SSG: Scaled Spatial Guidance for Multi-Scale Visual Autoregressive Generation
Authors: Youngwoo Shin
Jiwan Hur
Junmo Kim
Venue: ICLR 2026 Poster (2026)
Keywords: Generative Model
Guidance
Next-Scale Autoregressive Generation
Information Theory
Primary area: generative models
TL;DR: We introduce SSG, a training-free inference guidance that realigns VAR generation to coarse-to-fine by adding scale-appropriate high-frequency residuals, significantly improving fidelity and diversity with negligible latency.

Abstract: Visual autoregressive (VAR) models generate images through next-scale prediction, naturally achieving coarse-to-fine, fast, high-fidelity synthesis mirroring human perception. In practice, this hierarchy can drift at inference time, as limited capacity and accumulated error cause the model to deviate from its coarse-to-fine nature. We revisit this limitation from an information-theoretic perspective and deduce that ensuring each scale contributes high-frequency content not explained by earlier scales mitigates the train–inference discrepancy. With this insight, we propose Scaled Spatial Guidance (SSG), training-free, inference-time guidance that steers generation toward the intended hierarchy while maintaining global coherence. SSG emphasizes target high-frequency signals, defined as the semantic residual, isolated from a coarser prior. To obtain this prior, we leverage a principled frequency-domain procedure, Discrete Spatial Enhancement (DSE), which is devised to sharpen and better isolate the semantic residual through frequency-aware construction. SSG applies broadly across VAR models leveraging discrete visual tokens, regardless of tokenization design or conditioning modality. Experiments demonstrate SSG yields consistent gains in fidelity and diversity while preserving low latency, revealing untapped efficiency in coarse-to-fine image generation. Code is available at https://github.com/Youngwoo-git/SSG.

Published as a conference paper at ICLR 2026
SSG: SCALED SPATIAL GUIDANCE FOR MULTI-SCALE VISUAL AUTOREGRESSIVE GENERATION
Youngwoo Shin' Jiwan Hur*
Junmo Kim
Korea Advanced Institute of Science and Technology
(yshin0917, jiwan hur, junmo. k kim)@kas st ac kr
SG
SSG
Without SSG
With SSG
Figure 1: SSG provides a training-free generation quality improvement for next-scale predic- tion models at negligible cost, yielding sharper detail, fewer artifacts, and preserved global coher- ence. Full input prompts and model specifications are Appx.
ABSTRACT
Visual autoregressive (VAR) models generate images through next-scale predic- tion, naturally achieving coarse-to-fin fast, high-fidelity synthesis mirroring hu- man perception. practice, this hierarchy can drift inference time, as limited capacity and accumulated error cause the model to deviate from its coarse-to-fine nature. We revisit this limitation from an information-thhoore deduce that ensuring each scale contributes high-frrquency content not explained by earlier scales mitigates the train-inference discrepancy. With this insight, we propose Scaled Spatial Guidance (SSG), training-free. inference-time guidance that steers generation toward the intended hierarchy while maintaining global co- herence. SSG emphasizes target high- fiequrrey signals, defined as the semantic residual, isolated from a coarser prior. To obtain this prior, we leverage a prin- cipled frequency-domain procedure, Discrete Spatial Enhancement (DSE), which is devised to sharpen and better isolate the semantic residual through frequency- aware construction. SSG applies broadly across VAR models leveraging discrete visual tokens, regardless of tokenization design or conditioning modality. Ex- periments demonstrate SSG yields consistent gains in fidelity and diversity while preserving low latency, revealing untapped efficiency in coarse-to-fin image gen- eration. Code available Wetpt:::s..... ht///gtttugge github...... com/ Youngwoo-gitteeeooooooooooo git/SsG
perspective and
"Equal contribution.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Visual Autoregressive (VAR) structured models generate images via sequence of discrete visual tokens in next-scale, coarse-to- fine paradigm, delivering highly competitive fidelity and diversity
at substantial throughput (Tian et al. [024] [Tng et al. 2025] Han et al.] 2025). Requiring only about dozen inference steps, these models offer an efficient and conceptually grounded approach
to visual synthesis that aligns with the hierarchical nature of human perception.
