Scaling Group Inference for Diverse and High-Quality Generation
Authors: Gaurav Parmar
Or Patashnik
Daniil Ostashev
Kuan-Chieh Jackson Wang
Kfir Aberman
Srinivasa Narasimhan
Jun-Yan Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: generative models
diffusion models
Primary area: generative models
TL;DR: Generative diverse and high quality samples by scaling inference.

Abstract: Generative models typically sample outputs independently, and recent inference-time guidance and scaling algorithms focus on improving the quality of individual samples. However, in real-world applications, users are often presented with a set of multiple images (e.g., 4-8) for each prompt, where independent sampling tends to lead to redundant results, limiting user choices and hindering idea exploration. In this work, we introduce a scalable group inference method that improves both the diversity and quality of a group of samples. We formulate group inference as a quadratic integer assignment problem: candidate outputs are modeled as graph nodes, and a subset is selected to optimize sample quality (unary term) while maximizing group diversity (binary term). To substantially improve runtime efficiency, we use intermediate predictions of the final sample at each step to progressively prune the candidate set, allowing our method to scale up efficiently to large input candidate sets. Extensive experiments show that our method significantly improves group diversity and quality compared to independent sampling baselines and recent inference algorithms. Our framework generalizes across a wide range of tasks, including text-to-image, image-to-image, and image prompting, enabling generative models to treat multiple outputs as cohesive groups rather than independent samples.

Published as a conference paper at ICLR 2026
SCALING GROUP INFERENCE FOR DIVERSE AND HIGH-RESOLUTION GENERATION
Gaurav Parmar
Or Patashnik 2.3
Daniil Ostashev²
Kuan-Chich Wang
Kfir Aberman²
Srinivasa Narasimhan'
Jun-Yan Zhu'
Carnegie Mellon University
SSna Research
Tel Aviv University
ABSTRACT
Generative models typically sample outputs independently, and recent inference- time guidance and scaling algorithms focus on improving the quality of individual samples. However, in real-world applications, users are often presented with a set of multiple images (e.g., 4-8) for each prompt. where independent sampling tends to lead to redundant results, limiting user choices and hindering idea ex- ploration. In this work, we introduce a scalable group inference method that im- proves both the diversity and quality of group of samples. We formulate group inference as a quadratic integer assignment problem: candidate outputs are mod- eled as graph nodes, and subset is selected optimize sample quality (unary term) while maximizing group diversity (binary term). To substantially improve runtime efficiency, predictions, allowing our method to scale up to large candidate sets. Extensive ex- periments show that our method significantly improves group diversity and qual- ity compared to independent sampling baselines and recent inference algorithms. Our framework generalizes across wide range of tasks, including text-to-image. traae-to-image and image prompting, enabling generative models to treat multi- ple outputs as cohesive groups rather than independent samples.
progressively prune the candidate set using intermediate
INTRODUCTION
Recent advances in generative models, such as diffusion models, have driven significant efforts in inference-time guidance and scaling techniques (Ho Salimans 2021 Ma et al. 2025 Parmar et al.) 2023). These methods effectively improve various aspects of output quality, such as alignment with text prompts or image aesthetics, and offer fine-graine mne-grained controls over the output. However, much recent work focuses enhancing the quality of individual samples generated in isolation.
Yet, in real-world applications, users are often shown a group of samples rather than just one. For example, many platforms (Midjourney, Inc. 2024 Adobe Inc. 2025) display grid of four to eight images per prompt by default, a practice that offers users crucial benefits: more diverse choices regarding layout, lighting, and style, and new inspirations and ideas for prompt refinement and local edits. This creates a gap between current research, focused on independent samples, and the practical need for diverse, high-quality groups in content creation workflows. How can we close this gap? Beyond creative applications, grouped generation is also essential in numerous practical settings where humans, or downstream systems, require groups of outputs rather than single images. Examples include synthetic data generation, where multiple diverse outputs (including long-tail and rare cases) are necessary for robust training; design and engineering workflows that evaluate several plausible candidate solutions. In all of these scenarios, group quality and diversity need to be jointly optimized.
In this work, propose scalable group inference method to jointly improve the diversity and quality collection of generated samples. We formulate this task as a quadratic integer program- ming problem, representing output candidates we select a subset of size K that maximizes a combination of individual sample quality, as unary term, and group diversity as binary term. However, direct approach involves running the T-step
graph nodes. From large set of M candidates,

---

Published as a conference paper at ICLR 2026
".. spaghetti couple dancing ..."
photo of a suitcase"
I.I.D.
Group
hand drawing watch ..."
purple potted plant ..."
I.I.D.
Group
green skateboard
"a surreal pizza planet"
Group I.I.D.
Figure I: Scalable Group Inference Results. We show the advantage of our proposed group infer- ence method over I.I.D. sampling. While I.I.D. sampling often yields repetitive results the same prompt, method generates more diverse and high-quality collection of outputs.
denoising process for all M candidates, resulting in (MT)) complexity. This is computationally expensive for large M and T (e.g., M = 128, T = 20). To address this, we introduce an efficient progressive selection strategy that leverages intermediate predictions during denoising iteratively prune the candidate set. This approach is grounded in the insight that these intermediate predictions of the final output, despite originating from a long denoising chain, serve as effective previews of the final image at each step (Figure 3). This approach reduces complexity to O(M + KTT), where typically K << M, enabling us to scale up our group inference to handle large candidate sets.
Extensive experiments show that our group inference method significantly outperforms indepen- dent sampling baselines and recent algorithms across various generative tasks and modalities. Our method scales efficiently and produces more diverse and realistic outputs given the same compute budget. We further provide a comprehensive ablation study demonstrating the effectiveness of our design choices. In summary, contributions are:
We reformulate the generation of multiple outputs from task of independent sampling of cohesive group inference. We are the first to formulate this as Quadratic Integer Programming (QIP) problem that jointly optimizes for individual quality and group diversity.
We introduce a progressive pruning algorithm that makes our QIP formulation practical and highly scalable. By leveraging intermediate Xo predictions previews, this strategy reduces the computational complexity from (MT)) to O(M + KT).
We demonstrate that our framework is well suited for inference-time scaling. For any given inference budget, progressive pruning allows for using a larger initial candidate set and obtains a higher quality and more diverse final output group.
2

---

Published as a conference paper at ICLR 2026
M Candidatea
M/2 Candidates
Outputa
denoise drop
Eg
QIP
t=T
t=T-1
t=0
Figure 2: Overview. Given a large number of M candidate noises, we gradually shrink the candi- date set through iterative denoising and pruning steps. At each step, first denoise the sample with the diffusion model Eg. We then compute the quality metric (unary term) and pairwise distances (binary term), and solve a quadratic integer programming (QIP) program to progressively prune the candidate set, yielding a final group of K diverse and high-quality outputs.
We provide extensive validation across multiple tasks, models, and metrics. Our method signifi- cantly outperforms baselines across both automated metrics and human preference studies.
We show that our framework is flexible and capable of naturally incorporating different notions of diversity. This allows our scalable group inference process to be tailored to specific downstream applications or end-user preferences.
RELATED WORKS
Diffusion models
synthesize high-quality samples through iterative denoising (Sohl-Dickstein
al., 2015 Ho al. 2020 Song al. 2020) and have shown success in text-to-image (Rom-
bach et al. 2022, Podell et al. 2024] Saharia et al.] (2022). video (Ho et al.
2022
Blatmann
al. 2023b a) and 3D synthesis (Poole et al. 2022 Lin et al. 2023 Shi et al. 2023). However,
common strategies to improve individual quality, such as tinennuning for high quality, less diverse
datasets (Rombach et al. 2022) or strong classifier-free guidance (CFG) (Ho & Salimans) 2021)
often sacrifice diversity (Astolfi et al. 2024). Additional conditioning methods like spatial controls
(Zhagg et al. )023) or image prompting (Ye et al. (203) improve controllability but also reduce di- versity, especially with strong guidance values. This lack of diversity is further worsened by distilled
generators (Sauer et al. 2024a Kang et al., 2024: Yin et al. 2024). Our work addresses this trade- off with group inference, enhaneing both sample quality and diversity in batches, and demonstrating applicability across various controls (text, spatial, visual prompts). Generative Photomontage (Liu
al. 2025) generates diverse results by composing samples, but requires additional user inputs.
Diffusion Inference and Guidance. Inference-time guidance improves quality and controllability of diffusion models without costly model finetuning. Early methods, such classifier guidance (Dhriwwal & Nichol| (021) and widely-used classifier-free guidance (CFG) (Ho & Salimans [021])
significantly increase sample quality, often the cost of diversity. Recent approaches manipulate internal representations, such as cross-attention maps (Parmar et af.] (023), or incorporate spatial control from inputs such as layouts or sketches (Chen et al. [2224] Kim al. 2023 Phung et al.)
2024 Voynov et al./ (2023] He et al. (023). Other strategies apply guidance over limited intervals
Kynkäänniemi et al. 2024) thresholding CFG to reduce saturation (Saharia al., 2022).
Similarly, (Jena et al. 2025 studies the problem of reward hacking and how finetuning can lead to
a loss of diversity.
While the above techniques improve individual samples, our group inference approach explicitly optimizes collective properties, balancing sample quality and inter-sample diversity (Cohen et al. 2024). A closely related work is particle guidance (Corso et al. (023), which incorporates a pairwise potential during denoising steps to encourage diversity. Our method differs in three ways. First, we improve both quality and diversity, while particle guidance often hurts image quality (Section 4.2 Second, our method scales effectively to a large number of images through early candidate pruning and sample selection, avoiding expensive optimization. In contrast, particle guidance is limited to small sets (e.g., four images) due to memory-intensin pairwise gradient computation. Third, our framework supports non-differentiable scores, enabling metries derived from multimodal LLMss.
3

