TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive Generation
Authors: Zhekai Chen
Ruihang Chu
Yukang Chen
Shiwei Zhang
Yujie Wei
Yingya Zhang
Xihui Liu
Venue: NeurIPS 2025 poster ()
Keywords: Visual Auto-Regressive Modeling
Image Generation
Test-time Scaling
Primary area: applications

Abstract: Scaling visual generation models is essential for real-world content creation, yet requires substantial training and computational expenses. Alternatively, test-time scaling has garnered growing attention due to resource efficiency and promising performance. In this work, we present the first general test-time scaling framework for visual auto-regressive (VAR) models, TTS-VAR, modeling the generation process as a path searching problem. Inspired by VAR's hierarchical coarse-to-fine multi-scale generation, our framework integrates two key components: (i) At coarse scales, we observe that generated tokens are hard for evaluation, possibly leading to erroneous acceptance of inferior samples or rejection of superior samples. Noticing that the coarse scales contain sufficient structural information, we propose clustering-based diversity search. It preserves structural variety through semantic feature clustering, enabling later selection on samples with higher potential. (ii) In fine scales, resampling-based potential selection prioritizes promising candidates using potential scores, which are defined as reward functions incorporating multi-scale generation history. To dynamically balance computational efficiency with exploration capacity, we further introduce an adaptive descending batch size schedule throughout the causal generation process. Experiments on the powerful VAR model Infinity2B show a notable 8.7% GenEval score improvement (0.69→0.75). Key insights reveal that early-stage structural features effectively influence final quality, and resampling efficacy varies across generation scales.

TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive Generation
Zhekai Chen
Ruihang Chu
Yukang Chen
Shiwei Zhang?
Yujie Wei?
Yingya Zhang²
Xihui Liu
HKU MMLab
Tongyi Lab, Alibaba Group zkchen668outlook. com
CUHK
Abstract
Scaling visual generation models is essential for real-world content creation, yet requires substantial training and computational expenses. Alternatively, test-time scaling has garnered growing attention due to resource efficiency and promising performance. In this work, we present TTS-VAR, the first general lest-time scaling framework for visual auto-regressive (VAR) models, modeling the generation process path searching problem. To dynamically balance computational efficiency with exploration capacity, we first introduce an adaptive descending batch size schedule throughout the causal generation process. Besides, inspired by VAR s hierarchical coarse-to- to-fine multi-scale generation, our framework integrates two key components: (i) At coarse scales, we observe that generated tokens are hard for evaluation, possibly leading to erroneous acceptance of inferior samples or rejection of superior samples. Noticing that the coarse scales contain sufficient structural information, we propose clustering basee diversity search. It preserves structural variety through semantic feature clustering, enabling later selection on samples with higher potential. (ii) In fine scales, resampling- based potential selection prioritizes promising candidates using potential scores, which are defined as reward functions incorporating multi-scale generation history. Experiments model Infinity2B show a notable 8.7% GenEval score improvement (0.69.0.75). Key insights reveal that carly-stage structural features effectively influence final quality, and resampling efficacy varies across generation scales. Code available at https://gittbb..comalliiiaabbt/
the powerful VAR
Introduction
Recent years have witnessed significant progress image generative models [1-4], Previous text-to- image generative models primarily rely diffusion models [5, 6], which iteratively denoise the latent to generate high-quality images from random noise. Yet, advancements in large language models (LLMs) [7-9] have spurred interest in Auto-Regressive (AR) architectures for image generation [10- 12), leveraging sequential modeling to capture visual patterns. Among these, Visual Auto-Regressive Modeling (VAR) [13, 14] has emerged
groundbreaking paradigm. It encodes images into multi-scale coarse-to-fine representations and progressively predicts the "next scale" to synthesize images through hierarchical aggregation. Due to its superior efficiency and the potential for unified integration with LLMs, VAR is fast emerging as a key research frontier.
* Corresponding Authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Raw
Reward
(2) BoN
Reward
TTS-VAR
Reward 1.41
1.28
1.12
1.12
One batch size
Fixed batch size at any scale
Descending batch sizes at different scales
Prompt: Three origami white, gold, and blue perched black lacquered shelf above blooming bonsai
Color
Intermediate State
Rejected Low-Score Samples
Micouui
Clustering-besed Diversity Search
Resampling Potential Selection
Clusters
(a) Generation Process TTS-VAR
Figure 1: TTS- VAR generates several samples concurrently like Best-of-N (BoN). In 77S-VAR, we adopt an adaptive descending batch size schedule to make the most of AR efficiency, with feature clustering at early scales to ensure diversity, and resampling according potentials at late scales for more valuable samples. (1-3) are overviews showing the difference between raw inference, BoN, and TTS-VA VAR. (a) is detailed example of the generation process of our method.
Meanwhile, following the success of test-time scaling in LLMs [15-19]. researchers have begun exploring this methodology to image generative models for better results. In auto-regressive models, previous works typically formulate image generation as an image-level [20] or token-level [21] multi-stage process, treating the sequence of stages as the Chain-of- Thoought (CoT). However, these methods require additional training achieve effective scaling. Alternatively. diffusion- approaches [22-26] regard scaling as path searching problem, which scores different intermediate states and selects the most promising noise to denoise higher-quality images. There are two main strategies to achieve great improvement. One [22, 23] introduces additional denoising steps to obtain the clean latents for image decoding and select intermediate latent states based on the final decoded results. The other [24], instead, directly scores decoded images from intermediate noisy latents to guide selection by reward functions [27].
Inspired by this perspective, we explore whether VAR models can also benefit from path searching. However, directly adopting the two strategies from diffusion models non-trivial. For the former, the extra inference steps incur prohibitively high computation for VAR models with exponential complexity growth. They also disrupt the KV Cache mechanism [28, 29], which is crucial for retaining efficiency in AR inference. The latter also fails to reach the expectation. We observe that the rewards of images early scales struggle accurately represent the quality of final images, leading to incorrectly ruling out certain early-scale tokens which could be promising for later-scale generation, as demonstrated in Sec. 5.4. attribute this to difference between VAR and diffusion. VAR, unlike diffusion process that can refine generated noise through iterative denoising, all tokens remain fixed once generated. Fach token not only contributes decoding the final image but also directly affects all subsequent token generation, resulting in much lower tolerance for poor carly-stage tokens.

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In this paper, we introduce the first Test- Time Scaling framework for VAR, abbreviated as TTS- VAR. Different from simple selection according reward functions, we design scaling strategies aligned to the causal coarse-to- carse-to-fine generation process of VAR. Firstly, noticing the progressively increased consumption of FLOPs and RAM in VAR, we implement our framework under an adaptive descending batch size schedule, reduced from larger batch sizes at coarse scales to smaller ones in fine scales. This promotes the expression of more possibilities with little additional consumption. Secondly, though early scales are hard to evaluate by reward functions, we observe that structural information, which has a great impact on image contents, can be captured since early scales, as shown in Fig. (b) and Sec. 5.5. This motivates splitting the generation process into two key components: clustering- baaeddg-bbddddd diversity search for early scales and resampling- based potential selection for late scales. At early scales, while results of intermediate states can hardly be estimated, the diversity as batch size decreases, thus enabling later selections on samples with higher potential. We employ clustering on semantic features extracted by pre-trained extractors like DINOv2 [30], and pick from each category for dissimilar samples to ensure sampling diversity. At late scales, while scores of intermediate images share high consistency with those of final images, calculate potential scores to directly resample preferred samples. The potential scores are specifically defined reward functions based on the generation history of all scales, instead of only the current one.
aim to keep
To summarize, we propose TTS- VAR, the first general test-time scaling framework for VAR models. baaedniiin--eeedddddd
By integrating clustering-basee based diversity search and resampling- based potential selection tailored to VAR's s causal generation process, 77S- VAR consistently delivers stable performance improvements. We conduct comprehensive experiments and analysis on Infinity [14]. a scaled-up text-to-imagg VAR model, revealing why resampling methods exhibit scale-dependent limitations and demonstrating the benefits of structural feature clustering. Notably, 77S--VR significantly improves the GenEval score from 0.69 to 0.75, along with consistent improvements in other metrics.
2 Related Works
2.1 Test-time Scaling in Diffusion Models
Diffusion models [6, 31-34] create high-resolution images by denoising a Gaussian distribution into an image distribution. Initial research efforts [35, 5, 36] concentrated scaling up the number of denoising steps to enhance the quality. However, it has been observed that as the numher of inference steps increased, performance plateaued, and sampling additional steps is ineffective. Consequently, early research [37-39] mainly aims to reduce inference steps while maintaining image quality.
Ma et al. [22] address the scaling issue in diffusion models as a path searching problem, achieving significant improvements by applying several search strategies within the latent space, with reward functions serving as verifiers. Building on this problem definition, subsequent studies [23, 24] investigate the effectiveness of various search strategies and methods to accurately verify intermediate states for choice. Oshima et al. [23], for instance, employ few-step sampling instead of one-step sampling for denoised images that are clearer and more suitable for verification.
2.2 Test-time Scaling in Autoregressive Models
In autoregressive Large Language Models [9. 7, 8). test-time scaling is widely employed technique to enhance performance. Since Wei et al. [15] proposed Chain-of Thoought and enabled LLMs to benefit from a structured thinking process, various studies [16-19] have explored tree search, graph search, and other methodologies to improve outcomes further. All these strategics leverage the reasoning capabilities of models and utilize the properties inherent in natural language for scaling.
However, within autoregressive image generative models [10, 13, 11, 40], characterized by determin- istic process with a steady token length, it is unnatural to directly increase the image token sequence as "thinking". Instead, Guo al. [20] conceptualize generation CoT as an image- problem. By employing unified understanding and generation model [41] that first generates and then evaluates, it self-corrects results to align with expectations. Nevertheless, this approach relies solely on evaluating results, neglecting the generation process itself. Jiang et al. [21], instead, propose splitting the task into semantic-level and token-level phases, enabling a multi-stage generation as the thinking process. However, this method necessitates additional reinforcement learning for fine-tuning.
3

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3 Preliminary: Visual Auto-Regressive Modeling
Unlike traditional next-token prediction auto-regressive models such as LLama-Gen [10], Visual Auto- Regressive Modeling (VAR) [13] tokenizes an input image 7 into a feature map F E TR hxxxx With quantizer Q. quantizes the feature map F' into sequence of multi-scale discrete residual feature maps [42] } where K represents the number of residual features across varying resolutions. For each residual feature map rk. the resolution is h4 wk, which progressively increases from k 1 to k = K. Specifically, when k = 1, h2 wy = 1, and when & = K, h& h, Wi = w. From the sequence of residual features, at each scale k. gradually refined feature map f& can be computed as:
k
fA. Σ up(ra, (h, w)),
where up(..)) denotes upsampling the single-scale feature map to the target resolution, and S& is the aggregated sum of features {- During inference, the downsampled accumulated feature map fu = down(fk, (/1411, 1)) is appended as initial tokens for the prediction of the next scale and scale-wise causal mask is employed facilitate local bi-directional information modeling. The transformer is trained to predict the next-scale residual feature map. In Infinity [14], a VAR-based model scaled up for ext-tt--mmaee generation, the quantizer Q is advanced from VQ [43] to BSQ [44]. Additionally, Flan-T5 [45] text encoder ! is harnessed for prompt embeddings. With text prompt as the condition, the overall likelihood is:
K II
p(ri, ,
TK)
(ralri,2 ,,,,.,,,,....................,,. Pk-1;; (cc)).
(2)
4 Method
In 775- VAR, we conceptualize the generation of high-quality and numan-preferee images as a path searching problem, and identify two primary subproblems: (i) how to search for more possibilities, and (ii) how to select intermediate states for superior final results. Besides applying an adaptive batch size schedule as illustrated in Sec. 4.1 to enlarge the search scope, we introduce clustering- based diversity search in Sec. 4.2 and resampling-bast based potential selection in Sec. 4.3 to solve these problems. The complete method is illustrated in Fig. 1 (c).
4.1
Adaptive Batch Sampling
40
Fixed 1
Fixed
(TFLOPS)
Momory (GB)
30
Fixed
101
Adaptive
20
10a
Fixed
10-1
Fixed
10
Fixed
10-2
Adaptive
1
2
4
8
10
12
2
6
9
10
11
13
Scale Index
Scale
(a) M emory Usage
(b) Comput ional Complexity
Figure 2: Different Batch Size Schedules. We visualize the memory usage in (a) and computation complexity in (b) for 13 scales during the generation of Infinity, with fixed batch size and adaptive batch size. Specifically, the adaptive batch size here is 18.6666.6.4422222,,11,11,, schedule enables more possibilities with little additional consumption.
This batch size
Influenced by the causal attention mechanism, in VAR models, both RAM memory consumption and computational expense increase along with the length of the token sequence. As illustrated by the blue lines in Fig. 2 (a) and (b), during the inference process of early scales, memory requirements and computational costs are minimal. However, at later scales, when the pre-existing sequence extends significantly and the current prediction scale encompasses a large number of tokens, the resource consumption becomes substantial.
Therefore, we implement adaptive batch sizes during inference, capitalizing on the efficiency of lower consumption at carly scales. This descending adaptive batch size schedule {60, bi...., bx where