Improving -tr-structured models has been pursued along several axes: adding auxiliary refinement modules (Tang et al., 20255 Chen et al., 2025b; Kumbong et al. (205), modifying the transformer architecture for generation (Voronov et al. 2025), modifying tokenization (Qu et al., 2025 Han
et al. 2025). and replacing the native coarse-to-fine generation with flow matching (Ren et al.
Liu et al. 2025). While these approaches push the boundary of VAR-structured models, they typically require costly retraining and introduce overhead, undermining the efficiency that motivates the VAR paradigm. Furthermore, they are susceptible to train-inference discrepancy caused by error accumulation. While several methods have been proposed to mitigate this issue (Chen et al. 2025b
2025
Kumbong et al.
2025 Han et al. 2025), it remains a persistent challenge for VAR -tR-sttucttred models.
In this paper, re-examine next-scale prediction in VAR from information-theoreede perspective. Our analysis identifies a core principle that mitigates the train-inference discrepancy, Specifically, if each prediction step adds approorapee novel information not captured by the previous scale, it reduces informational redundancy. This raises a central question: How can we guide the model to add the intended novel information each step, realigning VAR with its coarse-to-fine nature?
To address this challenge, we propose Scaled Spatial Guidance (SSG), training-free guidance for VAR models with negligible overhead. SSG sets the target each step the semantic residual, the high-frequency detail specific to that scale. To isolate this residual from the coarse structure, we use a prior carrying that coarser structure from the preceding step. This prior constructed via Discrete Spatial Enhancement (DSE), frequency- across scales. Together, these components promote principled progression from coarse structure to fine detail. SSG applies across VAR models with discrete visual tokens, independent of tokenization and conditioning, and significantly improves fidelity without additional data or fine-tuning.
interpolation that preserves structural integrity
We evaluate SSG on strong VAR baselines with varied tokenization (Tian et al., 2024 Tang et al., 2025 Han et al.] (205)) achieving consistent gains on class- and rext-conditional generation. Across different VAR scales, applying SSG yields robust and competitive performance relative to recent diffusion Yaan et al. 20224] Hatamizadeh et al.) 20244; Peebles & Xic [023 Alpha-VLLM 2024 Dhariwal & Nichol, | Ho et al.] 2022) and masked models (Chang al. 2022
Liet al. 2024b).
while preserving the low latency of VAR architectures.
Our contributions are as follows:
We propose Scaled Spatial Guidance (SSG), training-fre free guidance that enforces a coarse-to to-fine hierarchy by prioritizing the generation of novel, high-frequency information each step.
We reinterpret VAR sampling from an information-theorrs theoretic perspective and analyze the per-step objective, identifying the optimal priority at each step for robust generation.
We demonstrate consistent improvements in both fidelity and diversity with negligible latency overhead, enhancing VAR-structured models for discrete visual generation.
METHODS
2.1 PRELIMINARIES: NEXT-SCALE AUTOREGRESSIVE GENERATION
The Visual Autoregressive (VAR) framework Tian et al. 2024 re- re-frames autoregressive visual generation from conventional "next-token prediction" " to a hicrarchical, coarse-to-fine "next-seale prediction." This approach operates
sequence of hierarchical token maps, (r1, ,2....,T7, (Esser et al. 2021 Lee et al. 2022 Tian et al. 2024), mirroring the human perceptual tendency resolve global structures before fine- mn-ggraied details.
image represented
Specifically, a feature map f E 1xxC is quantized into K discrete token maps, (ri...., Fxx Fx), of progressively finer resolutions. The generation of each map rkk E {1,..., h xwx is conditioned
2

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Published as a conference paper at ICLR 2026
Start Step
Step 7
Step 8
Step 9
Step 10
W/O SSS
Without SSG
0.50
With SSG
LPIPS(()
0.48
0.46
Wi SSG
0.44
Strp6
Step?