---

Published as a conference paper at ICLR 2026
FLUX_1 1 Schnell Sampling Trajectory
Uiner Score Correlation al
Binary Score Correiaio al difenent intermeiitte iata prédictions
difterent interned ala pradictiona
1.0
1.0
Score Correlaton
0.8
Score Corralation
0.8
FLUX. Dev Sampling Trajectory
0.4
0.4
0.2
0.2
0.0
Thup Coteet
H0:
Dhe Setool
0.0
p(a) (Noise)
Roos
Reas
1.0
0.5 Timesteps
0.0
Timesteps
0.5
0.0
(Sample)
Figure 3: Correlation Between Intermediate and Final Generation Scores. On the left, we show the reverse diffusion process, visualizing the intermediate predictions x; of the final image at different steps for FLUX.1 Schnell and FLUX.1 I Dev models. Observe that intermediate predictions look similar to true final sample Xg- We further demonstrate this quantitatively by plotting the Spearman correlation of the Unary and Binary scores from x; versus final Xy scores, across different steps. For multistep models, the plots demonstrate strong correlations rapidly approaching 1.0, even at early timesteps. For distilled models (Flux-Schnell), the correlation is high from the first denoising step. This motivates using intermediate predictions for progressively pruning candidate samples.
Other related works such as CADS (Sadt et al. 2025 DiverseFlow (Morshed & Boddeti 20255
Shielded Diffusion (Kirchhof et al. 2025), and Neg (Singh et al. (024) also attempt to improve the diversity of generated samples, through negative guidance from a reference image. CADS ap- proaches this by adding Gaussian noise to conditioning signal in a monotonically decreasing sched- ule. DiverseFlow and Shielded Diffusion respectively employ a determinantal point process, and group repellency to drive diversity of generated samples. All of these approaches modify the sam- pling trajectory, and result, produce samples that have lower quality.
Inference-time Scaling. Test-time scaling, leveraging methods like chain-of-thouugh (Wei al. 2022). proposer and verifier (Snell et al. (022)) or multi-step reasoning, has become a key research area for large-language models (Muennighoff
al.) (025). Recently, researchers have adopted the inference-time scaling for diffusion models (Ma et al.) 2025), which uses off-the-shelf models and evaluation metries to search for better noises and increase the sample quality, often requiring thousands to tens of thousands of function evaluations (NFEs). However, text-to-image models differ from L.MS in three ways: they are often more computationally expensive (Li et al., (0244) users often pay 5 to 10 cents per image on leading platforms, and low latency is often required. Our method balances the inference computational cost with quality and diversity improvement.
METHOD
We propose Scalable Group Inference, a test-time selection framework that chooses a diverse, high- quality subset from a large pool of generated outputs. The method relies on scoring objective that combines a unary term that measures the quality of an individual sample and a binary term that computes pairwise properties such as image distances. We first formulate this quadratic integer programming problem (QIP) over binary selection variables. Then, to reduce compute cost, we introduce a progressive filtering strategy that prunes low-quality candidates early using intermediate predictions from partially denoised samples. We now describe both components in detail.
3.1 FORMULATION
Given generative model Go(z, c) that maps latent noise z ~ (z)) and condition to outputs x, our goal is to obtain set of K outputs, {x())) ) that exhibits both high quality and diversity together. We begin by generating large set of M candidate outputs {x(() AM using i.i.d. sampling:
x (i) = ()) c), (i) i.d. ~ p(z).
(1)
Let I {l,..., M) index the candidate samples. We associate each sample i I with unary score u; E R and each pair (i,j) with a binary score bij E R. Concretely,
u (x
(2)
by cosine (FDiNO(x(()), (x)))
(3)
4

---

Published as a conference paper at ICLR 2026
where fCLIP (Ramesh al. 2022) computes the similarity between the input image and the target caption, and JDINO is the DINOv2 (Oquab et al.) 2023) feature extractor. Note that our method is general and accommodate many different choices of score functions discussed later in Section
We introduce binary selection variables y; E [0.1] where y; = indicates that candidate is included in the next group. We define the group selection objective as:
max (0,1)
Σn IEI
Cuyy
X Σ big y:y;
WEI
subject to
yy= K.
(4)
iEZ
is the hyperparameter that controls the relative weight between the unary and binary scores. The first term rewards individually strong outputs; the second promotes diversity by favoring dissimilar pairs. Solving this QIP yields a subset of size K with desirable group properties. We use the branch-and-cod algorithm implemented by an off shell solver (Gurobi Optimization, [LC|[202] to solve the QIP The formulation model-agnostic and can accommodate any scoring functions, including functions that are not differentiable.
3.2 PROGRESSIVE PRUNING FOR EFFICIENT SELECTION
Naively applying group selection requires generating all M candidates completion, which is pro- hibitively expensive for recent large models like Flux Dev (Labs) 2024). For example, generating M = 64 samples over T 20 denoising steps requires M 7 forward passes. Even modern GPU like NVIDIA H100, this naive approach results a runtime of more than minutes. To reduce this cost, our progressive strategy that prunes candidates early using intermediate predictions.
Intermediate pruning. We maintain set S; C I of candidate indices at each step For each sample in S,, compute the intermediate prediction X,, evaluate the unary and binary scores, and solve the QIP (Eq. 4) to select the best subset. This subset becomes the next set S,-1, forming a nested sequence:
ST 3 ST-1 Sg.
Once the reaches desired output group size K, we stop pruning and complete the remaining denoising steps only for selected samples. See Algorithm iin appendix for detailed pseudo-code.
Reliability of early predictions. In modern multi-ster diffusion and flow-based models, the inter- mediate state x, already encodes coarse information about the final generated sample Xo. A common approximation of the final image timestep / is the predicted reconstruction:
x. t (x t, c),
where es predicts the noise or velocity time Although these predictions are coarse, they are sufficient for computing the unary and binary scoring functions.
To quantify this, we compute the correlation between the scoring functions (e.g., CLIP similarity or pairwise DINO diversity) evaluated on the intermediate images x, and the final output Xg. Across range of denoising steps, Figure 3 (right) shows strong correlations (e.g., r 0.7 after steps for multi-step models, and r > 0.95 after the first step for distilled models), indicating that intermediate predictions are reliable proxies. Figure R (left) shows this visually. Appendix Figure 17 shows a similar trend for other models. This high correlation enables us to safely rank candidates before they are fully denoised.
3.3 COMPUTATIONAL COMPLEXITY ANALYSIS
Suppose we start with M candidates and prune the set by fixed ratio p E (0, 1) at each denoising step until reaching target set size K. The number of candidates at timestep t is given by:
|S max (p' M,M)) K)
5

---

Published as a conference paper at ICLR 2026
FLUX.1 1 Dev
FLUX.1 1 Schnell
SD3 (M)
0.75
See Diversity (Binary
Set Diversity
(Bimary S
0.70-
Set Diversity
(Binary Soore)
0.70
0.63
0.60
0.65
0.63-
0.80
0.60
0.30
0,32
0:33
0.34
0.35
0.30
0.32
0.5d
Individual Sample Quality (Unary Soore)
Individual Sample Quality
Individusl Sample Quality
(Unary Score)
(Unary Score)
FLUX.1 Dapth
SynCD
Incressing
steps
Set Diversity
Spore)
0.45
Set Diversity
(Birary Scorc)
0.65-
Varying CFG
0.60-
Particle
Guidance
0.40
0.55
Guddance
0.31
0.32
0.50
0.55
0.60
Group
Indivdual Sample Quality (Unary Score)
Individual Sample Quality [Unery Score)
Inference (ours)
Figure 4: Quality and Diversity Pareto front. Each curve corresponds different inference strategy for five different models (FLUX.1 Dev, FLUX.1 Schnell, Stable Diffusion Medium). Our proposed Group Inference (blue) consistently dominates alternate methods achieving Pareto optimality and superior tradeoffs between quality and diversity across all methods. Varying CFG and Interval Guidance do not apply to the distilled model (FLUX.1 Schnell).
Let T denote the total number of denoising steps. We define the timestep 1" at which the candidate set size first reaches or falls below the target K:
c' log Tog(K/MM)*
log(p)
The total number of model evaluations fo required throughout the process can be written as:
1 1- p'p" 1
M
+ K (T to +
In contrast, naive sampling without pruning would require M T model evaluations. For typical parameter settings (e.g.. MM 64, K 4, p 0.5, T 20). our progressive filtering approach yields substantial compute savings (i.e., 184 vs 1280 evaluations, ~ 85% reduction). Our method has an overall complexity of O(M + KT).
EXPERIMENTS
We demonstrate the effectiveness of our scalable group inference across three different tasks: text-to- image generation, depth- conpiioondii generation, encoder- based image customization, and five dif- ferent base models: FLUX.1 Schnell, FLUX.1 1 Dev, Stable Diffusion (Medium), FLUX.1 1 Depth, and SynCD. Section 1 describes dataset and evaluation protocols used. Section an2 and Section 4.3 show a comparison with baselines. Appendix A.I
A.2 s.3oss more results, analysis and abla- and 21 for additional visual results generated with our method. Note that our method does not cause an increase in peak-GPU memory usage even at early stages; it generates samples serially, one time.
tions. Please Appendix Figures 22
19
20
4.1 DATASET AND EVALUATION
Datasets. We use GenEval Ghosh et al. 2023), validation split of COCO 2017 dataset (Lin et al., 2014), and DreamBooth dataset (Ruiz et al. 2023) for text-to-image generation, depth-conditioned generation, and image customization, respectively. For depth-conditioned generation, we first ex- tract depth using recent method Yang et al. (202a)).
6

