL-Turbo
+CARINOX
PixArt-a
*CARINOX
spheriia sacer hal aet
i and a short cottage
3 tail skyseraper
dkyscrper
big truck and recinngullrr bacon strp
small Iaged and recsangalee uandeich
nveal loged und
big alephunt a small dog
ancuungar plune sad lcantrap phans charm
big horse
big small car
Figure 10: Qualitative examples for shape. CARINOX better preserves geometric structure and shape-
specific attributes under compositional prompts.
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Texture Samples
SD-Turbo
*CARINOX
SDXL-Turbo
CARINOX
PixArt-a
+CARINOX
fabric blanket and flaffy blanket
a fabric bag and a glass vasse
n fluffs cowe
and a glass jar
Wonden Bour a plastie bag and and a fabric
Weoden and a fabric hat
fabric bag and a glass va56
fabric hat and a glass mirrror
a plastic container fabric dress and a fabric blanket
and a glass Fas6
plastic chair and a woeden
a leather chair and a glass cup
Figure 11: Qualitative examples for texture. CARINOX captures fine-grained surface patterns and material attributes more reliably.
2D Spatial Samples
SD-Turbo
+CARINOX
SDXL-Turbo
*CARINOX
PixArt-a
CCARINOX
a candle the
bird the of person
rabblt on
right of
top
tHouse near
cundle left of a mouxe
a sheep on top
bay
plate on thee
a tablle on the top o pig
left of car
giraffe right of candle
vase on the right a
a horse on the
car
Figure 12: Qualitative examples for 2D spatial relations. CARINOX produces layouts that more faithfully
respect relative in-plane positions compared to baselines.
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3D Spatial Samples
SD-Turbo
CARINOX
SDXL-Turbo
CARINOX
PixArt-a
*CARINXX
candle front
a chalr in tront
waller in front of bowl
of o suitcase
of a
ballnon behind
a couch behind a giraffe behind
a pig behind
boy
a sofa
frog front of horse
a painting behind a
backpack
bird behind
dog behind
candle behind a vake
rabbiit
Figure 13: Qualitative examples for 3D spatial relations.
CARINOX better preserves depth and
front front-back/ee back/top bottom
banktto relationships.
Numeracy Samples
SD-Turbo
*CARINOX
SDXL-Turbo
CARINOX
PixArt-a
CARINOX
there rabbits and blegeles
sevce trmberrii
wir ripe for picking
four buttertiles
TWO SwaRt and me
flve kiles
three pillows
Four
Delicopters
Your frogs
Figure 14: Qualitative examples for numeracy. CARINOX matches object counts and distributions accurately than baselines.
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HRS Samples
SD-Turbo
-CARINOX
SDXI-Turbo
+CARINOX
PixArt-a
+CARINOX
SRIE dhen
bitger the thii..
SAvEO to 63144 merd
bare and cate the burur higger than cac.
dh und con the chair is bigger thuse the car
bagger
chal and eet
bigger cac.
hoex and cas. the fet is bigger the eac.
to
bigger
and rowe poopl illsbing thii cell phonoe
sinting RORBEY cutsie
3i20 Wrttee on IF rain Ahield.
RAULIP RANL CHILD SHIEED
RAIN SHIELD
teek bring ued tak Grott hiled
sondetttabee calin slice piaza
pii
EEW happlt ON CATRE
sigo weltie on BI Fost
barper than haras.
RSTEFIIC F IND
FAST FUN
siga writtn on IE
on
nion wilttes 11 Ahisls
FUSSSI FUSSAI
GRIZEY GRIZZLY GRIZR
FLUSH FLUSH!!
FLUSH
GRIZLY FUR
sige Dlsh!
FLUJSHI
Figure 15: Additional qualitative results on the HRS benchmark. Examples show that CARINOX consis-
tently improves compositional faithfulness over bascline models by correcting object relations, attributes, and text rendering.