Stopä
Step@
Step10
Generation Step
Figure 2: Impact of SSG on Image Completion (VAR-d30). (Left) By amplifying the semantic residual, SSG enables the model to accurately reconstruct high-frequene details like the bird's ' beak (red box), unlike the baseline. (Right) Consistently better LPIPS substantiates this improvement.
on the preceding maps Tck (ri.....Tk-11 where V is the codebook vocabulary size. The base map, rı, contains the global context and is predicted from initial class or text tokens. The joint probability distribution is then factorized autoregressively across these scales:
p(ri, p(ri) II p(rx r=e)).
At each step k, model M generates a residual logit tensor 4 K xV conditioned on T<ck, which defines a categorical distribution at each spatial location from which т2 is sampled.
To synthesize image, the generative process builds a final feature representation from the token maps (ri,.... rx) via residual de- de-quantization and accumulation (Lee et al. 2022 Tian et al. 2024). At each step k, the token map r de-quantized into continuous residual feature map, 2/. using its corresponding codebook embedding. Each residual zμ is then upsampled to the target resolution by operator (-) and added to accumulated feature map: fx fk-1 (=x), with fo 0. Finally, the completed map JK is passed decoder produce the output image.
While powerful, the effectiveness of this multi-scale generative process requires the model to faith- fully learn the hierarchical structure of the token representation, such as that from a multi-scale VQVAE (Tian et al.) 2024). This representation is structured such that idcally each subsequent generative step & exclusively models a new, higher-frequene band of details. In practice, how- ever, limited model capacity prevents strict adherence to this hierarchical frequency separation. This deviation from the ideal behavior becomes a primary source of the train-inference discrepancy.
Consequently, the model often fails its designated role each inference step. Instead of introduc- ing novel, finer details, redundantly predicts foerrffryyeenc information already established in previous steps. This inefficient misallocation of model capacity, direct result of the train-inference discrepancy, leads to the structural degradation and spatial disorientation seen in the upper row of Fig. 2 Therefore, the central challenge is to guide the generative process at each step k to focus exclusively on synthesizing the novel, figher-frequency details appropriate for that step.
2.2 DERIVATION OF SCALED SPATIAL GUIDANCE
To analyze details added per step. we re-interpret VAR sampling variational optimization prob- lem via the Information Bottleneck (IB) principle (Tishby et al. 2000 Alemi et al. 2017). to derive principled guidance to enhance fidelity by mitigating train-inference discrepancy. The principle seeks a compressed representation X of an input X maximally informative about target Y:
CB min 1(x; (Y(;)), X
where 1 ((::-) denotes mutual information. For VAR's sequential generation, the IB principle is con- ceptually reversed: rather than compressing data, the goal at each step is to generate a residual zą adding new, finer details. Thus, the objective in Eq. (2) maximizes information about the final output fx while minimizing redundancy with the previous state fe-1, yielding the VAR-specific objective:
3

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Published as a conference paper at ICLR 2026
Discrete Spatial
Suntracion
Enhancement
prior
Next-Scale A uuoorerressive Transformer
4
Bx
Scaled Spatial Guidance (e
Sample
Decode
Lookup
Z/x
985 Daisy
Class or Text Token
Latent
Residual
Discrete Tokens
Embeddings
Logits
Figure 3: Overview of VAR-structured model with our Scaled Spatial Guidance (SSG) module.
At each step, the autoregressive transformer predicts residual logits, which SSG refines by using DSE-enhanced prior to isolate and amplify the high-frequency semantic residual before sampling.
LVAR-IB = 31 (xllf/ -
(3)
Expanding the conditional term via the chain rule mutual information yields:
CVAR-IB = max 3I ( fx) - (3 + (=
(4)
24
We further simplify this objective from frequency-domain perspective. By decomposing the output fK via ideal low-pass (L) and high-pass (H) filters into its low-frequency (L(fx) se fa-1) and high-freyuency (H (fx)) components, the objective reduces to an intuitive form:
LVAR-IB so max 3l ((x)) ((z; L(fk))
(5)
To translate Eq. ( into practice, we work at the logit level: the model samples a residual token rk from residual logits la. whose codebook embedding yields 2ę. We therefore construct an IB- inspired, Maximum Posteriori (MAP)-style surrogate with two complementary parts:
Target-informaaiivness term (3 I(z:: H(f))) promotes adding new, fine-scale detail. Here 4prior coarse reference carrying information from the previous step, and l' is the guided version of the step-k logits optimized for sampling. We encourage to follow our proxy for high seegurryy detail, the semantic residual A₁ = l1 -pprior via the dot-product surrogate 3 ( A
State-redundancy term (- [(x:: I(fx))) limits deviation from established coarse structure. In practice, use L2 proximity regularizer that keeps the guided logits l close to the step-k base logits lx. adding the quadratic proximity term 1 -x
Combining these yields an objective conceptually aligned with the log-posterior of a MAP formula- tion (Appx. C. Optimizing this objective over the guided logits admits a closed-form solution:
C((') = (C)
1| 111.