---

Published as a conference paper at ICLR 2026
"A photo of a zebra and a bed.""
"A photo of cakes and a stop sign."
STOP
Ours
Ours
UEP
Low
CFG
CFG
STOP
Interval Guidance
Guidance
Interval
STOP
Particle
Guidance
Particle Guidance
STOP
STOP
Figure 5: Qualitative results. We compare our method (top row) against baselines targeting im- proved Juality-Diversing tradeoff with FLUX.1 Dev. For fair comparison, baselines were configured to approximate the diversity achieved by our approach. The result demonstrates that: (i) employing a low CFG scale to increase diversity results in diminished image quality; (ii) Interval Guidance exhibits reduced adherence the input text prompt; and (iii) Particle Guidance, by actively altering sampling trajectories, tends to produce less natural images. In contrast, our method outputs set of diverse outputs while maintaining good image quality and prompt fidelity.
Diversity
Quality
Model
Comparison
Ours pref.
Baseline pref.
Ours pref.
Baseline pref.
Ours vs Low-CFG
88.3%
11.70% 46.6%
85.6%
14.4%
Ours vs Interval Guidance
53.4 %
58.4 %
41.6%
FLUX.1 Dev
Ours vs Particle Guidance
81.2%
18.8%
79.4%
20.6%
Ours Low-CFG
N/A
Ours vs Interval Guidance
N/A
FLUX.1 Schnell
Ours vs Particle Guidance
55.5%
44.5%
62.3%
37.7%
Ours Low-CFG
76.8%
23.2%
80.8%
19.20%
Ours Interval Guidance
58.1 %
41.9%
57.9%
42.10%
SD3 (M)
Ours Particle Guidance
78.9 %
21.1%
85.9%
14.1%
Table 1: User preference comparison. User study results demonstrate that method is consis- tently preferred over alternative inference strategies. Across three different text-to-image models (FLUX.1 Dev, FLUX.1 Schnell, and Stable Diffusion Medium), users consistently chose our generations for both diversity and quality. Note that comparisons against Low-CFG and Interval Guidance are not applicable (N/A) for the distilled FLUX.1 Schnell model.
Models. We several recent models, including FLUX.1 Dev (Labs 2024 and Stable Diffusion 3 Medium (SD3-M) (Esser et al.) 2024), which are flow-based models typically requiring 20-50 denoising steps. We also evaluate FLUX.11 Schnell, timestep-distilles variant designed for efficient generation, typically using 1-8 steps. For depth-conditioned generation, we use FLUX.1 Depth, a model specifically trained for structural guidance based on depth maps. For customization, use SynCD Kumari et al. 2025), a recent encoder-based image prompting model.
text-image similarity to assess the quality of the individual samples for text-to-image and depth- to -omiattte-meeeeeee generation. For encoder-based image customization,
Score Functions. We use CLIP (Radford al. 202
cosine DINOv2 (Oquab et al. 2023) similarity between the input subject image and the output generated images for the unary score. Diversity computed for all tasks as one minus the cosine similarity between the DINOv2 patchwise features of all image pairs in the output set. We show evaluation with other metrics in Appendix Section A. and Figure [6] Our method can naturally accommodate a wide range of scores, fitting the user needs (Section 4.4

---

Published as a conference paper at ICLR 2026
Increasing Steps
X
Inference Diffusion Scaling
Group Inference (ours)
FLUX.1 1 Dev
FLUX.1 1 Schnell
SD3 (M)
1.104
1.10
Combined Score
1.05
Combined Score
1.05
Combined Score
1.05
1.00
1.00
1.00
0.95
0.95
0.90
0.90
0.90
0.85
0.85
25
50
75
20 Runtime (saconds)
40
25
50
75
Runtime (seconda)
Runtime (seconds)
Figure 6: Performance at different runtimes. Increasing Steps (red) shows limited gains with ad- ditional computation. Inference Diffusion Scaling (Ma al. 2025) (orange), which increases sam- ple count through independent I.I.D. generations, requires substantially more runtime for marginal improvements. In contrast, our proposed Group Inference (blue) achieves significantly better per- Cemmance-runtmnt tradeoffs, quickly outperforming both baselines with minimal overhead.
Ours wio progressive litering
Group Inference (Ours)
FLUX.1 Dev
FLUX.1 1 Schnell
SD3 (M)
1.05
1.15
1.15
49% faster
73% faster
Combined Score
1.00
Combinad Score
1.10
Combined Soore
1.10
55% laster
1.05
0.95
1.00
1.00
0.95
0.95
0.85
25
50
75
0.90
23
50
75
0.90
25
75
Runtime (seconds)
Runtime (seconds)
Runtime (saconds)
Figure 7: Importance of progressive pruning. In this ablation, we explicitly study the impor- tance of progressive pruning. The gray line generates all candidate samples to completion, without pruning, and solved the QIP at the end to select 4 output samples. Across multiple base generative models, progressive pruning consistently enables our method to select candidates efficiently and shows substantial speedups-499 73%, and 55% faster for comparable combined group scores.
4.2 BASELINES AND THE SITERSITY-QUALITY TRADEOFF
In generative modeling, tradeoff exists between optimizing for the perceptual quality of individ- ual samples and ensuring diverse set of outputs (Zhu al.] 2017 Ho & Salimans [021] Brock al. 2018). Figure 4 plots the quality-diversity Pareto front, Table I shows pairwise user prefer- ence studies, and qualitative comparisons are shown Figure Across all baselines, our proposed method consistently achieves superior iiversity-qualiin tradeoff. As illlustrated by the blue line in Figures 4 our approach dominates the Pareto fronts, yielding better diversity for a given quality, or higher quality for comparable diversity. Appendix Figures [6] 23 show additional compar- isons. We have included additional comparisons with CADS (Sadat et al. 2025), Shielded Diffu- sion (Kirchhof al. 2025). DiverseFlow (Morshed & Boddeti (0255) and NegToMe (Singh al., 2024 in the appendix.
Increasing Denoising Steps. We first consider the impact of simply increasing the denoising steps. While more steps can sometimes refine details, this has a minimal effect on meaningfully shifting the diversity-quality balance, as shown by the red line in Figures
Varying CFG. Next, we examine the widely used technique of varying the Classifier-Frn FFeef Guid- ance scale (Ho & Salimans 2021) (CFG). As depicted by the orange line in Figures 4 this traces a distinct tradeoff curve. Notably, low CFG values (c.g., CFG=1) largely increase output diversity but often at the cost of sharp degradation in sample quality and prompt alignment. This is visually
8

---

Published as a conference paper at ICLR 2026
seen through the poor image quality in the second row in Figure Table 1 user study further corroborate these observations.
where low CFG value is used.
Interval Guidance. Interval guidance (Kynkäänniemi et al. 2024 applies CFG only to subset of the timesteps. We conduct a sweep across various interval configurations (green line). Consistent with the third row in Figure []] this approach can offer image quality improvements CFG sweep. However, it still performs worse than our method in terms of both quality and diversity. In the example of zebra and bed on the left in Figure 5 Interval Guidance reduces diversity of zebra poses and does not generate a bed for two of the four outputs. Similarly, in the right example, interval guidance does not always generate stop sign. Moreover, both CFG sweeping and Interval Guidance do not apply to distilled models as they do not use guidance mechanisms.
a standard
Particle Guidance. Particle Guidance (Corso al. 2023) optimizes binary potential to en- courage diversity. Following the original work, we use DINO features for the diversity term. As illustrated by the purple line, Particle Guidance can indeed increase output diversity. However, this comes with sharp decrease in individual sample quality (fourth row in Figure as direct optimiza- tion of the binary potential actively alters the sampling trajectory. This can push the output samples off the learned data manifold, leading to less natural and artifact-prone images. Furthermore, Par- ticle Guidance incurs a substantial memory cost due to the necessity of computing gradients and backpropagating through the binary potential. This reliance on gradient computation also makes the method unsuitable for non nonfiifferentiable potential functions.
4.3 INFERENCE SCALING ANALYSIS
Scaling computational resources at the test time to enhance model performance is an increasingly useful paradigm in machine learning. For diffusion models, a native mechanism for test-time scaling involves increasing the number of denoising steps. Although this can initially lead to improved sample quality, this approach often yields diminishing returns; beyond a certain point, additional denoising steps provide progressively smaller gains in quality. In Figure we illustrate the impact of various test-time scaling strategies on the group objective (Equation
Our first baseline (Figure red line) increases the denoising steps for a fixed initial samples M. Consistent with existing findings (Ma et al.) 2025), this approach demonstrates minimal improve- ment in our combined score, with the curve quickly plateauing. Inference Diffusion Scaling (Mal iet al. 2025), uses the additional compute budget to perform search over multiple random seeds. For fair comparison, we implement this baseline with the CLIP text-image similarity as the veri- fier. This method does not incorporate intermediate predictions for their final text-to-image results and does not consider any pairwise terms. Consequently, is not effective in improving the group objective, as shown through the orange line. In contrast, our method invests scales by increasing the number of initial samples and produces consistent improvements as depicted by the blue line in Figure
Ablating progressive filtering. In Figure 7 we compare our complete method, which utilizes progressive filtering with intermediate predictions X, (red line), against variant that performs full denoising for all M candidate samples without such filtering (gray line). Demonstrating its ef- fectiveness across different architectures, our approach achieved comparable group scores while requiring up to 73% less runtime.
4.4 DIFFERENT DIVERSITY OBJECTIVES
Next, we show that our approach is general and can accommodate different pairwise binary objec- tives by simply swapping the binary term in our quadratic integer programming objective, as demon- strated in Figure Let us consider the example shown on the left corresponding to the caption "a giant neon rose." Standard I.L.D. sampling (top row) produces set of visually redundant images. All four roses are red and share a similar pose. In contrast, the bottom two rows are generated using our method with an identical unary quality term (CLIP text-image similarity) but different binary diversity objectives. The middle row uses direct coler-based dissimilarity as the binary term. This successfully steers the outputs towards set of images with varied and distinct color schemes. For the rose example on the left, this results in a vibrant set that includes blue, orange, and pink neon variants. In the bottom row, we use DINO diversity metric that captures more semantic features when comparing the pairwise distances. This change directs the model to produce a set with higher structural variance. As seen with the rose example, this yields outputs with different poses and camera angles. This direct comparison underscores a key strength of our approach: the ability to seamlessly integrate different notions of diversity to achieve targeted, user-defined visual outcomes.