34


Reviews:
[{"review_id": "n2cDuRb2h9", "reviewer": "Reviewer_Qjhm", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper addresses the persistent issue of compositional misalignment in text-to-image diffusion models, including failures in object relations, attributes, spatial layouts, and counts. Rather than modifying model weights, the authors propose CARINOX, an inference-time method tailored to single-step diffusion models. The method incorporates:\n\n- Initial Noise Optimization: gradient ascent on the noise vector using a composite reward, which is feasible with the choice of single-step diffusion architectures that would avoid long backpropagation chains.\n\n- Noise Exploration: sampling multiple noise seeds, optimizing each, and selecting the highest-reward image.\n\n- Principled Reward Selection: a correlation analysis on T2I-CompBench++ that identifies four reward models (HPS, ImageReward, DA Score, VQA Score) that best align with human judgments.\n\nThe approach maintains optimization stability through per-reward gradient clipping and a χ-distribution–based norm regularizer that keeps noise vectors close to the latent prior.\n\n**Strengths**\n\n- The combination of exploration and optimization is well motivated. Exploration improves diversity, and optimization refines candidates to achieve stronger compositional alignment.\n\n- The systematic reward selection is a key contribution. Instead of relying on ad-hoc choices, the authors evaluate rewards via Spearman correlation with human-annotated categories and select those that consistently correlate best.\n\n- The method demonstrates strong empirical performance. It outperforms baselines such as ReNO, InitNO, and ImageSelect across multiple backbones and compositional categories on T2I-CompBench++ and HRS. The ablation studies are also informative and support the design choices.\n\n**Potential Concerns**\n\nOverall, the approach is well motivated and I appreciate the principled way of choosing the reward functions. A few minor limitations include:\n\n- The caption of Table 4 seems incorrect: the table reports how various reward choices favor different aspects of compositionality, but the caption describes an ablation study for Figure 5.\n\n- Optimization must be run for each noise seed, so inference time increases linearly with the number of seeds. \n\n- The optimization component applies only to single-step diffusion models; using it with multi-step models would require backpropagating through many denoising steps, which leads to vanishing or exploding gradients.", "claims_and_evidence_yn": "Yes", "claims_explanation": "Yes, the claims are generally well supported.\n\n- The paper provides comparisons between exploration-only (CARINX), optimization-only (CARINO) variants, and the combined method (CARINOX), which demonstrates the benefit of integrating both components.\n\n- Both qualitative examples (Figure 4) and quantitative evaluations (Table 2) on T2I-CompBench++ show that CARINOX outperforms baselines such as ReNO, InitNO, ImageSelect, etc. across multiple categories and backbones.\n\n- The reward-selection procedure is justified through a Spearman correlation study (Table 5), where the four chosen rewards tend to show the strongest alignment with human-annotated scores.\n\n- Ablation studies further validate the impact of iteration count, seed count, and individual reward components, supporting the methodological choices.", "audience_yn": "Yes", "audience_explanation": "Yes. The paper addresses a known limitation of text-to-image diffusion models and proposes an inference-time solution that avoids retraining. The approach is well motivated and demonstrates strong empirical performance.\n\nResearchers working on diffusion models, controllability, and evaluation would likely find the work relevant. The systematic reward-selection procedure and the unified optimization–exploration framework could also be of interest to practitioners developing or assessing inference-time enhancement methods.", "requested_changes": "The caption in Table 4 should be corrected.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Ko9GqgqSDd", "reviewer": "Reviewer_w6sc", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "### **Summary**\n\nThis paper introduces **CARINOX**, an inference-time scaling framework that enhances text-to-image diffusion models by combining noise exploration and gradient-based noise optimization under multiple reward functions. It also proposes a **correlation-guided reward selection strategy** to identify complementary reward models that best align with human judgment. Experiments on T2I-CompBench++ and HRS benchmarks show that CARINOX significantly improves compositional alignment and semantic accuracy across backbones.\n\n---\n### **Strengths**:\nClear presentation: The paper is well written with clear structure, rich discussions, and an especially deep analysis of the Correlation-Guided Reward Combination Selection methodology.