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K represents the number of scales, generates more samples in earlier stages and fewer samples in the later stages. For a typical VAR model encompassing 13 scales, the batch size schedule is (8N, 8N, 6N, 6N, 6N, 4N, 2N, 2N, 2N, IN, IN, IN, IN) unless otherwise specified. At early scales, clustering in Sec. 4.2 filters several categories. states. As demonstrated in Fig. 2, while the increased number of batches amplifies memory and computation costs at early scales, these additions are relatively minor in the overall expenditure.
late scales, resampling in Sec. 4.3 chooses superior
4.2 Clustering-Based Diversity Search
As sequence length increases, maintaining a large batch size becomes cost- pastt-roiibitiee, necessitating a filtering method for the desired samples. A straightforward approach involves calculating the reward function for intermediate results and selecting those with higher scores. However, our findings in Sec. 5.4 indicate that rating models struggle to evaluate early intermediate images for reward scores consistent with the final images, as also observed by Guo et al. [20]. To avoid erroneous elimination of certain initial samples that may hold potential for later-scale generation, we explore ways keep the diversity of samples.
We observe that during the generation process, unlike the details appearing in late scales, the structural information, which significantly influences the quality of final images, is conveyed from the early phases. We analyze this phenomenon in Sec. 5.5 and find extractors like DINOv2 [30] can effectively capture features strongly connected with structures. Given this, we create clusters based on the semantic features filter samples from each category and ensure structural diversity, thereby maximally enhancing possibilities for valuable results.
Specifically, from the current batch size b, we need to select 6,41 samples next states. Firstly, for b, intermediate images { b. j=1 each image is embedded in a high-dimensional semantic embedding space via feature extractor F, creating apply the K-Means++ [46] algorithm to cluster these embeddings into b cluster centers and select samples with the shortest L2 distance to cluster centers as new batches.
of embeddings S U, F(T,). Subsequently,
To extract structural information for diversity, we primarily employ the self-supervist DINOv2 [30] as the extractor, generating the feature map E R xw")xd To obtain one-dimensional features for clustering, apply PCA reduction on feature patches for s' E g(''xu'), We also consider pooling
the second dimension for s' E IR,, and supervised features from Inception [47] without the final fully connected layer. We discuss these choices in Sec. 5.5.
4.3 Resampling-Bass -ased Potential Selection
In contrast to early-stage diversity preservation through clustering, when intermediate images show high consistency with final results, reward functions can directly guide the generation toward higher quality and alignment with human preferences at late scales. Typically, a reward function r (x) is derived from reward model é, which includes specially trained rating models and vision-language models. For scores conditioned on a text prompt c, the reward function can be expressed as r (I, c). In the context of a generative model based on the generation distribution (z), we aim to steer the distribution to align with reward preferences [48, 24], as follows:
1 Pe (x) = Z (o(2)) exp(A (z(z.c)
(3)
where (x)(x) is the target distribution, Z is a normalization constant, and is a hyperparameter to control the temperature in selection.
To obtain high-quality samples, we evaluate the reward score for each intermediate state at current scale & and replace them with ones sampled from a multinomial distribution based on the potential score Ps. Considering that the generation of VAR is a path with historical states and merely rating the image 2µ ((fx). ). decoded by the image decoder D from the accumulated feature fk. may not adcquately reflect the potential of final results, we therefore contemplate several potential scores.
Potential Score Pt. We denote Pe 21...., as the potential score of a sample at scale k with ,
To, I1, .1,...,24 24. representing the generation history of this sample.
( #1, vx) exp(> (*k, e)): This directly utilizes the reward score as the potential score, referred to as Value. It is also known as importance sampling (IS) [49, 50].
5

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GenEvalt
Importance Sampling
Methods
Params
Best-otN
Ours
Two Ohj.
Counting
Color Anri.
Overall
Diffusion Models
SDXL
2.6B
0.74
0.39
0.23
0.55
+FK (N 8) [24]
2.6B
0.65
PixArt-Alpha [1]
0.6B
0.50
0.44
0.07
0.48
Cantual
DALI-F. [3]
0.87
0.47
0.45
0.67
0.72
FLUX [4]
12B
0.81
0.74
0.66
SD3 [60]
8B
0.94
0.72
0.60
0.74
0.70
Models
LlamaGen [10]
0.8B
0.34
0.21
0.04
0.32
Namber
(a)
Chameleon [61]
7B
0.39
Show-o [41]
1.3B
0.52
0.49
0.28
0.53
+PARM (N 20) [20]
1,38
0.77
0.68
0.45
0.67
1.5
1.480
Emu3 [11]
8,5B
0.71
0.34
0.21
0,54
Infinity
2B
0.8351
0.5923
0,6150
0.6946
Infiniy+1S (NV 8)
2B
0.8969
0,6220
0,6550
0.7181
InfinityBBoN (NV =
2B
0,9201
0.6756
0.6700
0.7364
Infinity+Ours (NV =2)
2B
0,9278
0.7113
0.6775
0.7403
Infinity+Ours (N-
2B
0.9501
0.7411
0.6800
0.7530
Infinity
8B
0,8866
0.7292
0.6750
0.7646
Namber Sameles
(b)
Infinity+BoN BBo (N
4)
8B 0,9175
0.7946
0.7225
0.7995
Onus (NV 22
8B
0.9330
0.7798
0.7050
0.7985
Ours (N
8B
0.9304
0,8036
0.7600
0.8188
Figure 3: Score Curves
Table 1: Quantitative evaluation evaluatto GenEval.
over Sample Number N.
(1 zx) exp(A (ra(zz, (=( c))): This computes the difference between two consecutive scales as the potential score, termed DIFF.
(r,,,,,))) This selects the highest score in the generation path as the current potential score, designated MAX.
(1 Fx) exp(A
Pa (Io, (1o, El...., x&) = exp(X. E ro(a,, c)): This accumulates scores from the history to determine the current potential score, labeled SUM.
Different potential scores benefit distinct attributes of the generation history. For instance, DIFF favors samples with higher growth rates, while MAX favors those with higher ceilings. In our setting, VALUE performs well. We will explore these choices in Sec. 5.4.
Experiments
In this section, we demonstrate the effectiveness our VAR on powerful VAR model Infinity [14] with resampling temperature À = 10. We present the comparisons in Sec. 5.1 and Sec. 5.2, and precisely analyze design details in Sec. and See. 5.5. Following previous work [24, 22, 51], we utilize IewaeReward [52] the reward function for guidance. We evaluate results using the main metric GenEval [53], and T2I-CompBench [54], with relevant indicators ImageReward [52], H [55, 56], Aesthetic V2.5 [57], and CLIP-Score [58, 59], based on prompts offered by GenEval.
5.1 Overall Performance
As shown in Table our proposed method demonstrates significant improvements over existing state-of-the-ann models and conventional test-time scaling strategies (Importance Sampling and Best- of-N). With model size of 2B parameters, TTS- VAR achieves an overall GenEval score of 0.7530 at = 8, surpassing the record 0.74 Stable Diffusion 3 (8B parameters) while utilizing 60% fewer parameters. Our framework also exhibits substantial gains across single items, like two objects. Notably, even with minimal computational overhead (N = 2), our approach attains the competitive performance of score 0.7403, outperforming Best-of-N (N 8) with only 25% sample number.
From the perspective of different N, 7TS-VAR maintains consistent performance gains across varying sample sizes in both GenEval and ImageReward metries, illustrated in Figure 3. While Best-of-N

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2D
3D
Non- spatial
Color
Shape
Texture
Numeracy
Complex
Model
Avg.
Spatial
Sputial
B-VQA
B-VQA
B-VQA
UniDet
UniDet
UniDet
S-CoT
S-CoT
Stahle v2 [31]
0.4839
0.5065
0.4221
0.4922
0.1342
0.3230
0.4582
0.7567
0.7783
Stable XL [2]
0.5255
0.5879
0.4687
0.5299
0.2133
0.3566
0.4988
0.7673
0.7817
Pixart-a-ft
0.5583
0.6690
0.4927
0.6477
0.2064
0.3901
0.5032
0.7747
0.7823
DALLE: 3
0.6168
0.7785
0.6205
0.7036
0.2865
0.3744
0.5926
0,7853
0.7927
FLUX.1 [4]
0.6087
0.7407
0.5718
0.6922
0,2863
0.3866
0.6185
0.7809
0.7927
Infinity28 2B [14]
0.5688
0.7421
0.4557
0.6034
0.2279
0,4023
0.5479
0.7820
0.7890
InII (N = 8)
0.5965
0.7746
0.5078
0.6501
0.2462
0.4194
0.6002
0.7803
0.7937
InfinityB--BoN (N = 8)
0.6115
0.7950
0.5439
0.6886
0.2545
0.4205
0.6090
0.7870
0.7937
Infinity2B+Ours (N
0.6151
0.7887
0.5578
0.6858
0.2697
0,4286
0.6112
0.7853
0.7936
InfinityB+OOurs (N 8)
0.6230
0.8073
0.5914
0.7121
0.2644
0,4302
0.6340
0.7880
0.7963
Table 2: Quantitative evaluation on 121-CompBench.
sampling benefits more from larger N, its performance remains distinctly inferior ours, even failing to match our results at N = with = Detailed evaluations across different values are provided in the appendix for comprehensive analysis.
To better demonstrate the generalizability our framework across different models, we also con- ducted comparative experiments on the Infinit8BB model. The results show that even on this stronger model, our approach effectively boosts performance, improving accuracy from 0.76 0.82-ar an increase of about Moreever, it surpasses the gains achieved by methods such as IS and BoN un- der the same settings. This indicates that our method can be directly applied other VAR-based architectures.
We further evaluated performance on T21-CompBench [54]. As presented in Table 2, ITS-VAR exhibits a remarkable improvement across every indicator compared to the base model. Consistent with the consequences on GenEval, our method achieves superior results N 2 compared to Best-of-N at N = 8, and secures the highest scores on most individual items and the overall average.
5.2 Qualitative Comparison
We present images generated by different methods here for reference, to further explain the improve- ment in image quality and text alignment. As displayed in Fig, 4, our method correctly generated objects with the required number, for example, the "kayaks" in the first case and the "birds" in the second case, which hard for base models and other scaling strategies to achieve. In the complex scenes with several pairs of numbers and colors, 7TS- VAR ensures the color attribute like the "golden apples" in the fourth case, avoiding the problem of attribute forgetting.
5.3 User Study
To evaluate from the view of practical usage and gather user feedback, we selected a small set of 15 image groups and compiled them into questionnaire on Google Forms. For each group, the images are randomly shuffled, and participants were asked to select the best generated image based on three criteria: Image Quality, Image Rationality, and Consistency with the Prompt. total, we collected 21 completed questionnaires, resulting in 315 sets of survey data for each indicator. The percentages of votes received by each approach are summarized in Table below.
Metric
Baseline
IS
BoN
Ours
Image Quality Image Rationality
13.3%
7.9%
13.3%
65,4%
13.7%
8.6%
8.6%
69.2 %
Consistency with the Prompt
1.3%
1.9%
2.5%
94.3%
Table 3: User Study.
5.4 Resampling for Superior Samples
Analysis. In path searching, resampling intermediate states with high potentials a straightforward yet effective approach for superior results with minimal consumption. However, in VAR, this may not
7

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SD3 Prompt: Two pink flaminges standing wooden dock, with three turq uoise kayaks floating the calm lake behind them.
nf inity
nf inity-1 5
nf inity- BoN
infinit
Prompt: oll painting, green vintage blue left black bicycle right it, parked road, with two birds in the sky
Prompt: blooming garden path shows two orange marigolds leaning toward one yellow sunflower, all surrounded by lush green leaves, with two butterfiles hovering above them V-formation.
rromp:: An elegant dining table with dark wooden surface ho two ivory candles burnngg gently, crystal vase with three red roses, and a silver tray holding two. golden apples.
Figure 4: Qualitative Comparison. Each line shows results generated by Stable Diffusion (SD3) [60], Infinity, and Infinity with test-time scaling strategies, with objects marked blue and relationships marked green.
be advantageous and can even be detrimental at certain scales. In Fig. 5 (a), we illustrate the score distinctions between solely employing Best-of-N (N 2, 4) and concurrently applying potential (VALUE) resampling specific scales. Although Best-of-N selection ensures comparatively high results, is evident that resampling at early scales (e.g., scale 3) leads noticeable decline the final results. Instead, resampling later scales yields certain degree of improvement.
1.0
ImageReward Vanation
0.00
N-4
Resampling Scale Index
Consistency,
0.8
-0.02
0.6
0.4
-0.06
N-2
0.2
N=4
2
11
Index (b) Value Consistency
(a) Score Variation
Figure Resample choices. The left graph shows the variation in ImageReward Score when executing resampling-bant based potential selection at different scales (0-11). The right graph shows the consistency between scores intermediate states and those of final results at each scale. demonstrates that in VAR, not all scales are suitable for selection, and some may lead to degradation.