---

Published as a conference paper at ICLR 2026
"A giant neom rose."
"A small alien ... sitting ... digarette..."
Color Diversity
Group
DINO
Diversity
Different pairwise objectives. Compared to LLD. (top row), our method allows for targeted diversity by defining different pairwise objectives. Unary quality term is identical but pair- wise binary term is varied in second and third rows. The middle row uses color-based binary term, while bottom row uses a DINO-based binary term.
Figure 8:
5 CONCLUSION
In this paper, have introduced scalable group inference, novel method to generate diverse, high- quality sets samples by formulating the selection as a quadratic integer program and leveraging intermediate predictions for improving the runtime efficiency. Our efficient approach significantly enhances group diversity and quality compared to existing baselines across various generative tasks. Still, our method has several limitations such as reliance on the base model and the score functions being efficient. We expand on our limitations in Appendix Section A.4 and Figure [5]
6 ETHICS STATEMENT
Our work introduces scalable group inference method designed to enhance the diversity and qual- ity of outputs from generative models. We recognize that generative models can be used to produce harmful, biased, or misleading content. However, our method operates at the inference stage and does not alter underlying model, its training data, its inherent biases. The primary goal of our work is to improve the user experience by providing more varied and high-quality set of candidate outputs for given prompt, thereby fostering greater creative exploration. By discouraging redun- dant outputs, approach encourages more thoughtful and efficient of generative systems.
7 REPRODUCIBILITY OF RESULTS
We provide all details of the experiments done in our paper, including the datasets used, model and bascline inference parameters, score computation, in Section 3 and appendix Section A.3 We have also provided full pseudocode for method in appendix Algorithm [ An anonymized version of the code is also included as a zipped file in the accompanying supplementary materials. These details and resources should allow other researchers to reproduce all results have shown this paper.
We thank Daniel Cohen-Or and Sheng- Yu Wang for their helpful comments and discussion. We are also grateful Nupur Kumari for proofreading the draft. The project is partially supported by Snap Research, NSF 223-2239,76, DARPA ECOLE, and the Packard Fellow- ship.
Acknowledgment.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Adobe Inc. Adobe Firefly: Generative AI for Creatives. 2025.
https: www. www.e....b............. com/
products/firefly.
Pietro Astolfi, Marlene Careil, Melissa Hall, Oscar Mañas, Matthew Muckley, Jakob Verbeek, Adri- ana Romero Soriano, and Michal Drozdzal. aunsisseecc-divvrsittyrlllll pareto fronts of condi- tional image generative models. arXiv preprint arXiv:2406. 2024.
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:231..15127, 2023a.
Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 22563-2257, 2023b.
Ollin Boer Bohan. Tiny autoencoder for stable diffusion. Retrieved May, 22:2024, 2023.
Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv: 1106, 2018
Minghao Chen, Iro Laina, and Andrea Vedaldi. Training-free layout control with cross-attention guidance. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024.
Noa Cohen, Hila Manor, Yuval Bahat, and Tomer Michaeli. From posterior sampling to meaningful diversity in image restoration. In The Twelfth International Conference on Learning Representa- tions, 2024. httttt:///pennteeee
Gabriele Corso, Yilun Xu, Valentin De Bortoli, Regina Barzilay, and Tommi Jaakkola. Particle guidance: non-iid diverse sampling with diffusion models. arXiv preprint arXiv:2310. 13102, 2023.
Prafulla Dhariwal and Alexander Quinn Nichol. Diffusion models beat GANs on image synthesis. In A. Beygelzimer, Y. Dauphin, P. Liang. and J. Wortman Vaughan (eds.), Advances in Neu- ral Information Processing Systems, 2021. URL httpps:/ ew net/ forum?id AAMuCvzaVE.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. Forty-first international conference on machine learning, 2024.
Dan Friedman and Adji Bousso Dieng. The vendi score: A diversity evaluation metric for machine learning, arXiv preprint arXiv:2210.02410, 2022.
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focusee framework for evaluating text-to-image alignment. Advances in Neural Information Processing Systems, 36: 22152, 2023.
Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual, 2025. URL https: www. gurobi com
Yutong He, Ruslan Salakhutdinov, and J Zico Kolter. Localized text-to-image generation for free via cross attention control. arXiv preprint arXiv:2306. 122330664460 14636, 2023.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NewrIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021.
11

---

Published as a conference paper at ICLR 2026
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-666 6851, 2020.
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David Fleet. Video diffusion models. Advances in Neural Information Processing Systems, 35:8633- 8646, 2022.
Rohit Jena, Ali Taghibakhshi, Sahil Jain, Gerald Shen, Nima Tajbakhsh, and Arash Vahdat. Eluci- dating optimal reward-diversity tradeoffss in text-to-image diffusion models. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision ( (WACV), pp. 232-242. IEEE, 2025.
Minguk Kang, Richard Zhang, Connelly Barnes, Sylvain Paris, Suha Kwak, Jaesik Park, Eli Shecht- man, Jun- Yan Zhu, and Taesung Park. Distilling diffusion models into conditional gans, In Euro- pean Conference Computer Vision, pp. 428 447. Springer, 2024.
Yunji Kim, Jiyoung Lee, Jin-Hwa Kim, Jung-Wor Woo Ha, and Jun-Yan Zhu. Dense text-to-image generation with attention modulation. In IEEE International Conference on Computer Vision (ICCV), 2023.
Michael Kirchhof, James Thornton, Louis Béthune, Pierre Ablin, Eugene Ndiaye, et al. Shielded diffusion: Generating novel and diverse images using sparse repellency. In Forty-second Interna- tional Conference on Machine Learning, 2025.
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Pick- a-pic: An open dataset of user preferences for text-to-image generation. 2023.
Nupur Kumari, Xi Yin, Jun- Yan Zhu, Ishan Misra, and Samaneh Azadi. Generating multi- synthetic data for text-to-image customization. ArXiv, 2025.
Tuomas Kynkäänniemi, Miia Aittala, Tero Karras, Samuli Laine, Timo Aila, and Jaakko Lehtinen. Applying guidance in limited interval improves sample and distribution quality in diffusion models. arXiv preprint arXiv:2404.07724, 2024.
Black Forest Labs. Flux.
-oc. Caborest-aab//ae flux/ 2024.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. Blip-2: Bootstrapping language- pre- pre-training with frozen image encoders and large language models. In International conference machine learning, pp. 19730-19742. PMLR, 2023.
Muyang Li, Yujun Lin, Zhekai Zhang, Tianle Cai, Xiuyu Li, Junxian Guo, Enze Xie, Chenlin Meng. Jun- Yan Zhu, and Song Han. Svdqunat: Absorbing outliers by low-rank components for 4-bit diffusion models. arXiv preprint arXiv:2411.05007,200 2024.
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming- Yu Liu, and Tsung- Yi Lin. Magic3d: High-resolution ext--to3dd content creation. In Proceedings of the IEEF/CVE conference on computer vision and puttern recognition, pp. 300-309, 2023.
Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European Conference Computer Vision, pp. 740-755. Springer, 2014.
Sean Liu, Nupur Kumari, Ariel Shamir, and Jun-Yan Zhu. Generative photomontage. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, and Saining Xie. Inference-time scaling for diffusion models beyond scaling denoising steps. 2025.
Midjoumny,, Inc. Midjourney Website. http::/www..iccjouueee...oo com/omee
2024.
Mashrur M Morshed and Vishnu Boddeti. Diverseflow: Sample-efficient diverse mode coverage in flows. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 23303- 23312, 2025.
12

---

Published as a conference paper at ICLR 2026
Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Fei-Fei, Hannanch Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, and Tatsunori Hashimoto. s1: Simple test-time scaling, arXiv preprint arXiv:2501.19393 2025.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fermandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learn- ing robust visual features without supervision. Transactions on Machine Learning Research, 2023.
Gaurav Parmar, Richard Zhang, and Jun- Yan Zhu. On aliased resizing and surprising subtleties in gan evaluation. In CVPR, 2022.
Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. Zero-shot image-to-image translation. In ACM SIGGRAPH 2023 Conference Proceedings, pp-
1-11, 2023.
Quynh Phung. songwei Ge, and Jia-Bin Huang. Grounded text-to-image synthesis with attention refocusing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for heshuresolttion image synthesis. In The Twelfth International Conference Learning Representations, 2024. URL htttps://ppenreviiee net. /orrr/formmmmm/
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209. /4988, 2022.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning, pp- 8748-8763. PMLR, 2021.
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text- conditional image generation with clip latents. arXiv preprint arXiv:2204.06/25, 1(2):3, 2022.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In JEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yacl Pritch, Michael Rubinstein, and Kfir Aberman.
Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 22500- 22510, 2023.
Seyedmorteza Sadat, Jakob Buhmann, Derek Bradley, Otmar Hilliges, and Romann M Weber. Cads: Unleashing the diversity of diffusion models through condition- annealld sampling. In The Twelfth International Conference on Learning Representations, 2025.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural informa- tion processing systems, 35:36479-36494, 2022.
Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rom- bach. Fast high-resolution image synthesis with latent adversarial diffusion distillation. In SIG- GRAPH Asia 2024 Conference Papers, pp. 1-11, 2024a.
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion dis- tillation. In European Conference Computer Vision, pp. 87-103. Springer, 2024b.
Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejic Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308. 165 2023.
13