\n\nComprehensive evaluation: Systematic experiments across multiple diffusion backbones and diverse reward functions effectively demonstrate the robustness of the proposed approach.\n\n----\n### **Weaknesses**:\n**Incomplete literature coverage**: The paper overlooks several related works, such as SMC-based methods (e.g., DAS [R1], RBF [R2], Psi-Sampler [R3]), initial noise optimization (e.g., ORIGEN [R4]), continuous latent optimization (e.g., DOODL [R5], D-Flow [R6]), and flow-based models like FLUX-schnell.\n\n**Unfair computational comparison**: The evaluation uses different NFEs for baselines; fair comparisons should fix total NFE as done in prior literature.\n\n**Limited ablation depth**: Additional studies could explore alternative exploration strategies (e.g., sampling N candidates and selecting top-K, or progressive pruning under equal NFE budgets) to better analyze efficiency–performance trade-offs.\n\n---\n### **References** \n1. Kim et al., Test-time Alignment of Diffusion Models without Reward Over-optimization. ICLR 2025. (https://arxiv.org/abs/2501.05803)\n2. Kim et al., Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing. NeurIPS 2025. (https://arxiv.org/abs/2503.19385)\n3. Yoon et al., Psi-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models. NeurIPS 2025. (https://arxiv.org/abs/2506.01320)\n4. Min et al., ORIGEN: Zero-shot 3D orientation grounding for text-to-image generation. NeurIPS 2025. (https://arxiv.org/abs/2503.22194)\n5. Wallace et al., End-to-End Diffusion Latent Optimization Improves Classifier Guidance. CVPR 2023. (https://arxiv.org/abs/2303.13703)\n6. Ben-Hamu et al., D-Flow: Differentiating through Flows for Controlled Generation. ICML 2024.(https://arxiv.org/abs/2402.14017)", "claims_and_evidence_yn": "Yes", "claims_explanation": "Mostly yes; the experimental evidence is convincing and clearly supports the main claims, though fairness regarding NFE differences should be better justified.", "audience_yn": "Yes", "audience_explanation": "Yes; the findings would interest TMLR readers working on diffusion models, inference-time optimization, and reward-guided generation.", "requested_changes": "### **Critical (required for acceptance)**\n**1. Ensure fair comparison by equalizing NFE across baselines.** The paper should either (a) align total NFEs across all baselines or (b) explicitly justify the differences in computational budgets. Currently, CARINOX uses up to 250 NFEs (5 seeds × 50 iterations), whereas CARINO and ReNO use only 50. For a fair comparison, exploration-only baselines like CARINX should use a comparable total NFE budget (e.g., at least 50 or 250, matching the total computation of CARINOX), or the paper should clearly explain why unequal NFEs are acceptable. Providing this clarification would improve the credibility and fairness of the evaluation and align with practices in prior inference-time optimization literature.\n\n**2. Expand the literature review to include missing related works.**\n The paper should acknowledge and discuss prior methods that address similar goals using alternative paradigms:\n\n- SMC-based methods: DAS [R1], RBF [R2], Psi-Sampler [R3]\n\n- Initial noise optimization: ORIGEN [R4]\n\n- Continuous latent optimization: DOODL [R5], DFlow [R6]\n\n-----\n### **Recommended (would strengthen the work):**\n **3. Introduce a more elaborate ablation on exploration strategies.**\n For instance, test sampling N candidates and selecting top-K based on reward before optimization, or implement progressive pruning under equal NFE constraints. Such experiments would clarify how exploration efficiency and compositional accuracy trade off.\n\n**4. Discuss computation–performance trade-offs more explicitly.**\n Quantify and visualize how alignment improvements scale with NFE or GPU time, providing clearer justification for the “5 seeds × 50 iterations” design choice.\n\n**5. Clarify the role of norm-guided regularization.**\n Include an ablation removing this component (w/o regularization) to empirically verify its effect on stability and over-optimization prevention.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "MthBb5c3Oj", "reviewer": "Reviewer_r4G5", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents **CARINOX**, an inference-time framework that improves compositional text-to-image generation by combining gradient-based initial noise optimization with discrete exploration across multiple candidate seeds. The method employs multiple reward functions to optimize for different aspects of composition. Comprehensive evaluations are presented on T2I-CompBench++ and HRS benchmarks and a subsidiary evaluation on GenEval, across multiple single-step diffusion backbones, which show substantial increase in performance over previous methods. The approach combines exploration diversity with optimization precision through best-of-N selection, iterative refinement, and multi-backward gradient computation.