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N
Resampling Scale
GenFval
Imagekeward
HPS
CLIP
Aesthetic
0.6946
1.132
0.3042
0.3366
0.3811
[6. 9]
0.7133
1.2572
0.3066
0.3379
0,5801
[6, 8, 10]
0.7130
1.2591
0.3066
0.3381
0.5809
8,8,9 9, 10, 11]
0.7114
1.2497
0.3067
0.3378
0.5810
[6, 9]
0.7276
1.3534
0.3082
0.3398
0.5817
[6, 8, 10]
0.7247
1.3592
0.3085
0.3397
0.5822
16. 7, 9. 10. 11J
0.7210
1.3558
0.3083
0.3398
0,5830
Table 4: Resampling Scale Difference. This table shows results with different resampling scales.
We analyze this phenomenon from the perspective consistency between intermediate states and final images, shown in Fig. 5 (b). We compute potential scores each scale and select the with the highest potential accordingly. We then assess whether the selected best intermediate state aligns with the best final image (the optimal state scale 12), resulting in sequence of scores between and I, termed consistency. Low consistency of early scales in this evolving curve indicates that those scores rarely accurately reflect the quality of final results. The scores become valuable from certain late scale, like scale 6, with comparatively high consistency. This explains why resampling efficacy varies and should be applied selectively on later scales.
Resampling Scales. Based on the aforementioned observation, we further investigate whether increasing the resampling frequency late scales is beneficial. As illustrated in Table compared with raw inference, resampling greatly enhances the results. However, increasing the frequency has a negligible impact. For instance, there is modest improvement in ImageReward and HPS for scales [6,8, 10] compared to scales [6,9], but at the cost of Geneval. Considering that executing resampling incurs additional computational expenses related image decoding and score calculation, opt to resample only at scales 6 and 9.
1.0
1.0
VALUE
VALUE
0.9
MAX
MAX
Consistency
0.8
DIFF
Consistency
DIFF
0.7
0.6
0.4
0.5
11
Scale Index
Index
(a) Potential with N 22
(b) Potentials with N
Figure 6: Consistency of Different Potentials. We visualize the consistency between different pairs of potential scores and final results. Accordingly, VALUE and MAX can better indicate the potentials.
As mentioned in Sec. 4.3, we have developed distinct computational modes to explore those with higher potentials. Initially, we seek better options through theoretical approach by visualizing the consistency. As exhibited, DIFF continuously yields low consistency levels and fails to predict the desired outcomes. SUM demonstrates stable increment but with comparatively low values. In contrast, VALUE and MAX exhibit similar characteristics, maintaining relatively high scores since scale and showing a steady increase.
Potential Scores.
Experiments in Table 5 with N = 2, present coherent results. Among these potentials, DIFF lags in all indicators. Although SUM achieves some acceptable outcomes, the overall score remains low, As forecasted by consistency, VALUE and MAX achieve the highest scores in text-related metrics such as GenEval, IewgeReward, and HPS, indicating higher likelihood of selecting for superior final results. Considering that MAX requires score calculations at each scale and leads to an additional computational cost, we utilize VALUE the potential score.
N
Potential Gentival
ImageReward
HPS
CLIP
Aesthetic
2
VALUE
0.7133
1.2572
0.3066
0.3379
0.5801
2
MAX
0.7150
1.2510
0.3065
0.3379
0,5803
SUM
0.7130
1.2364
0.3064
0.3379
0,5801
DIFF
0.7006
1.1725
0.3042
0.3365
0,5798
VALUE
0.7276
1.3534
0.3082
0.3398
0.5817
4
MAX
0.7285
1.3495
0.3082
0.3398
0.5815
4
SUM
0.7244
1.3326
0.3082
0.3394
0,5830
DIFF
0.7030
1.2412
0.3051
0.3378
0.5808
Table 5: Potential Score Difference, This table shows results with different potential scores.

---

5.5
Clustering for Structural Diversity
Gener ated
0
1
2
3
5
6
9
12
Scales
Figure Visualization Generation Process. The text prompt is "a photo of bottle and bicycle". The left is the generated image. The right is the generation process and visualized DINOv2 features from scales. It demonstrates that features early scales can indicate structural information.
Analysis. In reference to Sec. 5.4, resampling is not universally applicable all scales. Nevertheless, VAR. the effectiveness and low cost associated with early scales present an invaluable and unmissable opportunity to search for more samples, thereby unlocking greater potential for the final outcomes. We notice that, given the same prompt, the structure images significantly influences the scores. Moreover, unlike details that emerge later, structural information can be captured from the early scales. The right side of Fig. 7 demonstrates that the generation process follows a structure- to-detail progression, with rough outlines becoming discernible from scale 2. When using DINOv2 to extract intermediate images and visualizing them through PCA, as seen in the bottom line, these features exhibit characteristics akin the original images. Consequently, we leverage structural information and conduct clustering- baseed diversity search to sample dissimilar structures, thus scaling for more possibilities, especially when resampling may suffice.
Clustering Scale
GenEval
ImageReward
N
Extractor
GenEval
ImageReward
0.7087
1.2545
2
PCA
0.7184
1,2682
2
[2]
0.7089
1.2513
2
Pool
0.7127
1.2720
2
[5]
0.7099
1.2558
Inception
0.7073
1,2727
2
[] 5]
0.7184
1,2682
PCA 0.7337
1,3610
4
0.7244
1,3471
Pool 0.7296
1.3629
4
[2]
0.7300
1.3502
Inception
0.7207
1.3664
[5]
0.7293
1.3558
Table 7: Extractor Difference. This table shows results when adopting different feature extraction methods. PCA and Pool here are both transformed from 2-dimensional features extracted by DINOv2.
[2.
0.7337
1.3610
Table 6: Clustering Scale Difference. This table shows results with and without cluster- ing at certain scales.
Clustering Scales. According Fig. features at scale 2 display coarse structure, while those at scale 5 reveal a refined structure similar to final outcomes. Therefore, we specifically apply clustering on these scales. The results of N 2, 4, with and without clustering. are presented in Table The first lines of each block denote the results without clustering (Best-of-N), with subsequent lines showing outcomes under different clustering seales. Evidently, each clustering increases the likelihood of yielding better results, and there is an obvious growth when employing both scales.
Extractor. We tested various extractors for clustering features outlined in Table 7. Both PCA and Pool are transformations of features extracted by DINOv2 [30], as detailed in Sec. 4.2. While supervised Inception [47] features perform optimally in ImageReward. they notably underperform in GenEval. PCA delivers superior results on average and is employed. We attribute this to that the patch-level features from PCA align more closely with the observed structural traits.
Conclusion
In this work, we introduce the first general test-time scaling framework for VAR models. Through analysis on different scales, we demonstrate that 7TS-VAR, which incorporates adaptive batch sampling, clustering-based diversity search, and resampling-based potential selection, aligns with distinct stages of VAR generation process. This dual-strategy approach enhances final result quality with minimal additional computational cost while maintaining algorithmic efficiency. We notice the limitation and potential socictal impact on privacy and copyright, and discuss these in the appendix.
10

---

Acknowledgements
This work is supported by the National Nature Science Foundation of China (No. 62402406).
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Appendix
A Algorithm of TTS-VAR
We describe the algorithm of TTS-VAR in Alg. 1. Following the generation process of VAR [13] (Infinity [14]), TTS-VAR first predicts the residual tokens at the current scale and adds them the accumulated feature maps. At scales that require clustering, 77S-VAR uses the extractor to gather features from b, intermediate images decoded from the feature maps. then clusters the samples based on these features and selects by+1 ones as the next batch. At scales that require resampling, TTS- VAR employs the potential function calculate scores for each image and samples b,+1 indexes from the multinomial distribution for superior intermediate states.
Algorithm 1 77S-VAR
Require:
Scales S {81, ...,............................ 8k), Descending batch sizes B {bi, b2, bx) Clustering scales set Se,
Resampling scales set S,, Generative model Reward model To Extractor F, Potential Score function P, Text prompt
1: Initialize accumulated feature map fo with zeros.
2: for E (1,2...., K) do Ti Generate b;, 84. f,)
Iterate through scales
3:
4:
fi + f-
5:
if S. then
Clustering phase
6:
+- Decode(fi) Seat F(x)
7:
8:
inder t- KMeenst+(feat, b(+1)
9:
f. s felinder]
10:
else if scale & S, then
> Resampling phase
+- Decode(ffi)
12:
rw t- (x)
p P(rw)
14:
index +- Multinomial(p, be+1)
15:
f - filindex)
16:
end if
17:
end for
return Final generated images Decode(fx)
B Hyperparameters Settings
In K-Means, we set the next batch size bi+1 in the adaptive descending batch sizes as the num- ber of centers, to find b different structure categories. For PCA, we only select the first ma- jor component, as we utilize it for dimension reduction only. In all benchmarks, we use seeds from 0 3 for different results. For experiments requiring multi-GPU, like N 8, we set for each device. We use adaptive N,,,N,I,, 1N,1N] for Infinity2B with 1024 pixel
the seed from 100*process _indeeess__idxxxeeeee to 100*process_incee batch size [8N,8N 6N WNN.NN,6N,44.. A.,.,,,,N,..... 2N and [8N, NN,6N,666.... N.N.N,,N,6NN444 NN N for Infinity8B with 512 pixel.
For other parameters related Infinity, we keep them the same as the original public settings.
C Detailed Main Results
We present detailed Infinity2B results of variant curves in Table 6. As evident, 77S- VAR demonstrates clear advantages across all indicators [53, 52, 56, 57, 59] compared the baselines. In Table we list each item of the GenEval [53] metric. Generally, our method significantly improves performance on handling two objects and counting tasks. We attribute this to importance of structural accuracy in multi-character scenes, particularly when two objects and multiple identical objects (counting) are involved. For instance, when provided with prompt for three objects, there is possibility that the model may incorrectly generate layout with four objects. Once this error occurs, following the
23

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NV
Strategy
GenEval
Imagekeward
HPS
CL.P
Aesthetic
Raw Inference
0.6946
1.1320
0.3042
0.3366
0.5811
Ours
0.7253
1.3226
0.3084
0.3395
0.5822
Importance Sampling
0.7022
1.1941
0.3051
0.3374
0.5807
2
Best-of-N
0.7087
1.2545
0.3069
0.3384
0.5813
2
Ours
0.7403
1.4136
0.3106
0.3411
0,5821
4
Importance Sampling
0.7116
1,2883
0.3067
0.3387
0.5815
4
Best-of-N
0.7244
1.3471
0.3083
0.3397
0,5820
4
Ours
0.7437
1,6055
0.3112
0,3414
0,5821
8
Importance Sampling
0.7181
1.3657
0.3085
0.3395
0.5810
Best-of-N
0.7364
1.4144
0.3103
0,3406
0.5820
Qurs
0.7530
1.4995
0.3122
0.3420
0.5810
Table 6: Scores Different Strategies of Infinity2B.
structure-to-detaal generation process in VAR, it becomes challenging for subsequent scales to rectify. However, 77S- VAR facilitates structure diversity, thereby enabling the selection of layout with the correct contiguration and avoiding the irreversible wrong generation process for inferior samples.
N
Strategy
Overall
Single Obj.
Two Obj.
Counting
Colors
Position
Color Attri.
Raw Inferenee
0.6946
0.9938
0.8351
0.5923
0.9293
0.2020
0.6150
Ours
0.7253
0.9938
0,9072
0.6518
0.9192
0.2096
0.6700
Importance Sampling
0.7022
0.9969
0.8497
0.6071
0.9268
0.1869
0.6475
Best-of-N
0.7087
0.9906
0.6339
0.9242
0.1944
0.6300
Ours
0.7403
0.9936
0,9278
0,7113
0,9318
0.1995
0.6775
Importance Sampling
0.7116
0.9906
0.8840
0.6339
0,9318
0.1970
0.6325
4
Best-of-N
0.7244
1.0000
0,8969
0.6756
0.9242
0.1944
0.6550
Ours
0.7437
0.9906
0,9510
0.6994
0.9293
0.2045
0.6875
Importance Sampling
0.7181
0.9906
0.8969
0.6220
0,9318
0.2121
0.6550
Best-of-N
0.7364
0.9938
0.9201
0.6756
0.9444
0.2146
0.6700
Ours
0.7530
0.9969
0.9501
0.7411
0.2172
0.6800
Table GenEval Details. This table shows each item of the GenEval benchmark. "Object" is short for "Obj.", ", and "Attribute" is short for "Attri.".
In Table 8, we present the detailed different scores of Infinity8B. As shown, our method promises stable increase in different models, including ones with superior performance.
N
Strategy
GenEval
ImageReward
HPS
CLIP
Aesthetic
Raw Inference
0.7646
1.2095
0.3081
0.3388
0.5721
Ours
0.7931
1.3929
0.3115
0.3414
0.5727
Importance Sampling
0.7834
1,3091
0.3097
0.3399
0.5716
Best-of-N
0.7880
1.3435
0.3127
0.3423
0.5722
Ours
0.7985
1.4572
0.3106
0.3411
0.5730
Importance Sampling
0.7901
1.3786
0.3110
0.3409
0.5722
Best-of-N
0.7995
1.4092
0.3122
0.3397
0.5730
Ours
0.8189
1.5100
0.3139
0.3425
0.5727
Table Scores over Different Strategies Infinity8B.
D Results on DPG-Bench
We evaluate the performance of the baseline Infinity model, the Best-of-N (BoN) method, and approach on the DPG-Bench, specifically for the case where N = 4. For each prompt, we generated four corresponding images for evaluation using seeds 0 through 3. The results are presented in Table 9.
E Performance over Computational Consumption
We here display the changing curves of GenEval, ImageReward, and HPSv2 over the increment of computation in Fig. 8, along with the increment of sample number N. As shown, our method 77S- VAR has higher computational efficiency and surpasses Importance Sampling and Best-of-N with less than half TFLOPs.
24