---

Published as a conference paper at ICLR 2026
Jaskirat Singh, Lindsey Li. Weijia Shi, Ranjay Krishna, Yejin Choi, Pang Wei Koh, Michael F Cohen, Stephen Gould, Liang Zheng, and Luke Zettlemoyer. Negative token merging: Image- based adversarial feature guidance. arXiv preprint arXhv:2412.013399 2024.
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling Ilm test-time compute optimally can be more effective than scaling model parameters. arXiv preprint arXiv:2408. 03314, 2024.
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learn- ing. pp. 2256-2265, PMLR, 2015.
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.
Andrey Voynov, Kfir Aberman, and Daniel Cohen-Or. Sketch-guided text-to-image diffusion mod- cls. In ACM SIGGRAPH 2023 conference proceedings, 2023.
Jason Wei, Xuezhi Wang. Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoe V Le, Denny Zhou, et Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-24837 2022.
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to- 2023.
image synthesis. arXiv preprint arXiv:2306.092:
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li. Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36:1 1593--15935, 2023.
Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything: Unleashing the power of large-scale unlabeled data. In CVPR, 2024a.
Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, and Hengshuang Zhao. Depth anything v2. Advances in Neural Information Processing Systems, 37:21875-21911, 2024b.
Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint 2308.02.00..6621,, 2023.
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman. Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 6613-6623, 2024.
Lymin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-imaae diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3836-3847, 2023.
Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A Efros, Oliver Wang, and Eli Shechtman. Toward multimodal image-to-image translation. Conference on Neural Information Processing Systems (NeurIPS), 30, 2017.
14

---

Published as a conference paper at ICLR 2026
A APPENDIX
Section A.2 presents additional qualitative and quantitative results obtained by our method across multiple different base generative models and tasks, Section A.. provides an additional analysis of the different components of our method. Section A.3 details the datasets and implementation settings used for each of the baseline methods. Section A.4 discusses the broader impacts and limitations of our method.
A.1 ANALYSIS
In this section, we provide additional analysis of the different components of our method.
Runtime breakdown. In Figure g we show the runtime breakdown of different steps in the pipeline for the FLUX.1 Dev base model using CLIP text image similarity as the unary score and DINOv2 diversity as the binary score. On the left, we fix the output set size K to be 4 and increase the initial candidate size from 4 to 200. On the right, we fix the initial candidate size to be 200, and increase the output set size from 4 to 128. Note that across all settings, the runtime cost incurred by the QIP solver and the score computation is negligible compared to the forward pass of the de- noising transformer. Our method denoises one candidate at time and does not incur any additional inference memory costs even at early timesteps when the candidate size is large.
Different P values. Figure 12 illustrates the effects of varying pruning ratios (p) on the FLUX.1 Dev and FLUX.1 Schnell models. The figure presents both the Number of Function Evaluations (NFE) (left plot) and the wallclock runtime on single NVIDIA H100 (right plot). Across all plots, a pruning ratio of p = 1.0 signifies no progressive pruning. For the FLUX.1 Dev model, lower pruning ratios (c.g.. P 0.1 and p 0.25) are overly aggressive, leading to suboptimal scores. Conversely, a pruning ratio of p = 1.0 (no candidate filtering) achieves a good combined score but incurs high inference cost. A pruning ratio of p 0.5 strikes effective balance, yielding higher scores without excessive computational cost. We use p = 0.5 for all FLUX.1 Dev experiments.
A different trend observed for distilled FLUX.1 Schnell model. It can accommodate a more aggres- sive pruning ratio, such as = 0.1, without a noticeable decrease in the score. This can be attributed to the better reliability of the intermediate predictions for the distilled models, as shown in Figure of the main paper.
Efficient decoding. Our method uses an efficient decoder Bohan 2023) to decode all intermediate predictions for progressive pruning. In Figure [4 we ablate the use of efficient decoder and show that across both FLUX.1 Dev and FLUX.1 Schnell, using efficient decoder improves the runtime without sacrificing the score.
Evaluation with different score functions. Figure in the main paper shows the quality and diversity Pareto front for the text to image generation task. That figure uses CLIP text-image simi- larity (Equation 2 as the quality score and DINO diversity Baas the diversity score. Next, we evaluate our method using several additional score functions that are not used by our method for selection in Figure [6 The top row uses Image Reward Xu et al. (023) for measuring quality of samples and depth features measure diversity. Image Reward a network that is trained to learn human preferences for text-to-image generation. The depth diversity is calculated with the DepthAnything V2 model Yang et al. (2024b). The bottom row uses BLIP2 Li et al. (2023) to measure the qual- ity and CLIP features to compute the diversity. Figure 16 shows a comparison with three different base models: F Dev (left), FLUX.1 Schnell (middle), and Stable Diffusion medium (right). Across each model, our proposed group inference shows better trade off between quality and di- versity. Note that particle guidance obtains slightly better BLJP2 score than our method for Stable Diffusion (M). However, the outputs generated by particle guidance have artifacts. This is also reflected by low score for other metrics (Image Reward and CLIP), and a worse user preference score.
A.2 ADDITIONAL. RESULTS
Qualitative results. In Figures 19 20 and 21 we show additional visual examples of our method.
Across multiple models and tasks, method consistently outputs samples that are more diverse, and without any degradation in the quality. Similar to the Figure [5) in the main paper, Figure 23 shows additional visual comparison to baselines. In these figures, the Low CFG baseline uses
15

---

Published as a conference paper at ICLR 2026
Denoising
Unary
Binery Score
300
50-
Runtime
(ssconds)
Runtime
(scconds)
40-
200
30
20
100-
10
64
128 200
16
64
Initial Candidates
Output Size
(K)
Figure 9: Runtime breakdown. We show a runtime breakdown of our method using FLUX.1 Dev model the number of initial candidates (M, left) and the output set size (K, right) is increased. On the left plot, the output size is fixed to and the right plot the initial candidate size fixed to 200. Across all settings, the runtime is dominated by the denoising step.
CFG value of 1.0. Interval Guidance uses an interval of [0.6, 0.4], and particle guidance uses coefficient value of 100.
Correlation analysis. Figure 3 in the main paper shows the correlation between the scores com- puted with the final image and intermediate images. Figure 17 we show that similar corre- lation trend is visible in other base models (FLUX.1 Depth and SynCD). FLUX.1 Depth and SynCD show the CLIP text image similarity as the unary scores, and DINO diversity as the binary scores. This is consistent with our observations in the main paper.
A.3 IMPLEMENTATION DETAILS
Section 1 first provides implementation details and hyperparameters used for all settings shown in the main paper. Section A.3.2 lists details about the datasets used for each task.
A.3.1 BASELINES
Increasing steps. For FLUX.1 Dev, Stable Diffusion 3 Medium, FLUX.1 Depth, and SynCD, we consider the timesteps 10, 20, 30, 40, and 50. For the distilled model, FLUX.1 Schnell, we consider the timesteps 1, 2, 4, and 8.
FLUX.1 Dev, FLUX.1 Depth, and SynCD we consider the CFG values 1, 2, 3, 4, and 5. For Stable Diffusion 3 Medium, we consider the CFG values 1, 10, and 15. Note that FLUX.1 Schnell does not use CFG.
Varying CFG.
Interval guidance. For FLUX.1 Dev, FLUX.1 Depth, Stable Diffusion 3 Medium, and SynCD we consider the guidance intervals j0.9, 0.1], [0.8, 0.2). [0.7, 0.3], and [0.6, 0.4]. Note that FLUX.1 Schnell does not use CFG, and this baseline is not applicable.
Particle guidance. For the Particle Guidance baseline, we consider coefficient values 0, 10, 50, 100, and 200. Note that this baseline significantly increases the memory consumption during infer- ence.
CADS Sadat al. (2025). We follow the implementation details and hyperparameters provided by authors. We use = 0.6, = 0.9, and = 0.25. In Figure I0 we consider several v values (0, 0.5, 1.0).
DiversityFlow Morshed & Boddeti (2025). We implement this baseline following the details provided in the paper. Figure 10 conssiiders different scale values 10, 100, 200.
ShieldedDiffusion KKrchhof et al. (2025). We implement Shielded Diffusion following the al- gorithm and hyperparameters provided in the appendix of the paper. We set the overcompensation value to be 1.6 and vary the radius values to be 100, 200. 500. Figure 10 plots the different radius values.
Neg ToMe Singh al. 2004).. We the official released implementation for NegToMe.
16