\n\n\n**Key Strengths:**\n\n- Clear and smooth flow paper writing, well written paper \n- Unified framework addressing limitations of optimization-only and exploration-only methods\n- Reward selection based on correlation study with human preferences following logical research protocol\n- Per-reward gradient clipping mechanism is ingenious, preventing a single reward's dominance. Discussions in the paper providing reasoning for the mechanism is appropriate and welcomed.\n- Regularization formulation is well grounded in theory and empirical evaluations\n- Extensive qualitative and quantitative evaluation including standard benchmarks comparing against appropriate baselines, including appropriate derived conclusions and discussions on the findings\n- Transparent reporting and empirical evaluation of limitations\n\n**Key Weaknesses:**\n\n- Substantial computational overhead (33GB VRAM, 60s generation time) undermines single-step model advantages", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claims made in the paper are backed by appropriate evidence. Comprehensive qualitative and quantitative evaluations have been presented across 3 standard benchmarks, as well as several competing baselines. The selection of the reward functions and their use is done using a well-thought research approach involving a human study. Subsidiary evaluations as well as detailed experimental setups and analyses have been presented in the paper. Barring few parameter selection criteria, the choices taken in terms of framework architecture, supporting parameters, choice of gradient clipping and regularization have been clearly justified and reasoned in the paper. Ablation studies on independent parts of the method's modules provide additional insights into the effectiveness of a combined method over standalone approaches. The limitations of the method are transparently presented with empirical evaluations, offering directions for future work.", "audience_yn": "Yes", "audience_explanation": "This paper would be of interest for researchers studying generative models, which would benefit from the ideas of improving inference time performance without fine-tuning.", "requested_changes": "### **Weaknesses :**\n\n***Major*** - \n- The computational cost of the method and time taken for generation is quite significant, especially when compared to the base single-step generation mode. This undermines the essence of single step T2I image generators which were developed to reduce both time and computation over their multi-step counterparts, while delivering non-compromising quality. But since the method utilizes ~33GB of VRAM and upto ~60s of generation time, this defeats the purpose of single-step generation models with this inference time methodology, and their multi-step counterparts which are generally better in performance vs time may be used\n- While the limitation discussion and transparency is appreciated, an evaluation on the benchmarks comparing the [method + single step t2i models] vs multi-step models (without CARINOX) may be fruitful to see in terms of performance vs time, if by taking similar computational requirements the multi-step models perform in terms of generation time (I acknowledge that this was was partially represented on GenEval & T2ICompBench++ comparison). As I understand standard multi-step diffusion models have a typical generation time of 5-10s on an L40/A100 GPU. Please provide an appropriate comparison/discussion here\n\n***Minor*** :\n- An analysis of how many gradients are actually clipped to show quantitative effectiveness of the gradient clipping may be fruitful (it will also help identify which reward function is susceptible to gradient vanishing or explosion). A statistical evaluation could be added to offer more insight\n- In Equation 9, which depicts the final constraint optimization equation, the paper does not specify how is  **γ** chosen, is it to be done through training or is it a hyperparameter to be tuned?\n-An adaptive method could also explore different weight assignments to the rewards based on suitability to the prompt, wherein reward functions are weighted on prompt semantics. An ablation study on reward coefficients (**λ**) could be helpful here. The authors could offer an explanation on why λ values were all set to 1 and why is it an optimal solution\n- I would advise some fruitful results from the appendix such as the GenEval analysis to be moved to the main content along with other additional suggestions from above\n- It would be helpful to include exact GPU model and its TFLOP performance in the 'Time and Memory Usage Analysis' section. This would give a better idea of the actual computational performance", "broader_impact_concerns": "N/A", "additional_comments": "N/A", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)