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Methods
Global
Relation
Overall
SDv2.1
77.67
80.72
68.09
DALL-E
90.97
90.58
83.50
SDXL
83.27
86.76
74.65
PixArt-Sigma
86.89
86.59
80.54
SD3 (d=24)
84.08
HART
80.89
Show-o
67.48
Emu3
81.60
Infinity
80.96
89.83
81.72
InfinityB++BoN (NV = 4)
88.02
87.33
82.52
InfinityB++Ours (N = 83.37
88.81
82.94
Table 9: Evaluation results on DPG-Bench with = 4.
Best-of-N
Importance Sampling
Ours
0.7530
1.4995
0.312
0.74
ImageReward
0.310
GenEval
0.72
1.3
0.308
1.2
0.306
0.70
0.304
16a
10a
10a
10:
TELOPs
TFLOPs (b)
TELOPs
(a)
(C)
Figure 8: Performance over Flops. This figure shows the variant curves of different methods with computational consumption as the x-axis, demonstrating the efficiency our method.
Evaluation on Image Diversity
We follow [24] to adopt an evaluation on the CLIP latent space, which is more in accordance with the
T21 task. The metric CIJP-Div, as formulated below, with CL.PP encoder so and k samples {x] {xi calculates the difference between samples with the same prompt and directly measures the diversity on text-aligned features.
k
2 i=1 j=i k(k 1) [x(x)) fo(x3)
CLIP-Div ((x))
Though the metric is under a relatively small range, we can judge the diversity by comparing the relative differences between different methods, as shown in Table 10. Generally, with the growing N, the generated results gain higher possibility of convergence adjacent superior features, resulting in lower CLIP-Div score. However, the difference is quite small, indicating that method enhances the performance with little sacrifice diversity.
Method
N
N 2
N
N -
Importance Sampling
0.0828
0.0819
0.0822
0.0794
Best-of-N
0.0828
0.0815
0.0788
0.0791
Ours
0.0792
0.0788
0.0789
0.0779
Table 10: CLIP-Div scores under different sampling strategies and values of N.
25

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G
Ablation Study
G.1 Pipeline Ablation
We present the ablation study of different design components in the overall pipeline in Table 11. As adaptive batch sampling alone (without integrated sample selection mechanisms) cannot directly enhance generation performance, these cases are denoted by "-". Excluding these baseline cases, both clustering-basee diversity search and resampling-han baaend potential selection demonstrate performance improvements, with statistically significant gains observed in reward and related evaluation indicators.
Notably, the clustering approach yields relatively moderate improvements, which can be attributed to its primary function of maintaining structural diversity rather than actively identifying superior samples for subsequent generation. The combination of diversity maintenance through clustering and quality-based selection via resampling synergistically enhances the effectiveness of the pipeline. This dual-mechanism framework ultimately achieves substantial performance gains over the baseline system, with the resampling component playing the pivotal role in selecting high-quality candidates for iterative refinement.
Method
GenFval
ImageReward
HPS
CLIP
Aesthetic
Infinity
0.6946
1.1320
0.3042
0.3366
0.5811
+BoN
0.7087
1,2545
0.3069
0.3384
0.5813
+Adpptive Batch Sampling
+Clustering- Based Diversity Search
0.7220
1,2591
0,3072
0.3385
0,5816
+Resampling- Based Potential Selection
0.7403
1,4136
0.3106
0.3411
0,5821
Infinity
0.6946
1.1320
0,3042
0,3366
0,5811
+BoN
0.7244
1,3471
0,3083
0.3397
0.5820
+Adaptive Batch Sampling
+ -Clustering-Basee Based Diversity Search
0.7294 0.7437
1,3608
0,3095
0.3403
0.5824
RReammlingg Based Potential Selection
1,4605
0,3112
0.3414
0.5821
Table 11: Pipeline Ablation. This table shows gains from each design.
G.2 Computation-Perfommanc Tradeoff
We evaluate our method with = 2, on the GPU Nyidia A80-SXMM4-80BB Below is the ablation of different designs and corresponding gains in inference time and peak memory utilization. Note that we do not include image decoding time here, as this related to VAE and image size, which is irrelevant to the test-time scaling method itself. Because of the upper boundary of the 80G memory, BoN with = 8 requires two inferences and doubles the consumption.
N Method
GenEval
ImageReward Inference Time (s) Peak Memory (GB)
Infinity
0.6946
1.1320
1.15
20.34
Infinity
0.6946
1.1320
+BoN
0,7087
1.2545
1.70
28. 12
+Adaptive Batch Sampling
2,20
28.20
+Clustering
0.7220
1,2591
2,28
29.90
+Resampling (Ours)
0.7403
1.4136
2.51
30.73
4
Infinity
0.6946
1.1320
+BoN
0,7244
1.3471
2,84
43.68
+Adaptive Batch Sampling
3,99
43.87
+Clustering
0.7294
1,3608
4.12
45.57
+Resampling (Ours)
0.7437
1.4605
4.59
46.40
Infinity+BoN
0.7364
1.4144
5.70
43.68
Table 12: Computation-performanee tradeoff of different designs.
Our method takes less time and memory achieve comparable or even superior performance, for example, BoN with N' and ours with This proves the effectiveness and efficiency of TTS-VAR.
26

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G.3 Reward Models
We implement comparisons on using different reward models to rate the intermediate images and cal- culate the potential scores (VALUE), including Aesthetic [57], ImageReward [52], HPSv2 [56], and HPS+1JaggRoward. HPS+ImageRewwrd, we first calculate scores using the two models separately, then softmax the values of each model into the range [0, 1J. and finally take the average as the potential scores.
Owing to different value ranges of HPS and ImageReward, for
As shown in Table 13, generally, each reward model motivates an increase in the corresponding metric. For instance, with N = 4, the Aesthetic model, the ImageReward model, and the HPS model achieve the highest scores the associated indicators, respectively. Among different models, ImageRewaad eward promotes improvements more. Especially with N = 2, ImageReward demonstrates clear lead in GenEval and even defeats HPS HPS score. We attribute this to the ability clearly distinguish between superior and inferior samples, along with scoring that better aligns with human preferences.
Reward Model
GenEval
ImageReward
HPS
CT.PP
Aesthetic
0.7087
1.2545
0.3069
0.3384
0.5813
Aesthetic
0.6966
1.123
0.3054
0.3366
0.6004
2
IewarR
0.7403
1.4136
0.3106
0,3411
0.5821
2
HPS
0.7135
1.2246
0.3102
0.3391
0,583
2
IIPS+InageRewarl
0,7238
1,3522
0,3088
0.3402
0,5824
4
0.7244
1,3471
0,3083
0.3397
0.5820
Aesthetic
0.6842
1.1172
0,3056
0.3363
0.6114
IewaeReward
0.7437
1.4605
0.3112
0.3414
0.5821
4
HPS
0.7255
1,2812
0.3154
0.3402
0.5843
4
HeS+amageRewarl
0.7413
1,4128
0.3101
0.3406
0,5818
Table 13: Reward Model Ablation. This table shows results using different models for the potential.
G.4 Ablation on Clustering
We compare our clustering method with a random drop process in Table 14. As shown in the results, even though resampling is in place to help ensure image quality, the effectiveness of scaling is significantly diminished when clustering removed. This shows that clustering serves as an effective selection mechanism.
N Method
GenEval
ImageReward
HPS
CLIP
Aesthetic
1 w/o Clustering
InfinityBB+Ours 0.7253
1.3226
0.3084
0.3395
0.5822
0.7193
1.2740
0.3072
0.3391
0.5821
2 w/o Clustering
InfinityBB+Ours
0.7403
1.4136
0.3106
0.3411
0.5821
0.7271
1.3672
0.3094
0.3403
0.5821
Infinity2B+Our 0.7437
1.4605 1.4323
0.3112
0.3414
0.5821
w/o Clustering
0.7369
0.3103
0.3409
0.5830
Table 14: Ablation study on clustering.
G.S A Setting
In Table 15, we exhibit the results using different temperature À in the resampling process with fixed clustering operations. Intuitively, higher temperature promotes the expression of intermediate states with higher potential scores and prevents superior samples. However, excessively high temperatures can also widen the gap between intermediate states with the highest scores and those with scores that are only slightly lower. This can directly inhibit the generation of these slightly lagging intermediate states, which may ultimately become the optimal results. As shown, though there is steady increase in ImageReward, À 10.0 falls behind À = 5.0 with N 4 in GenEval.
27

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N
Lambda
GenEval
Imagekeward
HPS
CT.IP
Aesthetic
0.7087
1.2545
0.3069
0.3384
0,3813
0.1
0.7065
1.2458
0.3065
0.3387
0,5811
0.7210
1.3167
0.3080
0.3400
0,5819
0.7222
1.3459
0.3087
0,3403
0.5821
5.0
0.7361
1,4010
0.3101
0.3410
0.5821
10.0
0.7403
1,4136
0.3106
0,3411
0,5821
50.0
0.7350
1.4132
0.3103
0.3410
0,5818
100.0
0.7244
1.3663
0.3087
0.3401
0,5809
1.3471
0.3083
0.3397
0,5820
0.7308
1.3576
0.3089
0.3400
0,5816
0.5
0.7347
1.3918
0.3094
0.3406
0.5819
1.0
0.7418
1,4097
0.3099
0.3407
0.5820
5.0
0.7465
1.4500
0.3108
0.3412
0,5820
10.0
0.7437
1.4605
0.3112
0.3414
0.5821
1.4629
0.3112
0.3413
0,5818
100.0
0.7394
1.4283
0.3103
0.3408
0.5818
Table 15: À Ablation. This table shows results with different lambda values.
H More Visualization Results
We present pairs of results on GenEval Fig. to compare quality Infinity, Infinity-IS, Infinity-BoN, and Infinity-7SS-VA, -ARR. These cases are sampled from text prompts in GenEval, which include the single object, two objects, counting, colors, position, and color attributes. As shown, our method produces higher-quality samples for the single object, such the airplane in the left second line, effectively avoiding the generation of artifacts. In two-object settings, 77S- VAR successfully distinguishes references to different objects and generates accurate outputs based prompts. For example, in the right second line, eliminates conceptual mixtures and object disappearances. As illustrated in lines 3-10 the right side, 77' VAR also excels determining counting numbers, positional relationships, and color attributes. Notably, in the last right line, our method generates counterintuitive "green carrot," demonstrating its ability to separate objects from their natural attributes.
I Discussion on the Efficiency of Investing in Different Architectures
The efficiency of scaling across different model architectures remains an open question. In this section, share our perspective the potential benefits and limitations of scaling alternative architectures, such as VAR, in comparison to diffusion models.
First, different architectures possess distinct characteristics and advantages. Diffusion models, with their progressive denoising process and ability to model continuous latent spaces, generally achieve superior generative quality. By contrast, the VAR architecture more closely resembles the structure of large language models, which may be more conducive to unifying generation and understanding tasks [62]. Thus, depending the specific objective, scaling efforts targeted given architecture may yield complementary benefits.
Second, we believe that current scaling strategies for diffusion models are still at an early stage. In contrast to the significant improvements demonstrated by our approach over Best-of-N (BoN), the search strategy introduced in [22] provides only marginal improvements over Random Search. We hypothesize that the multi-step denoising process inherent diffusion models substantially enlarges the search space, thereby necessitating more sophisticated methodological designs. As a result, it remains difficult, based on current evidence, to make a definitive judgment on which scaling strategy or architectural choice is superior.
Societal Impact
When applied to VAR models, TTS-VAR enhances the alignment of generated images with textual descriptions, making the generation process more controllable and better suited to meet creative and production demands. However, we also acknowledge the potential for misuse of this method, which could lead privacy and copyright concerns. Nonetheless, we believe that our in-depth research into the VAR generation process will help researchers gain clearer understanding, advance studies
28