---

Published as a conference paper at ICLR 2026
Algorithm Efficient group inference
model: The diffusion model 69
28: Initial noise vectors (2_i)
N: Total number of denoising steps
ts: The noise schedule (t_j}
K: The target number of samples
Conditicning information
rho: The dropping ratio P for pruning
def proup_inferent (model, 23, N, ts, K, G, rho):
Initialize the set of candidates from noise candidate_set - list(2s)
Dencising loop for in reversed (range +1)):
iiccrmccdltt__prvvviee next_latents [], []
# Get intermediate previews for all candidates
for x_t in candidate_set
preview, x_next dencise (x_,, tslil, tslj-11, c, model) wwwviews.appene (preview) next latents append (x next)
if len (candidate_sett > K:
Score previews and select the best subset
u unary.score (previews)
b binary-score (preev)
# Prune candidates based on the dropping ratio rho max int (len (candidate_
rhh)))
indices ces SolveQ (u, b, m)
candidate_set det [next_latents] for in indices] else:
candidate set next latents
return candidate_set
Inference diffusion scaling Ma al. (2025). Figure the main paper shows comparison to Inference Diffusion Scaling Ma et al. (2025), concurrent work, that shows an improvement in the quality samples. We follow the results in their paper and use random search as the strategy. For fair comparison, we use the same CLIP text-image-similari as the verifier.
Group inference (ours). In Figures 4 and of of the main paper, we vary the À defined in Equa- tion 4 while keeping the input samples M fixed. We use M 128 for FLUX. Dev. FLUX.1 Schnell, SD3 (M), and SynCD. Note that we additionally disable the classifier free guidance for the first denoising step for multi-step methods such as FL.UX.1 Dev and FLUX.1 Depth. Note that varying the weighting factor À does not change the runtime, and only shows the trade-off between the diversity of samples in the generated output set and the individual quality. For FLUX.I Dev, FLUX.1 Depth, SynCD we use p 0.5. For SD3 (M) we use higher 0.75, and for timestep distilled model FLUX.1 Schnell = 0.1 all experiments.
In Figures I and 7 of the main paper, want to study the performance at different runtimes, and therefore we fix the weighting factor À = 1 but vary the number of input samples M from 4 to 128.
Choice of scores. Unless specified otherwise, FLUX.1 Dev, FLUX.1 Schnell, SD3 (M), Flux.1 Depth use CLIP text-image similarity as the unary score, and DINO diversity as the binary score. SynCD uses DINO target image similarity score across all results. Figure keepss keeps the unary score fixed as the CLIP text-image similarity and shows the effects of varying the binary score function.
the unary score, and DINO diversity the binary
17

---

Published as a conference paper at ICLR 2026
FLUX.1 Schnell
FLUX.1 Dev
SD 3 (M)
0.70
0.7-
0.7-
Set Diversity
Sat Diversity
Set Diversity
0.63
0.6
0.6-
0.5
0.53
0.5
0.4
0.50
0.20
0.25
0.30
0.35
0.20
0.25
0.30
0.25
0.30
Individual Sample Quality
Individual Sample Quality
Individual Sample Quality
Increasing steps
Varynn
Interval Guidance
Particle Guidance
Group
Inferencc (ouns)
CADS
Diversty
NegToMe
Shelded
Dillusion
Figure 10: Quality and Diversity Pareto front. Each curve corresponds to a different inference strategy for three different models (FLUX.1 1 Dev, FLUX.1 Schnell, Stable Diffusion 3 Medium). Our proposed Group Inference (blue) consistently dominates alternate methods achieving Pareto optimality and superior tradeoffs between quality and diversity across all methods. Varying CFG and Interval Guidance do not apply to the distilled model (FLUX.1 Schnell).
FLUX.1 Dev
FLUX.1 Schnell
SD3 (M)
M4-188
0.80
Ma128
Ma138
Set Diversity
0.70
Mudd
Set Diversity
0.75-
Mus4
Set Diversity
0.70
M-B8
0.65
Ma
0.70
Mc18
M-16
0.65
Mi.6
0.65-
0.60
1..0f.
M.D.D.
0.60
I.I.D
0.32
0.33
0.32
0.33
0.34
0.35
0.330 Individual Sample Quality
0.335
Individual Sample Quality
Individual Sample Quality
Figure 11: Improvements as the number of initial samples M is increased. We show how the sample quality (measured with CLIP) and the set diversity (measured with DINO) improves as the number of initial starting samples is increased from 4 to 128.
Runtime. We measure inference runtime using wallclock time. Specifically, this is the time taken by each method to generate an output set of K images (where K = 4, unless specified otherwise) from a given input condition (i.e., a text prompt, depth map, or subject image). This measurement excludes initial model loading times and is averaged over 20 independent runs for each reported value. All runtime experiments utilize a single NVIDIA H100 GPU.
Uncertainty Estimation. We report standard errors for all quantitative results presented through- out our experiments. These standard errors are computed via bootstrapping with 1000 resamples.
User Study. We conduct two user preference studies to compare our method against each baseline on text-to-image generation. The first user study evaluates output diversity. In each comparison, the users are presented with two sets of output images generated by two methods. The users are instructed to choose the set that has the higher variety. The second user study evaluates individual sample quality. For this study, the users are shown two images generated by two methods and asked to pick the one with higher quality. Both studies use Amazon Mechanical Turk (AMT). Each comparison was rated by three unique users, resulting in a total of 23,226 preference judgments.
Additional Metrics. Tables 2 3 and compare our approach with prior methods along additional image quality (HPSv2 (Wu al. (2023). PickScore (Kirstain et al. 2023), ImageReward (Xu et al. 2023), and FID (Heusel et al. 2017), (Parmar et al. (202)) and diversity metrics (Vendi score (Fried man & Dieng 2022) and MSS). We use the COCO validation dataset for all FID results.
18

---

Published as a conference paper at ICLR 2026
FLUX.1 Dev
FLUX. 1 Schnell
1.10
1.00
Combined Score
Combined Score
Comminne Scora
Corbbined Score
1.05
pmm 0.1
P- 9.1
1.00-
3.00
p- 0.25
p.n 9.25
p
6.95
0.95
0.5
p
200
400
00
100
200
400
50
100
NFE
Runtime (sacondss
NFE
Rumiime
(socandss
Figure 12: Effects of different dropping ratio We show the effects of different dropping ratios P for two different base models: FLUX.1 Dev and FLUX.1 Schnell.
Ours w/o progressive filtering
--- Group Interence (Ours)
FLUX.1 1 Dev
FLUX.1 Schnell
Combined Score
Combined Score
Combined Score
Combined Score
1.00-
1.00
1.10-
1.30
2.08-
1.05
0.06-
0.35
1.00-
1.00-
0.95
0.85
0.85
200
400
600
25
50
75
200
400
25
50
75
Inference Cost (NFE)
Runtime (seconds)
Inference Cost (NFE)
Runtime (seconds)
Figure 13: Ablating the effect progressive pruning. Similar for Figure 7 from the main paper, show the importance of progressive pruning. We report both, the number of function evaluations (NFEs) and the wallclock runtime (using one NVIDIA H100). The two plots on the left show the comparison using FLUX.1 Dev. The two plots the right show FLUX.1 Schnell comparison.
Ours wlo efficient decoder
Ours w/ efficient decoder
FLUX.1 1 Dev
FLUX.1 Schnell
Combined Score
1.00
Combined Score
1.05
1.00
0.95
0.95
0.90
0.85
0.85
20
40
60
10
20
30
Runtime (scconds)
Runtime (scconds)
Figure 14: Ablating efficient decoder. We show the effects using an efficient decoder for decoding the intermediate predictions.
A.3.2 DATASET
Text to image generation. All text-to-image generation results with models FLUX.1 Dev, FLUX.1 Schnell, and SD3 (M) use 553 prompts from the GenEval dataset Ghosh et (2023). Note that we use 250 COCO validation prompts for computing FID.
Depth to image generation. All FLUX.1 Depth experiments use depth maps computed using Depth Anything Large Yang et al. (2024)) from 250 images from the validation split the COCO 2017 dataset Lin et al. (2014).
Encoder-based image customization. For all encoder-based image customization experiments using SynCD Kumari et al. (2025), we use 400 samples from the images in the standard DreamBooth dataset.
19

---

Published as a conference paper at ICLR 2026
Quality
Diversity
CLIP
HPSv2
PickScore
Image Reward f
FID
DINO 1
Vendi
MSS
Method
1
+
L.D..
0.327
0.302
0.235
1.028
21.22
0.584
3.173
0.562
Partiele Guidance
0.329
0.301
0.234
1.043
19.55
0.581
3.166
0.564
CADS
0.176
0.124
0.179
-22244
51.00
0.710
3.522
0.468
NegToMe
0.319
0.272
0.224
0.608
21.00
0.714
3.567
0.464
Shielded Diffusion
0.327
0.302
0.235
1.028
21.26
0.584
3.174
0.562
Diversity Flow
0.328
0.302
0.234
1.030
20.48
0.585
0.561
Ours
0.341
0.298
0.234
I.071
18.76
0.744
3.644
0.442
Table 2: Quality and Diversity Comparison with FLUX.1 Schnell. We compare our method to several baseline sampling methods for the generation quality and group diversity using the FLUX.1 Schnell as the base model. Across all baselines, only our method is able to substantially improve group diversity without sacrificing the quality of the samples. For instance, the vanilla L.I.D. inference has good sample quality but substantially worse diversity. Please refer to Figure TO for visual illustration the pareto frontier. Note that the FID results are computed using the COCO validation dataset.
Quality
Diversity
CLIP
HPSv2
PickScore
Image Reward
FID
DINO
Vendi
MSS
Method
LLD.
0.324
0,303
0.237
0,953
23.77
0.558
0.582
Interval Guidance
0.305
0.270
0.226
0.294
19.12
0.681
3.462
0.489
Particle Guidance
0.276
0.199
0.204
-0,699
58.28
0.564
3.101
0.577
CADS
0.165
0.139
0.177
-2.204
$1.91
0.784
3.714
0.412
NegToMe
0.324
0.301
0.234
23.02
0.663
0.503
Shielded Diffusion
0.323
0.294
0.231
0.907
20.02
0.627
3.313
0.530
Diversity Flow
0.282
0,214
0.208
-0.432
40.04
0.635
3.301
0.524
Oürs
0,326
0.347
'02237
[.00I
23.21
0.70
33522"
0.466
Table Quality and Diversity Comparison with FLUX.1 Dev. We compare our method several baseline sampling methods for the generation quality and group diversity using the FLUX.1 Dev as base model. Across baselines, only our method is able to substantially improve the group diversity without sacrificing the quality of the samples. For instance, CADS obtains high diversity score but has significantly degraded sample quality. Please refer to Figure 10 for a visual illustration of the pareto frontier. Note that the FID results are computed using the COCO validation dataset.
Quality
Diversity
CLIP
HPSv2
PickScore
Image Reward f
FID
DINO f
Vendi
MSS
Method
LLD.
0.333
0.288
0.232
0.973
19.48
0.605
3.233
0.546
Interval Guidance
0.315
0.241
0.220
0.121
19.49
0.703
3.518
0.473
Particle Guidance
0.317
0.257
0.216
0.583
22.85
0.636
3.518
0,523
CADS
0.202
0.091
0.179
-2.270
86.75
0.491
2.799
0.632
Neg
0.328
0.269
0.224
0.678
18.19
0.706
0.471
Shielded Diffusion
0.329
0,280
0.229
0.803
20.35
0.588
3.144
0.559
Diversity Flow
0.322
0.272
0.223
0.719
18.79
0.635
3.385
0.509
Ours
0.333
0.288
0.231
0.930
18.34
0.712
3,558
0.466
Table 4: Quality and Diversity Comparison with SD3 (M). We compare our method to several baseline sampling methods for the generation quality and group diversity using the Stable Diffusion (M) as the base model. Across all baselines. only our method is able to substantially improve the group diversity without sacrificing the quality of the samples. For instance, the vanilla I.I.D. inference has good sample quality but substantially worse diversity. Please refer to Figure TO for visual illustration of the pareto frontier. Note that the FID results computed using the COCO validation dataset.
A.4 BROADER IMPACTS, AND LIMITATIONS
Our work has a few limitations. First, our method relies on the base generative model's ability to produce sufficiently diverse and high-quality initial candidate pool. Consequently, if the underly- ing model generates outputs inherently poor quality or suffers from significant mode collapse, the efficacy of Scalable Group Inference in identifying optimal set will be inherently constrained, as
20