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Infinity-
Infinity BoN
Infinity TTS-VAR
Infinty in
Infinity- BoN TTS-VAR
Inf inity
Infinity
IS
is
Prompt: photo of a hot dog
Prompt photo of bus and computer keyboard
Prompt: photo of airplane
Prompt: photo of person and clock
8
Prompt: photo of umbrella
Prompt photo two books
Prompt: photo of remote and kite
Prompt: photo three
Prompt: photo a sheep backpack
Prompt: photo three ties
Prompt: photo of a scissors and stop sign
Prompt: photo of computer keyboard of a fork
SNOP db
Prompt: photo of laptop and a bear
P Prompt: a photo cell phone above carrot
Prompt: photo of a person and sink
Prompt photo of white book and red car
Prompt: photo of dog and scissors
Prompt: photo of orange cake and brown pizza
Prompt: photo of zebra and an orange
P Prompt photo of green carrot and blue toaster
Figure 9: More Visualization Results. Samples generated from GenEval prompts, with "IS" meaning Importance Sampling, "BoN" meaning Best-of-N, and our method 77'- VAR. We display various cases, including the single object, two objects, counting, colors, position, and color attributes.
29

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controllability and safety in generation, and ultimately ensure that image generation models become safe and manageable tools.
K Limitation and Future Work
Though TTS-VAR shows significant improvement over the baseline and sets record, it still has two main limitations. First, TTS- VAR does not completely address the misalignment between text prompts and generated images. As indicated by the scores in Table 7, there are still some failure cases, particularly in the Position item. Second, while 77S- VAR is based on a general coarse-to fine process, its potential application other coarse-to-fine models, such autoregressive models that use 1-D tokenizers, remains unexplored. more thoroughly, examining the reasons for failure and designing solutions unlock further scaling potential. Additionally, we plan to assess the compatibility of 77S-VAR with other autoregressive coarse-to-fine models, including those utilizing 1-D tokenizers and hybrid architectures that combine diffusion models. These efforts aim to create a more robust scaling framework for text-to-image synthesis while enhancing the methodological transferability of coarse-to- fine paradigms.
the future, we will investigate the generation process
30


Reviews:
[{"review_id": "JowYiHaGgB", "reviewer": "Reviewer_wUiv", "summary": "The article introduces a novel test-time scaling framework (TTS-VAR) for autoregressive models in the vision domain. The method proposed is able to take any trained text-to-image model and apply multi-scale path search algorithms and use the extra time to improve output quality by making batch sizes adaptive, employing clustering early for diversity and potential-based resampling once images are almost complete. Extensive empirical evidence suggests that TTS-VAR is able boost performance using few fewer parameters without touching the training process.", "questions": "1. Could you provide include more base-models on extended number of benchmark datasets to validate the generation of the proposed framework?\n2. Would it be possible to provide concrete GPU times used on top of flops/memory usage to validate the practical improvements of TTS-VAR? Would be also nice to see how 60% deduction in parameters compared to SD-3 translate to real gains on inference efficiency (memory, time etc).\n3. Could you provide a small set of human feedbacks on how TTS-VAR performs compared to baselines (such as best of n) on top of qualitative benchmarks?\n4. Could you provide detailed hyperparameters used in the studies including but not limited to K (in K-mreans), seeds for experimentation and PCA energy threshold for reproducibility purposes?", "limitations": "N/A", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Empirical gains are quite solid on GenEval 0.694 → 0.753 at N = 8 and already beats Best-of-N-8 with just N=2 while using 60% fewer parameters than SD-3. \n2. The problem presented is well-motivated since test-time scaling has seen tremendous success in the text domain. The intuition for TTS-VAR makes sense and I think achieving SD-3 level quality without retraining could unlock a wide range of real-world applications with costs significantly reduced. \n3. In general, the methodologies are clear to follow. Motivations for novel techniques such as adaptive batching, potential resampling and clustering are clearly motivated and formulated. \n4. Ablations are comprehensive on the design choices of potential functions (table 3), resampling scales (table 2), clustering scales (table 4) and clustering extractors (table 5).\n\nConcerns:\n1. A problem I have with this paper is the lack of evaluation scope since only infinity-2B is used as the base model and GenEval as the sole benchmarking metric.\n2. Figure 2 only presented Flop and memory information but does not include the type of GPU used and actual GPU hrs used to run the experiments.\n3. There is limited disclosure of hyper parameters used in the studies including but not limited to K (in K-mreans), seeds for experimentation and PCA energy threshold.", "was_revised": true, "final_justification": "The authors responses were extremely comprehensive and addressed most of my concerns.", "rebuttal": "We sincerely thank the reviewer wUiv for positive feedback and insightful comments, and will add your advice in the camera-ready version. We are particularly encouraged that you found our work to be well-motivated and recognized its potential to unlock a wide range of real-world applications. We answer your questions below.\n\n&nbsp;\n\n## **1. [W1, Q1] More Results for validation**\n\n### **1.1 Other Model**\n\nOur test-time scaling framework is designed based on VAR-based Text-to-Image (T2I) models. Since Infinity is currently the only provider of an open-source T2I model with a VAR architecture, in addition to the 2B Infinity model used as base model in the paper, we selected another latest **8B model (resolution 512)** to validate the effectiveness of the framework. Specifically, since the generation process for the 512x512 resolution involves only 10 scales, we use an adaptive batch size schedule of {8**N**, 8**N**, 6**N**, 6**N**, 6**N**, 4**N**, 2**N**, 2**N**, 1**N**, 1**N**}. Clustering is applied to the scales in the range **[2, 5]**, and resampling is performed on the scales in the range **[6, 8]**.\n\nThe experiments were conducted using the same settings as in Table 1 of the main submission and Table 6 in the Appendix. The results are shown in the table below.\n\n|$N$|**Strategy**|GenEval|ImageReward|HPS|CLIP|Aesthetic|\n|-|-|-|-|-|-|-|\n|1|Raw Inference|0.7646|1.2095|0.3081|0.3388|0.5721|\n|1|Ours|**0.7931**|**1.3929**|**0.3115**|**0.3414**|**0.5727**|\n||||||||\n|2|Importance Sampling|0.7834|1.3091|0.3097|0.3399|0.5716|\n|2|Best-of-N|0.7880|1.3435|**0.3127**|**0.3423**|0.5722|\n|2|Ours|**0.7985**|**1.4572**|0.3106|0.3411|**0.5730**|\n||||||||\n|4|Importance Sampling|0.7901|1.3786|0.3110|0.3409|0.5722|\n|4|Best-of-N|0.7995|1.4092|0.3122|0.3397|**0.5730**|\n|4|Ours|**0.8189**|**1.5100**|**0.3139**|**0.3425**|0.5727|\n\nAs shown in the table, our framework also achieves excellent results on this model. Notably, when $N=4$, our method even surpasses the 0.80 threshold, delivering exceptional performance. We look forward to the emergence of more text-to-image models based on the VAR architecture, which will enable us to further validate our approach in the future.\n\n### **1.2 Other Benchmarks**\n\nRecognizing the limitations of a single benchmark in fully assessing a method's effectiveness, we performed further evaluations on the **Infinity-2B** model using  **T2I-Compbench++** [1], a prominent benchmark in text-to-image generation. The corresponding results are detailed in the following table.\n\n|Model|Avg.|Color  (B-VQA)|Shape  (B-VQA)|Texture  (B-VQA)|2D Spatial  (UniDet)|3D Spatial  (UniDet)|Numeracy  (UniDet)|Non-spatial  (S-CoT)|Complex  (S-CoT)|\n|-|-|-|-|-|-|-|-|-|-|\n|Stable XL|0.5255|0.5879|0.4687|0.5299|0.2133|0.3566|0.4988|0.7673|0.7817|\n|Pixart-α-ft|0.5583|0.6690|0.4927|0.6477|0.2064|0.3901|0.5032|0.7747|0.7823|\n|Stable v3|0.6314|0.8132|0.5885|0.7334|0.3200|0.4084|0.6174|0.7782|0.7919|\n|DALLE· 3|0.6168|0.7785|0.6205|0.7036|0.2865|0.3744|0.5926|0.7853|0.7927|\n|FLUX.1|0.6087|0.7407|0.5718|0.6922|0.2863|0.3866|0.6185|0.7809|0.7927|\n|||||||||||\n|Infinity|0.5688|0.7421|0.4557|0.6034|0.2279|0.4023|0.5479|0.7820|0.7890|\n|Infinity2B+IS ($N=8$)|0.5965|0.7746|0.5078|0.6501|0.2462|0.4194|0.6002|0.7803|0.7937|\n|Infinity2B+BoN ($N=8$)|0.6115|0.7950|0.5439|0.6886|0.2545|0.4205|0.6090|0.7870|0.7937|\n|**Infinity2B+Ours ($N=2$)**|0.6151|0.7887|0.5578|0.6858|0.2697|0.4286|0.6112|0.7853|0.7936|\n|**Infinity2B+Ours ($N=8$)**|0.6230|0.8073|0.5914|0.7121|0.2644|0.4302|0.6340|0.7880|0.7963|\n\nAs displayed, our approach yields substantial performance gains over the baseline Infinity model. Echoing the results on GenEval, our method with just $N=2$ outperforms BoN even when it uses $N=8$. While our method does not surpass SD3 on this benchmark, it significantly narrows the performance gap, achieving this result on a considerably smaller model.\n\n&nbsp;\n\n## **2. [W2, Q2] Computation-Performance Tradeoff of Different Designs**\n\nWe evaluate our method with $N=2,4$ on the **GPU Nvidia A800-SXM4-80GB**. Below is the ablation of different designs and corresponding gains in inference time and peak memory utilization. Because the upper boundary of the 80G memory, BoN with $N=8$ requires two inferences and doubles the consumption.\n\n|$N$|Method|GenEval|ImageReward|Inference Time (s)|Peak Memory (GB)|\n|-|-|-|-|-|-|\n|1|Infinity|0.6946|1.1320|1.15|20.34|\n|||||||\n|2|Infinity|0.6946|1.1320|||\n||+BoN|0.7087|1.2545|1.70|28.12|\n||+Adaptive Batch Sampling|-|-|2.20|28.20|\n||+Clustering|0.7220|1.2591|2.28|29.90|\n||+Resampling (Ours)|0.7403|1.4136|2.51|30.73|\n|||||||\n|4|Infinity|0.6946|1.1320|||\n||+BoN|0.7244|1.3471|2.84|43.68|\n||+Adaptive Batch Sampling|-|-|3.99|43.87|\n||+Clustering|0.7294|1.3608|4.12|45.57|\n||+Resampling (Ours)|0.7437|1.4605|4.59|46.40|\n|||||||\n|8|Infinity+BoN|0.7364|1.4144|5.70|43.68|\n\nOur method takes less time and memory to achieve comparable or even superior performance, for example, BoN with $N=4$ and ours with $N=2$. This proves the effectiveness and efficiency of TTS-VAR.\n\n|Model|Model Size|Resolution|Step (Scales)|Inference time (s)|Memory (GB)|GenEval|\n|-|-|-|-|-|-|-|\n|SD3 (d=38)|8B|1024$\\times$ 1024|28|13.24|31.10|0.74|\n|Infinity+Ours ($N=2$)|2B|1024$\\times$ 1024|13|2.51|30.73|0.7403|\n\nWe display the comparison between Infinity+Ours ($N=2$) and SD3, because of similar memory consumption and GenEval score under the same resolution. As shown above, a 2B Infinity model with our test-time scaling achieves similar performance as an 8B SD3 model with less inference time (2.51s vs 13.24s).\n\n&nbsp;\n\n## **3. [Q3] Human Feedback**\n\nTo gather user feedback, we selected a small set of 15 image groups and compiled them into a questionnaire on Google Forms. For each group, the images are randomly shuffled, and participants were asked to select the best generated image based on three criteria: **Image Quality**, **Image Rationality**, and **Consistency with the Prompt**. In total, we collected 21 completed questionnaires, resulting in 315 sets of survey data for each indicator. The percentages of votes received by each approach are summarized in the table below.\n\n|Metric|Baseline|IS|BoN|Ours|\n|-|-|-|-|-|\n|Image Quality|13.3%|7.9%|13.3%|65.4%|\n|Image Rationality|13.7%|8.6%|8.6%|69.2%|\n|Consistency with the Prompt|1.3%|1.9%|2.5%|94.3%|\n\nAs demonstrated, the results generated by our method received the most votes across all three criteria, highlighting its effectiveness, particularly regarding its consistency with the prompt.\n\n&nbsp;\n\n## **4. [W3, Q4] Hyperparameters**\n\nIn K-Means, we just set the next batch size $b_{i+1}$ in the adaptive descending batch sizes as the number of centers, to find $b_{i+1}$ different structure categories. For PCA, we only select the first major component, as we utilize it for dimension reduction only. In all benchmarks, we use seeds from `0` to `4` for different results. For experiments requiring multi-GPU, like $N=8$, we set the seed from `100*process_index` to `100*process_index+4` for each device.\n\nAbout other parameters related to Infinity, we just keep them the same as the original public settings.\n\n&nbsp;\n\n**Reference**\n\n[1] T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generation. Huang, Duan, et al."}, {"review_id": "312MkNrj6N", "reviewer": "Reviewer_XWDr", "summary": "This paper presents TTS-VAR, a test-time scaling framework designed for visual autoregressive models by formulating generation as a path search task. To maintain computational efficiency, the authors introduce an adaptive descending batch size schedule, which operates similarly to an ensemble method with pruning to achieve a balance between performance and cost. For selecting high-quality candidate images, the method proposes a clustering-based diversity search to preserve structural information at coarse scales, followed by a reward model-based potential resampling to refine details at finer scales. Experimental results demonstrate that TTS-VAR improves generation quality.", "questions": "1. I suggest the authors include a detailed table comparing the time/memory cost of the method against each variant and baseline. Since this paper focuses on testing-time scaling behaviors, such a comparison is necessary.\n2. Why use \"clustering\" instead of \"pruning\" or \"selection\"? The choice seems somewhat counter-intuitive.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### Strengths:\n\nThe authors make the first attempt to explore test-time scaling for visual autoregressive models to improve performance, which appears to be an interesting and promising direction.\n\n\n### Weaknesses:\n\n1. **Contribution of adaptive batch sampling:**\n    \n    In Fig. 2, I suggest the authors include additional curves showing memory usage and computational complexity with increasing scale index while using fixed but larger batch sizes (e.g., 2, 4, 8) for comparison. The current figure only illustrates the quadratic growth in computation due to increasing tokens but does not clarify the dependency on batch size. This weakens the motivation for adaptive batch sampling and clustering-based search.\n    \n2. **Comparison with Best-of-N (BoN):**\n    \n    The comparison in Fig. 1 between (b) and (c) seems unfair. BoN operates on a batch size of 2, while TTS-VAR starts with 8 initial samples, followed by clustering and resampling. Intuitively, BoN with resampling (batch size = 8) should perform at least as well as the current method, if not better, since no filtering is required. If a fixed batch size of 8 is feasible, why constrain the approach with decreasing batch sizes? The authors should provide a deeper discussion of this alternative, clarify how their method excels in efficiency, and present quantitative metrics to better evaluate the trade-offs.\n    \n3. **Design of clustering-based diversity search and resampling-based potential selection:**\n    - The clustering approach needs further justification. Why does proximity to a cluster center imply high-quality structure? Additionally, clustering with very few samples (e.g., <8) may not be meaningful. What happens if all candidate samples are of poor quality? More visualizations (e.g., feature comparisons between superior and inferior samples, clustering results of \"diverse\" outputs) would help clarify this.\n    - The reward model is used for resampling, but as noted in Supp. D.2, this overlaps with the model used for quantitative evaluation, potentially biasing the results. Leveraging this prior likely inflates performance metrics unfairly.\n4. **Missing experimental results:**\n    - Ablation of clustering-based diversity search: To validate clustering’s efficacy, the authors should include a baseline where N=8 samples are randomly dropped instead of clustered. The current experiments are insufficient.\n    - While most experiments follow the setting of Infinity, results on the DPG benchmark are missing.\n    - In Supp. Tab. 10, the authors should test λ > 10 to demonstrate that the current parameter choice is optimal.\n5. **Confusing notations:**\n    - L108: Replace i with k.\n    - L158: The subscript of the union symbol should be j, not *bᵢ*.\n    - L137-138: The definition of *N* is unclear and seems inconsistent with the batch size notation in evaluations (e.g., Tab. 1).", "was_revised": true, "final_justification": "The authors did provide additional clarifications and the extensive new experiments in the rebuttal, and most of my concerns have been addressed. I am raising my rating to **borderline accept**.", "rebuttal": "We thank Reviewer XWDr for the insightful feedback. We will incorporate the suggestions in the camera-ready version and appreciate the recognition of our work. Our responses follow.\n\n&nbsp;\n\n## **1. [W1] Contribution of Adaptive Batch Sampling**\n\nTo illustrate the motivation for adaptive batch sampling, we compare it with fixed batch sizes below. While our method has higher initial FLOPs and memory usage, its computational growth is limited, and peak memory does not exceed that of a fixed batch size of 1. This design expands the search space at a minimal cost and prevents out-of-memory (OOM) errors.\n\n- FLOPs (TFLOPS)\n\n|Method|scale1|2|3|4|5|6|7|8|9|10|11|12|13|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|**Fixed 1**|0.0062|0.0298|0.1244|0.3370|0.7151|1.566|3.078|5.441|8.843|14.89|24.34|37.95|62.15|\n|**Fixed 2**|0.0120|0.0600|0.2490|0.6740|1.430|3.132|6.156|10.88|17.69|29.79|48.69|75.91|124.3|\n|**Fixed 4**|0.0246|0.1193|0.4975|1.348|2.861|6.263|12.31|21.76|35.37|59.57|97.38|151.8|248.6|\n|**Adaptive**|0.0493|0.2386|0.9949|2.271|4.540|9.643|15.69|20.42|27.22|39.32|48.77|62.38|86.58|\n\n- Memory (GB)\n\n|Method|scale1|2|3|4|5|6|7|8|9|10|11|12|13|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|**Fixed 1**|7.40|7.40|7.43|7.45|7.51|7.85|8.20|8.18|8.65|11.29|10.88|12.87|16.31|\n|**Fixed 2**|7.41|7.43|7.48|7.49|7.63|8.22|9.06|8.98|9.94|15.18|14.55|18.65|25.12|\n|**Fixed 4**|7.42|7.44|7.52|7.62|7.83|9.04|10.62|10.59|12.46|23.09|22.09|29.98|42.89|\n|**Adaptive**|7.45|7.48|7.71|7.76|8.06|9.84|10.59|8.98|9.96|15.20|11.06|12.99|16.45|\n\nWe will incorporate the curves of other fixed batch sizes into the figure in the camera-ready version.