---

Published as a conference paper at ICLR 2026
Unary Score Used
ID
Group Inference
Image Reward HPSv2
1.028
1.451
0.302
0.315
Table 5: Robustness to Unary Score with FLUX.1 Schnell. We evaluate how different unary scoring functions affect IID and Group Inference performance when using FLUX.1 Schnell as the base model. The results show that our group inference method improves quality for both ImageRe- ward Xu al. 2023) and HPSv2 Wu al. (023)) unary scores.
Unary Score Used
III
Group Inference
Image Reward
0.953
1.303
HPSv2
0.303
0.311
Table 6: Robustness Unary Score with FLUX.1 Dev. We evaluate how different unary scoring functions affect IID) and Group Inference performance when using FLUX.1 Dev as the base model.
The results show that our group inference method improves quality for both ImageReward (Xu al, 2023) and HPSv2 Wu et al. 2023)
unary scores.
Unary Score Used
IID
Group Inference
Image Reward HPSv2
0.973
1.126
0.288
0.293
Table 7: Robustness to Unary Score with SD-3 M. We evaluate how different unary scoring func- tions affect IID and Group Inference performance when using Stable Diffusion 3 (M) as the base model. The results show that our group inference method improves quality for both ImageRe- ward (Xu et al. 2023) and HPSv2 Wu et al. (202)) unary scores.
"A photo of a Ferrari.
"A photo of Albert Einstein."
Group I.I.D.
Figure 15: Failure cases. The performance our method depends on the diversity of the initial candidate pool. (Left) For the prompt "A photo of Ferrari," the base model (FLUX.1 Schnell) exhibits strong color bias, exclusively generating red cars. Consequently, our method can find varied poses but is unable produce color-diverse set. (Right) Similarly, for "a photo of Albert Einstein," the base model only generates black-and- -nd--whte images, constraining our method from finding any color photographs.
our method selects from, rather than intrinsically enhances, these initial candidates. This is visually illustrated in Figure 13
Second, our method assumes that the unary (quality) and binary (diversity) scores are fast com- pute. If evaluating these scores, especially the pairwise diversity metric across a large candidate set, computationally intensive, the runtime benefits of our scalable optimization would be reduced.
Nevertheless, our method offers path to more user-centric systems that efficiently output diverse, high-quality sets of options, and enhance creative exploration. This capability can significantly reduce the iterative burden in content generation across various domains. Concurrently, the increased efficiency in generating diverse sets of synthetic media could also have potential for misuse, such
21

---

Published as a conference paper at ICLR 2026
Incressing steps
Varying CFG
Interval Guicance
Particle Guidance
Group Interence (ours)
FLUX.1 Dev
FLUX.1 Schnell
SD3 (M)
0.14
Depth Divereity
0.10
Depth Diversity
Depth Diversity
0.12
18
0.08
0.10
0.10
0.06
0.0
0.5
1.0
1.0
1.2
00.5
0.0
0.5
1.0
Image
Image Reward
Image Reward
0.38
CLI Diversity
0.35
CLP Diversity
0.36
CLIP Diverstty
0.34
0.34
0.30-
0.32
0.32
0.30
0.30
0.38
0.40
0.42
0.12
0.13
0.40
0.12
0.44
BLIP2
BLIP2
BLIP2
Figure 16: Quality and Diversity Pareto front with additional metries. We evaluate the quality and diversity of samples generated by different inference strategies for three text-to-image models (FLUX.1 Dev. FLUX,1 Schnell, and Stable Diffusion 3 Medium). The top row shows evaluation using Image Reward Xu et al. (2023) as the quality metric and Depth Diversity as the diversity metric. The bottom row uses BLiiP2 et al. (2023) and CLIP Diversity. Note that these metrics are unseen and not used by our method.
as creating more varied and potentially harder-to-detect misleading content, demanding proactive ethical guidelines and mitigation strategies.
A.5 LLM USAGE
We use LLM, specifically Google Gemini, in the preparation of this paper. Specifically, LLM was used to check for grammar and clarity of writing. L.Ms were not used for any ideation, technical content, analysis.
22

---

Published as a conference paper at ICLR 2026
Unary Score Conclation
Binary Bco Conricaton
1.0
1.0
FLUX Dapth Sampling Trajectory
Score Corretetion
0.00
Score Carrelation
0.0
0.0
0.8
0.0
0.4
0.2
0.2
Depth
(Noise)
(Sample)
0.0
0.0
1.0
Timestops
Timestops
Unary Score Comelation
Binary Store Comietion
d dftcrrtintemroee mitii pred ctcon
3lfe ont nntomediina predictions
SynCD Sampling Trajectory
Score Correlation
Score Correlation
0.81
0.6
0.2
Input Image
As u(a) (Noise)
Racs
Russ
Jo (Sample)
1.0
0.0
1.0
0.5
0.0
Timesteps
Timestape
Figure 17: Correlation between intermediate and final generation Scores. We follow the same protocol as Figure 3 in the main paper. On the left, show the reverse diffusion process, visualizing the intermediate predictions X, of the final image at different steps for FLUX.1 Depth and SynCI) models. We can observe that the intermediate predictions look similar to true final sample xo for both the models. We further demonstrate this quantitatively by plotting the Spearman correlation of the Unary and Binary scores from X, versus final X scores, across different steps.
FLUX. Dev
FLUX.1 Dev
1.10
1.10
Combined Score
1.05
Combined Score
1.05
1.00
1.00
0.95
0.95
0.90
0.90
Increasing Steps
0.85
0.85
600
50
75
Inference Cost (NFE)
Runtime
(seconds)
Inference Diftusion Scaling
FLUX.1 Schnell
FLUX.1 Schnell
1.104
1.104
Group Inference (ours)
Combined Score
1.05
Combined Score
1.05-
1.00
1.00
0.95
0.95
0.90
0.90
0.85
0.85
100
200
0
20
Inference Cost (NFE)
Runtime (seconds)
Performance different runtimes. Similar for Figure 6 from the main paper, show the different ways of allocating inference budget. We report both, the number of function evaluations (NFEs) and the wallelock runtime (using one NVIDIA H100).
Figure 18:
23

---

Published as a conference paper at ICLR 2026
I.I.D.
"A photo of a person and a bear." "
Group (ours)
I.I.D.
"A photo of horse and computer keyboard."
Group (ours)
I.I.D.
"A photo of two backpackks.
Group (ours)
I.I.D.
"A photo of red skis and brown tie."
X
Group (ours)
I.I.D.
"A photo of carrot."
Group (ours)
Figure 19: Gallery of results. Qualitative results that show the advantage of our proposed method over I.I.D. sampling for depth-to-image generation using FLUX.1 Depth as the base model. The input depth maps and captions are shown on the left and the generated outputs are shown on the right. Our method consistently generates outputs that have more diverse backgrounds, styles, and textures.
24

---

Published as a conference paper at ICLR 2026
I.I.D.
"A photo of purple wine glass and a black apple."
Group (ours)
I.I.D.
"A photo of red cake."
Groug (ssno
I.I.D.
"A photo of a refrigerator.
Group (ours)
I.I.D.
"A photo of donut below cat."
Group (ours)
I.I.D.
"sketch potted plant"
Group (ours)
Figure 20: Gallery of results. Qualitative results that show the advantage of our proposed method over I.I.D. sampling for depth-to-image generation using FLUX.1 Depth as the base model. The input depth maps and captions are shown on the left and the generated outputs are shown on the right. Our method consistently generates outputs that have more diverse backgrounds, styles, and textures.
25