\n\n&nbsp;\n\n## **2. [W2, Q1] Comparison with BoN & Computation-Performance Tradeoff**\n\n1. **The VRAM limit is a critical bottleneck in test-time scaling, especially in VAR models with quadratic memory growth.** A key challenge, therefore, is how to effectively expand the search space under severe memory constraints, which is also the primary motivation for our design of Adaptive Batch Sampling. While a BoN approach with $N=8$ should, in theory, perform greater than or equal to our method with $N=1$, it requires ~8x the VRAM. In practice, this leads to Out of Memory (OOM) errors even on an 80GB GPU (we used multi-GPU parallelism for our $N=8$ experiments).\n\n2. **In terms of efficiency, our method's time overhead is minimal compared to increasing BoN's sample count.**  We tested the time and corresponding memory footprint for each component on the NVIDIA A800-SXM4-80GB GPU. Owing to the OOM error, BoN with $N=8$ requires two inferences and doubles the consumption. As shown, our method not only surpasses BoN with $N=8$ when ours is with $N=2$ in performance, but also shows a significant advantage in both time and memory consumption.\n\n|$N$|Method|GenEval|ImageReward|Inference Time (s)|Peak Memory (GB)|\n|-|-|-|-|-|-|\n|1|Infinity|0.6946|1.1320|1.15|20.34|\n|||||||\n|2|Infinity|0.6946|1.1320|||\n||+BoN|0.7087|1.2545|1.70|28.12|\n||+Adaptive Batch Sampling|-|-|2.20|28.20|\n||+Clustering|0.7220|1.2591|2.28|29.90|\n||+Resampling (Ours)|0.7403|1.4136|2.51|30.73|\n|||||||\n|4|Infinity|0.6946|1.1320|||\n||+BoN|0.7244|1.3471|2.84|43.68|\n||+Adaptive Batch Sampling|-|-|3.99|43.87|\n||+Clustering|0.7294|1.3608|4.12|45.57|\n||+Resampling (Ours)|0.7437|1.4605|4.59|46.40|\n|||||||\n|8|Infinity+BoN|0.7364|1.4144|5.70|43.68|\n\n&nbsp;\n\n## **3. [W3] Design Discussion**\n\n### **3.1 Effectiveness of Clustering**\n\n**The primary objective of clustering is not to find higher-quality structures, but to preserve the diversity of crucial structural features**, especially when the batch size is reduced due to VRAM limits. This approach ensures that a broader range of possibilities is available for evaluation in the subsequent resampling stage, enabling the selection of the most promising samples from a more diverse pool.\n\nTake the intermediate process for the prompt, \"Three origami cranes in white, gold, and blue are perched on a black lacquered shelf above a blooming bonsai tree,\" as an example. During generation, issues such as incorrect numbers of cranes, repeated colors, or wrong color sequences may arise. We observed that clustering can efficiently group samples with similar flaws into one category while grouping the generally correct samples into others. This filtering preserves representative samples, ensures structural diversity, and maximizes the probability that a correct sample exists for the subsequent reward-based filtering.\n\nDue to the inability to submit images during the rebuttal phase, we will incorporate visualizations of this filtering process in the camera-ready version of the paper.\n\n### **3.2 Using Reward Model as Metric Indicator**\n\nWe acknowledge that using the reward score itself as an evaluation metric can indeed lead to a biased assessment. This is why we stated at the beginning of Section 5 that we use GenEval, which is an objective metric without bias on a specific indicator, as our main metric. We also report a detailed breakdown of its sub-metrics in the Appendix. For a more comprehensive evaluation, we also present results on another mainstream benchmark, **T2I-CompBench++** [1].\n\n|Model|Avg.|Color  (B-VQA)|Shape  (B-VQA)|Texture  (B-VQA)|2D Spatial  (UniDet)|3D Spatial  (UniDet)|Numeracy  (UniDet)|Non-spatial  (S-CoT)|Complex  (S-CoT)|\n|-|-|-|-|-|-|-|-|-|-|\n|Stable XL|0.5255|0.5879|0.4687|0.5299|0.2133|0.3566|0.4988|0.7673|0.7817|\n|Pixart-α-ft|0.5583|0.6690|0.4927|0.6477|0.2064|0.3901|0.5032|0.7747|0.7823|\n|Stable v3|0.6314|0.8132|0.5885|0.7334|0.3200|0.4084|0.6174|0.7782|0.7919|\n|DALLE· 3|0.6168|0.7785|0.6205|0.7036|0.2865|0.3744|0.5926|0.7853|0.7927|\n|FLUX.1|0.6087|0.7407|0.5718|0.6922|0.2863|0.3866|0.6185|0.7809|0.7927|\n|||||||||||\n|Infinity|0.5688|0.7421|0.4557|0.6034|0.2279|0.4023|0.5479|0.7820|0.7890|\n|Infinity2B+IS ($N=8$)|0.5965|0.7746|0.5078|0.6501|0.2462|0.4194|0.6002|0.7803|0.7937|\n|Infinity2B+BoN ($N=8$)|0.6115|0.7950|0.5439|0.6886|0.2545|0.4205|0.6090|0.7870|0.7937|\n|**Infinity2B+Ours ($N=2$)**|0.6151|0.7887|0.5578|0.6858|0.2697|0.4286|0.6112|0.7853|0.7936|\n|**Infinity2B+Ours ($N=8$)**|0.6230|0.8073|0.5914|0.7121|0.2644|0.4302|0.6340|0.7880|0.7963|\n\n&nbsp;\n\n## **4. [W4] Other Experimental Results**\n\n### **4.1 Ablation on Clustering**\n\nWe compare our clustering method with a random drop process in the table below. As shown in the table below, even though resampling is in place to help ensure image quality, the effectiveness of scaling is significantly diminished when clustering is removed. This shows clustering is an effective selection mechanism.\n\n|$N$|Method|GenEval|ImageReward|HPS|CLIP|Aesthetic|\n|-|-|-|-|-|-|-|\n|1|Infinity2B+Ours|0.7253|1.3226|0.3084|0.3395|0.5822|\n||w/o Clustering|0.7193|1.2740|0.3072|0.3391|0.5821|\n|2|Infinity2B+Ours|0.7403|1.4136|0.3106|0.3411|0.5821|\n||w/o Clustering|0.7271|1.3672|0.3094|0.3403|0.5821|\n|4|Infinity2B+Ours|0.7437|1.4605|0.3112|0.3414|0.5821|\n||w/o Clustering|0.7369|1.4323|0.3103|0.3409|0.5830|\n\n### **4.2 Results on DPG-Bench**\n\nWe evaluate the performance of the baseline Infinity model, the Best-of-N (BoN) method, and our approach on the DPG-Bench, specifically for the case where $N=4$. For each prompt, we generated four corresponding images for evaluation using seeds 0 through 3. The results are presented in the table below.\n\n|Methods|Global|Relation|Overall|\n|-|-|-|-|\n|SDv2.1|77.67|80.72|68.09|\n|DALL-E 3|90.97|90.58|83.50|\n|SDXL|83.27|86.76|74.65|\n|PixArt-Sigma|86.89|86.59|80.54|\n|SD3 (d=24)|-|-|84.08|\n|||||\n|HART|-|-|80.89|\n|Show-o|-|-|67.48|\n|Emu3|-|-|81.60|\n|||||\n|Infinity|80.96|89.83|81.72|\n|Infinity2B+BoN ($N=4$)|88.02|87.33|82.52|\n|**Infinity2B+Ours ($N=4$)**|83.37|88.81|82.94|\n\n### **4.3 $\\lambda$ > 10 Results**\n\nWe conduct an ablation study on $\\lambda \\in \\{50, 100\\}$ for both $N=2$ and $N=4$, with the complete results presented in the table below. The rows labeled $\\lambda=-$ represent the baseline results from BoN.\n\nA higher $\\lambda$ promotes selecting better intermediate states, but an excessive value (e.g., 100) can hinder performance by prematurely pruning potential samples. The optimal setting varies with $N$: $\\lambda=10$ performs best for $N=2$, whereas $\\lambda=50$ achieves slightly better results for $N=4$.\n\n|$N$|Lambda|GenEval|ImageReward|HPS|CLIP|Aesthetic|\n|-|-|-|-|-|-|-|\n|2|-|0.7087|1.2545|0.3069|0.3384|0.5813|\n|2|1.0|0.7222|1.3459|0.3087|0.3403|0.5821|\n|2|5.0|0.7361|1.4010|0.3101|0.3410|0.5821|\n|2|10.0|**0.7403**|**1.4136**|**0.3106**|**0.3411**|**0.5821**|\n|2|50.0|0.7350|1.4132|0.3103|0.3410|0.5818|\n|2|100.0|0.7244|1.3663|0.3087|0.3401|0.5809|\n||||||||\n|4|-|0.7244|1.3471|0.3083|0.3397|0.5820|\n|4|1.0|0.7418|1.4097|0.3099|0.3407|0.5820|\n|4|5.0|0.7465|1.4500|0.3108|0.3412|0.5820|\n|4|10.0|0.7437|1.4605|**0.3112**|**0.3414**|**0.5821**|\n|4|50.0|**0.7489**|**1.4629**|0.3112|0.3413|0.5818|\n|4|100.0|0.7394|1.4283|0.3103|0.3408|0.5818|\n\n&nbsp;\n\n## **5. [W5, Q2] Notations and Words**\n\nWe apologize for the incorrect notations on lines L108 and L158 and will correct them in the camera-ready version. Regarding the variable $N$ on lines L137-138, it represents the same as the $N$ in Table 1—the final number of generated samples. We will add a clarification on lines L137-138 to prevent any confusion.\n\nAs for using clustering instead of pruning and selection, this decision is based on our observation of structural features that emerge early in the generation process. We found that these features can be effectively extracted, and by using clustering, we can preserve a diverse set of representative structures. This, in turn, provides a richer pool of candidates for the subsequent selection phase. Furthermore, direct pruning and selection are difficult at these early stages due to the lack of consistency between the early intermediate images and the final outputs. For these reasons, we chose and named our design 'clustering'.\n\n&nbsp;\n\n**Reference**\n\n[1] T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generation. Huang, Duan, et al."}, {"review_id": "D6OqY773so", "reviewer": "Reviewer_4YxR", "summary": "The paper introduces TTS-VAR, a novel test-time scaling framework for Visual Auto-Regressive (VAR) models, addressing the challenge of enhancing image generation quality without retraining. VAR models generate images hierarchically from coarse to fine scales, but early-stage tokens are hard to evaluate, leading to suboptimal sample selection. TTS-VAR tackles this via:\n1. Clustering-based diversity search\n2. Resampling-based potential selection\n3. Adaptive descending batch size\nEvaluated on the text-to-image VAR model *Infinity*, TTS-VAR improves the GenEval score by 8.7% (0.69→0.75) and outperforms diffusion/AR baselines with 60% fewer parameters.", "questions": "**Questions:**\n\n- How does TTS-VAR framework affect the output distribution’s diversity? In [1], the paper demonstrates the FID and IS after scaling NFEs.\n- What’s the computation-performance tradeoff of different designs?\n- In table 3. and table 4., why does the proposed framework have limited improvements on HPS, CLIP and Aesthetic score?\n- The efficiency of investing compute between diffusion models and VAR models?\n\nReference\n\n[1] Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps. Ma, Nanye, et al.", "limitations": "The limitation discussions are sound and clear to me.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n- **Quality**:\n    - Rigorous experiments across various metrics (GenEval, ImageReward, etc.) demonstrate consistent gains.\n    - Ablation studies validate design choices (e.g., resampling scales, potential scores).\n- **Clarity**:\n    - Well-structured; figures (e.g., Fig 1, 5, 7) effectively illustrate the framework and scale-dependent behaviors.\n    - Limitations and societal impact acknowledged (Sec 6, Appendix).\n- **Significance**:\n    - First general test-time scaling method for VAR models, enabling efficient high-quality generation.\n    - Achieves SOTA results with lower computational costs (e.g., 25% samples vs. Best-of-N).\n- **Originality**:\n    - Novel integration of clustering (for structural diversity) and history-aware resampling tailored to VAR’s hierarchy.\n\n**Weakness:**\n\n- The paper does not provide quantitative or qualitative analysis of how the TTS-VAR framework affects output diversity. While the method emphasizes preserving structural diversity through clustering (Sec 4.2), there is no visualization comparing image variation between TTS-VAR and base Infinity model.\n- The paper inadequately examines the compute-performance tradeoffs inherent in its design choices. Specifically, there is no analysis of how resampling at different scales impacts both computational cost (FLOPs, latency) and final output quality.", "was_revised": true, "final_justification": "I thank the authors for their thorough and detailed rebuttal. They have addressed the main weaknesses pointed out in my initial review. My primary concerns were the lack of analysis on output diversity and the compute-performance trade-offs. The authors' response has resolved these issues, and I am maintaining my positive rating for the paper.", "rebuttal": "We appreciate the reviewer 4YxR’s constructive feedback and will add your advice to the camera-ready version. We are encouraged that you acknowledged the originality of our method and the rigorous experiments demonstrating consistent gains across various metrics. We answer your questions below.\n\n&nbsp;\n\n## **1. [W1, Q1] Output distribution diversity**\n\nThe concept of output diversity can be divided into two components. The first is the diversity of the final output results, which we term **Final Semantic Diversity**. The second is the structural diversity among the intermediate states during the inference process, which we refer to as **Intermediate Structural Diversity**.\n\n### **1.1 Final Semantic Diversity**\n\nIn reference [1], the authors test IS and FID scores under the Class-to-Image (C2I) task on ImageNet. Since IS is based on the classification of generated images, it is not suitable for the T2I task. In addition, FID requires the reference distribution of real images, which is rarely adopted in open-domain T2I tasks.\n\nTherefore, we follow [2] to adopt an evaluation on the CLIP latent space, which is more in accordance with the T2I task. The metric *CLIP-Div*, as formulated below, with CLIP encoder $f_\\theta$ and $k$ samples ${\\\\{x_0^i\\\\}}_{i=1}^k$, calculates the difference between samples with the same prompt and directly measures the diversity on text-aligned features.\n\n$$ \\\\text{CLIP-Div} \\\\left( \\\\{\\\\mathbf{x}\\_0^i\\\\}\\_{i=1}^k \\\\right) := \\\\sum\\_{i=1}^k \\\\sum\\_{j=i}^k \\\\frac{2}{k(k-1)} \\\\left\\\\|f\\_{\\\\theta}(\\\\mathbf{x}\\_0^i) - f\\_{\\\\theta}(\\\\mathbf{x}\\_0^j) \\\\right\\\\|\\_2^2 $$\n\nThough the metric is under a relatively small range, we can judge the diversity by comparing the relative differences between different methods, as shown in the table below. Generally, with the growing $N$, the generated results gain a higher possibility of convergence to adjacent superior features, resulting in a lower CLIP-Div score. However, the difference is quite small, indicating that our method enhances the performance with little sacrifice of diversity.\n\n|Method|$N=1$|$N=2$|$N=4$|$N=8$|\n|-|-|-|-|-|\n|Importance Sampling|0.0828|0.0819|0.0822|0.0794|\n|Best-of-N|0.0828|0.0815|0.0788|0.0791|\n|Ours|0.0792|0.0788|0.0789|0.0779|\n\n### **1.2 Intermediate Structural Diversity**\n\nWe have visualized the generation process and analyzed the role of clustering in filtering these structural features. We found that clustering effectively groups samples with similar structures, thereby selecting for different structural characteristics and increasing the likelihood that various samples advance to later filtering stages.\n\nTake the intermediate process for the prompt, \"Three origami cranes in white, gold, and blue are perched on a black lacquered shelf above a blooming bonsai tree,\" as an example. During generation, issues such as incorrect numbers of cranes, repeated colors, or wrong color sequences may arise. We observed that clustering can efficiently group samples with similar flaws into one category while grouping the generally correct samples into others. This filtering preserves representative samples, ensures structural diversity, and maximizes the probability that a correct sample exists for the subsequent reward-based filtering.\n\nDue to the inability to submit images during the rebuttal phase, we will incorporate visualizations of this filtering process in the camera-ready version of the paper.\n\n&nbsp;\n\n## **2. [W2, Q2] Computation-Performance Tradeoff of Different Designs**\n\nWe evaluate our method with $N=2,4$ on the **GPU Nvidia A800-SXM4-80GB**. Below is the ablation of different designs and corresponding gains in inference time and peak memory utilization. Because the upper boundary of the 80G memory, BoN with $N=8$ requires two inferences and doubles the consumption.