---

Published as a conference paper at ICLR 2026
I.I.D.
"A red backpack.." "
Group (ours)
I.I.D.
"A sneaker with wheat field in the background.
Group (ours)
I.I.D.
"A cat in the jungle."
Group (ours)
I.I.D.
"A dog in the jungle." "
Group (ours)
I.I.D.
"A boot on top of a wooden floor."
Group (ours)
Figure 21: Gallery of results. Qualitative results that show the advantage of our proposed method over L.L.. sampling for feedforward customized generation using SynCD Kumari et al. (2025). The input image and captions are shown the left and the generated outputs are shown on the right. Our method consistently generates outputs that have more diverse backgrounds, object poses, and styles.
26

---

Published as a conference paper at ICLR 2026
"A photo of an apple."
"A photo of tie."
I.I.D.
Group
"A photo of boat."
"A photo of skateboard.."
I.I.D.
Group
"A photo of a computer mouse."
"A photo of a broccolí and vase."
I.I.D.
Group
"A photo of a couch and horse." "
"A photo of three refrigerators."
I.I.D.
Group
Figure 22: Gallery of Results. Qualitative results that show the advantage of our proposed group inference method over L.ID. sampling for text-to-image generation and depth-to-image generation. Top row shows results with FLUX.1 Schnell, the second row uses FLUX.1 Dev, and the last two rows use FLUX.1 Depth the base model. For text-to-image generation, our method produces more diverse object poses and orientations, while for depth-to-image generation, it enhances color and texture diversity while adhering to the input depth condition.
27

---

Published as a conference paper at ICLR 2026
"A photo of a baseball bat and giraffe."
"A photo of cell phone and a horse."
Ours
Low CFG
Low CFG
Interval Guidance
Interval Guidance
Particle Guidance
Particle Guidance
Figure 23: Qualitative results. We compare our proposed method (top row) against alternative
inference strategies targeting improved -uivest- tradeoff with FLUX.1 Dev base model.
28


Reviews:
[{"review_id": "IfgpsDbosL", "reviewer": "Reviewer_P5jo", "summary": "The paper proposes a method for generating diverse and high-quality image groups. The approach begins with a large pool of candidate initial noises and gradually prunes this pool by solving a QIP. The final subset of images is selected to jointly optimize quality and diversity. The evaluation is primarily based on human preference studies, which indicate improvements in both diversity and perceived quality.", "questions": "- According to the described procedure, most candidates are rejected at the timesteps with the lowest correlation with the final image. Could the authors provide an ablation where no pruning is done until the last stage, and the QIP is solved only once at the final step with $M = 64$ candidates and $K = 4$ outputs? How does this compare to the default setting with $p = 0.5$? \n- Could the authors provide a comparison under the same time budget, where the naive baseline produces 10 images and the proposed method produces 4? Could the authors report results for a setting with only 10 candidates, $p = 1.0$, and a single QIP solve at the final step for 4 outputs?\n- Could the authors report results using alternative unary score terms (i. e. HPSv2[1], ImageReward[2]) instead of CLIP similarity? These reward models are explicitly trained to optimize human visual preference. How sensitive is the final selection to the choice of unary score metric?\n- Could the authors clarify the exact definition of the combined score? In particular, is this score meant to be maximized or minimized? How are the different components combined to produce this score?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper addresses the practically relevant setting of generating a group of images rather than a single sample, which is common in many real-world applications.\n- The proposed selection strategy leads to substantial gains over baselines, according to human evaluations. The visual examples clearly illustrate the improvements of the method.\n- The paper is clearly written and easy to follow.", "weaknesses": "- The method is significantly slower than the baseline that directly generates four images. According to the paper, the proposed pipeline takes approximately 2.5 times longer. Given the same time, a straightforward baseline can generate approximately ten images, while the proposed method generates only four.\n- The method does not scale efficiently with respect to the number of candidates $M$ and the pruning ratio $p$. QIP solving grows quickly with $M$, and the required decoding and scoring introduce further overhead. According to Figure 9, the growth rate is considerably steeper than that of direct generation. This could sufficiently limit the method’s applicability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DznDw5gLbH", "reviewer": "Reviewer_nqjm", "summary": "This paper proposes a group inference approach for diffusion-based generative models to improve the diversity of generated samples. To this end, the authors formulate the generation of multiple outputs as a Quadratic Integer Programming (QIP) problem, where unary terms represent sample quality and binary terms represent diversity among samples. To address computational inefficiency, they introduce a progressive pruning mechanism that leverages intermediate denoising predictions to discard low-quality candidates early. Experiments across various tasks show improvements in both diversity and quality compared to several baselines.", "questions": "See weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The paper addresses an important problem of improving diversity of modern diffusion models, which excels at producing high-quality images yet suffers from lack of variations.\n- It is interesting to cast diversity-enhancing inference as a QIP problem.", "weaknesses": "- The related work section omits several important and relevant diversity-promoting diffusion methods, such as CADS [1], Shielded Diffusion [2], and DiversityFlow [3].  \n- Experimental comparison with existing baselines is limited.  \n  1. The paper only compares with Particle Guidance (PG) and Interval Guidance (IG). More diversity-oriented baselines such as CADS [1], Shielded Diffusion [2], and DiversityFlow [3] should be incorporated to clarify the empirical benefits of the proposed approach.  \n  2. There is no runtime comparison against baselines. Since the proposed method involves seemingly-expensive additional steps (during QIP solving and pruning), it is important to quantify the runtime overhead relative to existing methods. This is a significant point, since some diversity approaches (like CADS, IG, and Shielded Diffusion) are known to introduce marginal computations for improving diversity.\n- The evaluation metrics are incomplete.  \n  1. The diversity metric should include established measures like Vendi Score [4] or MSS [1], which are standard in diversity-oriented generation research [1-3].  \n  2. The quality evaluation relies only on ImageReward and CLIPScore, which are often highly correlated. Including other metrics such as PickScore or HPSv2 would provide a more comprehensive assessment. \n  3. A standard comprehensive metric like FID is missing.\n\n---\n**References**\n\n[1] CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling, ICLR 2024.\n\n[2] Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency, ICML 2025.\n\n[3] DiverseFlow: Sample-Efficient Diverse Mode Coverage in Flows, CVPR 2025.\n\n[4] The Vendi Score: A Diversity Evaluation Metric for Machine Learning, Arxiv 2022.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "btyo2h0O6f", "reviewer": "Reviewer_Vhst", "summary": "They propose scalable group inference method which improves the diversity and quality of generated outputs. They view this problem as quadratic integer programming problem (QIP), and they leverage intermediate predictions from each denoising steps for efficient filtering. To provide validity, they do experiment on accuracy of intermediate predictions, and provide computational complexity of its algorithm. Its results show better trade-off in diversity and quality than naive baselines, such as Low-CFG, and related works, such as Interval Guidance and Particle Guidance. Its algorithm can be extended to unique objectives, such as color diversity.", "questions": "Q1. In Figure 7, the progressive pruning in FLUX-dev does not seem to reduce the runtime as effectively as expected. Is there a specific reason for this difference compared to Schnell?\n\nQ2. Better diversity through negative guidance[2] could also serve as a baseline. While your gradient-free method seems more effective in terms of quality, I am curious about how it compares in terms of runtime.\n\n[2] Singh et al, Negative Token Merging: Image-based Adversarial Feature Guidance, Arxiv", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "S1. The paper is well-structured and clearly presented, with a solid experimental evaluation that thoroughly demonstrates all experiments substantiating the proposed method. The paper is well-organized and easy to follow.\n\nS2. The problem addressed in the paper is highly relevant to real-world scenarios, and the proposed method effectively resolves it without requiring any additional training.\n\nS3. The supplementary material is highly detailed, including numerous applications and qualitative results. It also provides reproducible implementation details and actual code.", "weaknesses": "W1. Limited novelty of proposed method. Proposed method is too straightforward. The idea of leveraging intermediate prediction accuracy is already well established, and the algorithm simply performs pruning based on group properties.\n\nW2. Error in line 455: the original inference-time scaling paper (orange) [1] also incorporates intermediate prediction in its algorithm (see page 8). \n\nW3. Although the proposed method is effective for scaled group inference scenarios, it relies on a pruning (filtering)-based approach rather than directly guiding the generation process (e.g., explicit guidance). Therefore, it may be less suitable than other baselines when performing additional small-budget sampling with given images (pre-sampled or user-provided), particularly for rare cases within the generation pool.\n\n[1] Ma et al., Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps, Arxiv version", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ptdbSViMKF", "reviewer": "Reviewer_rTVN", "summary": "This paper studies the task of batch inference with both quality and group diversity constraints. The authors formulate this problem as a quadratic integer program. To scale the method to large batch sizes, the authors propose a method that progressively prunes the candidate set of generations based on intermediate predictions of their final quality and final group diversity constraints. The authors conduct thorough experiments showing that their method both qualitatively and quantitatively outperforms existing batch inference methods.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The paper is really well-written and easy to follow. As a non-expert in this area, I commend the authors for how smooth the paper reads!\n- The proposed method is simple, natural, and seemingly efficient (I view this as a strength!). More overall, its general enough to capture quality and diversity constraints beyond the ones that the authors focus on in the main text. \n- The experimental results seem very promising -- the proposed method clearly dominates existing batch inference methods in terms of both quality and efficiency, based on Figures 4 and Figures 6. However, I am not an expert in this field, and it is very likely that I am not aware of other baselines beyond the ones the authors have considered. \n-  I also liked that the authors conducted a user study. One suggestion for Table 1 is that it seems like the baseline pref. column is redundant (since its just one minus the \"ours pref\" column). For clarity, I would remove that.", "weaknesses": "I don't have any particular concerns. However, I do feel that this paper would benefit from providing more motivation for why one should care about the batch inference problem studied. Right now, the only motivation is provided in Lines 37 - 42, however, I felt a bit unsatisfied. It would be great if the authors can further expound about why I should care about this problem.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)