\n\n|$N$|Method|GenEval|ImageReward|Inference Time (s)|Peak Memory (GB)|\n|-|-|-|-|-|-|\n|1|Infinity|0.6946|1.1320|1.15|20.34|\n|||||||\n|2|Infinity|0.6946|1.1320|||\n||+BoN|0.7087|1.2545|1.70|28.12|\n||+Adaptive Batch Sampling|-|-|2.20|28.20|\n||+Clustering|0.7220|1.2591|2.28|29.90|\n||+Resampling (Ours)|0.7403|1.4136|2.51|30.73|\n|||||||\n|4|Infinity|0.6946|1.1320|||\n||+BoN|0.7244|1.3471|2.84|43.68|\n||+Adaptive Batch Sampling|-|-|3.99|43.87|\n||+Clustering|0.7294|1.3608|4.12|45.57|\n||+Resampling (Ours)|0.7437|1.4605|4.59|46.40|\n|||||||\n|8|Infinity+BoN|0.7364|1.4144|5.70|43.68|\n\nOur method takes less time and memory to achieve comparable or even superior performance, for example, BoN with $N=4$ and ours with $N=2$. This proves the effectiveness and efficiency of TTS-VAR.\n\nBuilding upon Table 3 in the main submission, we have also included an analysis of the computation-performance tradeoff when applying resampling at different scales. As the results show, increasing the number of resampling steps incurs additional inference costs.\n\n|$N$|**Resampling Scale**|Inference Time (s)|GenEval|ImageReward|HPS|CLIP|Aesthetic|\n|-|-|-|-|-|-|-|-|\n|1|-|1.15|0.6946|1.132|0.3042|0.3366|0.5811|\n|||||||||\n|2|[6, 9]|1.78|**0.7133**|1.2572|0.3066|0.3379|0.5801|\n|2|[6, 8, 10]|1.84|0.7130|**1.2591**|0.3066|**0.3381**|0.5809|\n|2|[6, 7, 8, 9, 10, 11]|1.94|0.7114|1.2497|**0.3067**|0.3378|**0.5810**|\n|||||||||\n|4|[6, 9]|3.03|**0.7276**|1.3534|0.3082|**0.3398**|0.5817|\n|4|[6, 8, 10]|3.14|0.7247|**1.3592**|**0.3085**|0.3397|0.5822|\n|4|[6, 7, 8, 9, 10, 11]|3.33|0.7210|1.3558|0.3083|0.3398|**0.5830**|\n\n&nbsp;\n\n## **3. [Q3] Limited Improvements on Other Indicators**\n\nIn our view, these limited improvements can be attributed to two main factors.\n\n1. **Different metrics possess inherent biases due to their distinct training data.** Although most metrics (excluding Aesthetic) evaluate text-image alignment to some degree, they have different tendencies and priorities. This is evidenced by the inconsistent rates of improvement across various metrics. In the majority of our experiments, we exclusively used ImageReward to guide the selection process. This method, however, primarily favors cases that align with the preferences of the ImageReward model itself. Consequently, while we observed notable improvements on GenEval, which focuses purely on text-image consistency, the gains on other metrics with specific biases, such as HPS and CLIP, were less pronounced.\n\n2. **The metrics exhibit varying levels of numerical sensitivity.** As shown in Table 9 of the Appendix, even when using HPS as the reward for selection, the corresponding improvement in the HPS metric itself is not substantial. This is because, while all metrics are designed on the principle that a higher score indicates better quality, their specific numerical ranges and the magnitude of improvement represented by a score increase are not uniform across metrics.\n\nWe also introduced a user study on Google Forms, asking participants to select the best image from those generated by different methods based on three criteria: **Image Quality**, **Image Rationality**, and **Consistency with the Prompt**. This study consisted of 15 sample groups, and we collected 21 responses, resulting in 315 total votes. The percentages of votes received by each approach are summarized in the table below.\n\nAs shown, though some metrics did not show significant improvements, the results of the user study keenly capture the enhanced performance of our method across all three criteria, particularly in terms of consistency with the prompt.\n\n|Metric|Baseline|IS|BoN|Ours|\n|-|-|-|-|-|\n|Image Quality|13.3%|7.9%|13.3%|65.4%|\n|Image Rationality|13.7%|8.6%|8.6%|69.2%|\n|Consistency with the Prompt|1.3%|1.9%|2.5%|94.3%|\n\n&nbsp;\n\n## **4. [Q4] Efficiency of Investing in Different Architectures**\n\nOverall, the benefits of scaling on different model architectures are not yet clearly established. Our work aims to provide a feasible solution for the alternative architecture VAR, and we are willing to share our perspective on this matter.\n\n1. **Different architectures possess distinct characteristics and advantages.** Diffusion models, benefiting from their progressive denoising process and modeling of continuous latent spaces, tend to achieve superior generative quality. In contrast, the VAR architecture more closely resembles that of large language models, which may be more conducive to unifying generation and understanding, like [3]. Depending on the specific goal, scaling efforts on a particular architecture might yield distinct advantages.\n\n2. **We believe that current scaling strategies for diffusion models are still in their early stages.** In contrast to the significant improvements our method shows over Best-of-N (BoN), the search method used in reference [1] offers only a marginal improvement over Random Search. We hypothesize that this is because the multi-step denoising process in diffusion models creates a much larger search space, likely requiring more sophisticated methodological designs. Therefore, based on the current works, it is difficult to make a direct judgment on which approach is definitively superior.\n\n&nbsp;\n\n**Reference**\n\n[1] Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps. Ma, Nanye, et al.\n\n[2] A General Framework for Inference-time Scaling and Steering of Diffusion Models. Singhai, Horvitz, et al.\n\n[3] VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning. Zhuang, Xie, et al."}, {"review_id": "3yr8agEYZ9", "reviewer": "Reviewer_QM2g", "summary": "In the inference stage of VAR, this work employs semantic feature clustering in the early steps to maintain diversity in the sampling process. In the later steps, a reward model is used to compute potential scores and reorder candidate samples, thereby achieving computationally efficient test-time scaling.", "questions": "As the first TTS work for VAR architectures, I think it barely meets the acceptance criteria for NeurIPS, although I do have some concerns about this work (see weaknesses).\n\nThe authors adopt N=8 as the final setting, which requires starting inference with a batch size of 64. I am concerned that the performance improvement may be attributed to more sampling rather than the ingenuity of the proposed design.  Inference speed is also a factor that needs to be considered.  \n\nWhat is the performance when using only Clustering-Based Diversity Search during TTS?", "limitations": "NO.\nI do not find any potential negative societal impact in this work.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**  \n1. This work is the first general test-time scaling framework for VAR models.   \n2. The proposed TTS-VAR achieves effective improvements on the GenEval benchmark.\n\n\n**Weaknesses**\n1. I am concerned about the inference speed of the model. This work starts with a batch size of 8*N and employs additional components such as DINOv2, k-means clustering, and multi-time reward model evaluations. Would these factors significantly reduce the inference speed? If we increase the value of N in the BoN, is it possible to achieve similar performance to TTS-VAR at a comparable inference speed?  \n2. Geneval reflects the semantic consistency of generated images under simple prompts and cannot capture the quality of details in the generated images. The advantages of high-scale resampling do not seem to be effectively captured by Geneval.  \n3. In Tab. 2, higher resampling frequency appears to lead to worse performance. Could the authors provide a more in-depth analysis of this phenomenon?", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer QM2g for your insightful suggestions, and will add your advice to the camera-ready version. We appreciate your crucial questions, including inference efficiency, and we have provided analysis to address them below.\n\n&nbsp;\n\n## **1. [W1] Computation-Performance Tradeoff of Different Designs**\n\nWe evaluate our method with $N=2,4$ on the **GPU Nvidia A800-SXM4-80GB**. Below is the ablation of different designs and corresponding gains in inference time and peak memory utilization. Because the upper boundary of the 80G memory, BoN with $N=8$ requires two inferences and doubles the consumption.\n\n|$N$|Method|GenEval|ImageReward|Inference Time (s)|Peak Memory (GB)|\n|-|-|-|-|-|-|\n|1|Infinity|0.6946|1.1320|1.15|20.34|\n|||||||\n|2|Infinity|0.6946|1.1320|||\n||+BoN|0.7087|1.2545|1.70|28.12|\n||+Adaptive Batch Sampling|-|-|2.20|28.20|\n||+Clustering|0.7220|1.2591|2.28|29.90|\n||+Resampling (Ours)|0.7403|1.4136|2.51|30.73|\n|||||||\n|4|Infinity|0.6946|1.1320|||\n||+BoN|0.7244|1.3471|2.84|43.68|\n||+Adaptive Batch Sampling|-|-|3.99|43.87|\n||+Clustering|0.7294|1.3608|4.12|45.57|\n||+Resampling (Ours)|0.7437|1.4605|4.59|46.40|\n|||||||\n|8|Infinity+BoN|0.7364|1.4144|5.70|43.68|\n\nWhile these methods introduce some computational overhead, the impact is minimal. The adaptive batch size schedule, for instance, only adds a slight expense because it primarily increases the sample count when the sequences are short and the tokens are limited. Furthermore, by applying low-rank PCA (`pca_lowrank`) for dimensionality reduction before K-Means and utilizing a compact reward model on bf16, we effectively control filtering costs, ensuring that total resource consumption remains acceptable.\n\nCompared with BoN under similar time boundaries, for example, BoN with $N=4$ and Ours with $N=2$, there is a clear advance in GenEval Score, indicating the effectiveness and efficiency of our designs.\n\n&nbsp;\n\n## **2. [W2] Other Benchmark Results**\n\nWe acknowledge that GenEval's evaluation primarily focuses on text-image alignment and does not offer a comprehensive assessment of the overall generation quality. To provide a more complete result, in **Table 6** of the Appendix, we also report on metrics related to image quality, such as **ImageReward** and **HPS**, in addition to GenEval. These results are presented in the table below.\n\n|$N$|**Strategy**|GenEval|ImageReward|HPS|CLIP|Aesthetic|\n|-|-|-|-|-|-|-|\n|1|Raw Inference|0.6946|1.1320|0.3042|0.3366|0.5811|\n|1|Ours|**0.7253**|**1.3226**|**0.3084**|**0.3395**|**0.5822**|\n||||||||\n|2|Importance Sampling|0.7022|1.1941|0.3051|0.3374|0.5807|\n|2|Best-of-N|0.7087|1.2545|0.3069|0.3384|0.5813|\n|2|Ours|**0.7403**|**1.4136**|**0.3106**|**0.3411**|**0.5821**|\n||||||||\n|4|Importance Sampling|0.7116|1.2883|0.3067|0.3387|0.5815|\n|4|Best-of-N|0.7244|1.3471|0.3083|0.3397|0.5820|\n|4|Ours|**0.7437**|**1.4605**|**0.3112**|**0.3414**|**0.5821**|\n||||||||\n|8|Importance Sampling|0.7181|1.3657|0.3085|0.3395|0.5810|\n|8|Best-of-N|0.7364|1.4144|0.3103|0.3406|**0.5820**|\n|8|Ours|**0.7530**|**1.4995**|**0.3122**|**0.3420**|0.5810|\n\nFurthermore, we have incorporated test results from **T2I-Compbench++** [1]. This benchmark assesses attributes such as color and shape, which, to some degree, reflect the quality of the generated images.\n\n|Model|Avg.|Color  (B-VQA)|Shape  (B-VQA)|Texture  (B-VQA)|2D Spatial  (UniDet)|3D Spatial  (UniDet)|Numeracy  (UniDet)|Non-spatial  (S-CoT)|Complex  (S-CoT)|\n|-|-|-|-|-|-|-|-|-|-|\n|Stable XL|0.5255|0.5879|0.4687|0.5299|0.2133|0.3566|0.4988|0.7673|0.7817|\n|Pixart-α-ft|0.5583|0.6690|0.4927|0.6477|0.2064|0.3901|0.5032|0.7747|0.7823|\n|Stable v3|0.6314|0.8132|0.5885|0.7334|0.3200|0.4084|0.6174|0.7782|0.7919|\n|DALLE· 3|0.6168|0.7785|0.6205|0.7036|0.2865|0.3744|0.5926|0.7853|0.7927|\n|FLUX.1|0.6087|0.7407|0.5718|0.6922|0.2863|0.3866|0.6185|0.7809|0.7927|\n|||||||||||\n|Infinity|0.5688|0.7421|0.4557|0.6034|0.2279|0.4023|0.5479|0.7820|0.7890|\n|Infinity2B+IS ($N=8$)|0.5965|0.7746|0.5078|0.6501|0.2462|0.4194|0.6002|0.7803|0.7937|\n|Infinity2B+BoN ($N=8$)|0.6115|0.7950|0.5439|0.6886|0.2545|0.4205|0.6090|0.7870|0.7937|\n|**Infinity2B+Ours ($N=2$)**|0.6151|0.7887|0.5578|0.6858|0.2697|0.4286|0.6112|0.7853|0.7936|\n|**Infinity2B+Ours ($N=8$)**|0.6230|0.8073|0.5914|0.7121|0.2644|0.4302|0.6340|0.7880|0.7963|\n\nWe also introduced a user study on Google Forms, asking participants to select the best image from those generated by different methods based on three criteria: **Image Quality**, **Image Rationality**, and **Consistency with the Prompt**. This study consisted of 15 sample groups, and we collected 21 responses, resulting in 315 total votes. The percentages of votes received by each approach are summarized in the table below.\n\nIn addition to receiving high marks for text consistency, these results also demonstrate that our method holds a competitive advantage over other approaches in terms of image quality and image rationality.\n\n|Metric|Baseline|IS|BoN|Ours|\n|-|-|-|-|-|\n|Image Quality|13.3%|7.9%|13.3%|65.4%|\n|Image Rationality|13.7%|8.6%|8.6%|69.2%|\n|Consistency with the Prompt|1.3%|1.9%|2.5%|94.3%|\n\n&nbsp;\n\n## **3. [W3] Worse Performance with Higher Resampling Frequency**\n\nWe discuss this phenomenon in Section 5.3 of the main submission. As shown in Figure 5, increasing the frequency of resampling does not always lead to performance improvements. This is because, as illustrated in Figure 5(b), the images decoded during the VAR generation process may not be effective representatives of the final generated image's quality. Consequently, resampling at early stages can lead to the erroneous selection of low-potential samples, thereby limiting the upper bound of scaling.\n\n&nbsp;\n\n## **4. [Q1] Performance with only Clustering-Based Diversity Search**\n\n|$N$|Method|GenEval|ImageReward|HPS|CLIP|Aesthetic|\n|-|-|-|-|-|-|-|\n|2|Infinity|0.6946|1.1320|0.3042|0.3366|0.5811|\n||+BoN|0.7087|1.2545|0.3069|0.3384|0.5813|\n||+Adaptive Batch Sampling|-|-|-|-|-|\n||**+Clustering**|0.7220|1.2591|0.3072|0.3385|0.5816|\n||+Resampling (Ours)|0.7403|1.4136|0.3106|0.3411|0.5821|\n||||||||\n|4|Infinity|0.6946|1.1320|0.3042|0.3366|0.5811|\n||+BoN|0.7244|1.3471|0.3083|0.3397|0.5820|\n||+Adaptive Batch Sampling|-|-|-|-|-|\n||**+Clustering**|0.7294|1.3608|0.3095|0.3403|0.5824|\n||+Resampling (Ours)|0.7437|1.4605|0.3112|0.3414|0.5821|\n\nIn the table above, we present the ablation, including results of using only Clustering-Based Diversity Search for the cases where $N=2$ and $N=4$. Without resampling, we cannot filter intermediate states in the subsequent steps. Therefore, we cluster directly down to $N$ samples from the outset. Specifically, the adaptive batch size schedule used is {8**N**, 8**N**, 6**N**, 6**N**, 6**N**, 1**N**, 1**N**, 1**N**, 1**N**, 1**N**}.\n\nAs shown, the clustering process effectively selects diverse intermediate states, ensures sample diversity, and provides more possibilities for the final selection, resulting in a significant improvement over the standard Best-of-N (BoN) method.\n\n&nbsp;\n\n**Reference**\n\n[1] T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generation. Huang, Duan, et al."}]

Author rebuttal:

Meta-review: The final ratings for this paper are unanimously positive (one "Accept" and three "Boarderline Accept").

The AC agrees with the reviewers' positive evaluation.  The paper introduces TTS-VAR, a framework for test-time scaling of visual auto-regressive models. The main concerns shared by the reviewers are two folds. First, the initial experiments were limited in scope, focusing on a single base model and a primary benchmark, which raised questions about the method's generalizability. Second, for a paper focused on test-time efficiency, the initial draft lacked a detailed quantitative analysis of the compute-performance trade-offs. 

The authors addressed these concerns in their rebuttal. They demonstrated the framework's effectiveness on a larger 8B model and on additional benchmarks (T2I-CompBench++, DPG-Bench). Also, they provided breakdowns of inference time and memory usage for each component, offering a detailed analysis of the efficiency trade-offs. They also supplemented their quantitative results with a user study. The authors' rebuttal have convincingly resolved the initial weaknesses identified by the reviewers. 

Therefore, the AC recommends this paper for acceptance.
Decision: Accept (poster)