L' RVV.
(6)
where 4, E RD'l is the residual logits at step k: A. E R is the semantic residual, and B 2 0. This quadratic is strictly concave in (Hessian -T) with unique maximizer
ess
l + A
Letting 3 be stepwise, 3g, yields Scaled Spatial Guidance (SSG) (full derivation in Appx. D
SSG lx +
Br A
Lx + BR (lx Eprior).
(8)
'The coarse-state approximation and deterministic conditioning for the chain rule: Appx.
A
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Published as a conference paper at ICLR 2026
The scaling factor 35 controls the magnitude of the semantic residual Åk. trading off the injection of frequecyyy detail against preservation of Dase-model coherence. Empirically, SSG refines detailed structures (e.g., the bird's beak in Fig. 2 and yields consistently lower LPIPS across gen- eration steps (graph in Fig. 2 in line with emphasizing the fargetinfformatiennes term while sup- pressing the state-redundancy term in Eq. (5). Nonetheless, the effect depends the quality of the transported prior fprio:: if the prior is distorted, A can be misaligned and suppress essential detail. Thus, principled construction of fprirr is critical to realizing the full benefit of SSG.
Algorithm DSE formulation
Algorithm 2 SSG Formulation
1: Input: Previous logits fprevi target size Ss.
1: Input: Raw logits Ex: previous logits pprev Hyperparameter: Guidance scale B3g
2: Oufput: Upsampled prior pprior
3: if Eprev is not None then
3: Initialize: Guided logits eSSG
4: linterp < Interpolate((prrr Sk);
4: l: then
5: Lyrev f DCT((pprv))
5: k + Cu:
6:
L'meer + DCT (CT(((iner))) ((inerp)i
6: else
7:
i+ -Iinerp>
7:
ppror + DSE( (iz)((x)))
8:
LIO size(L Lieei
8:
AS f E lpriari
9:
Epesor + (L):
10: end if
9:
SSSG
E
+ lx + Bk Ass
10:
return Epriors
11: return /SSG k
11:
end if
2.3 PRIOR CONSTRUCTION IN THE FREQUENCY DOMAIN
We construct the prior Eprior from the previous step's logits 41-1. Because the hierarchy is relative, 4-11 encodes coarser, lower-frequency band than the details at step k. but its smaller spatial scale requires upsampling. Simple spatial interpolation is local and approximate: linear interpolation yields an overly smooth, attenuated prior, while nearest neighbor introduces blocky discontinuities and spurious high frequencies, contaminating the semantic residual. In contrast, frequency-dome domain construction leverages orthonormal discrete transforms to provide global, energy-preserey rep- resentation in which bands are independent and non-interfering. This independence affords two benefits: precise separation in the forward transform and exact, lossless reconstruction in the in- verse. As a result, coarse structure is preserved without distortion, enabling As to isolate the new information bandwidth required at step k.
To implement this, we introduce Discrete Spatial Enhancement (DSE), performing spectral fusion in the frequency domain. DSE first applies a discrete frequency transform to two signals: the original coarse logits ε and a simple upscaled version, ''merr'' The low-frequency coefficients of the transformed - serve as the ground-truth coarse structure, while the high-frequency coefficients of the transformed finter provide plausible extrapolation of new details. We then construct hybrid frequency spectrum by combining the low-frequency coefficients from the former with the high- frequency coefficients from the latter. Applying the inverse transform to this fused spectrum yields prior, Epeioe. that rigorously preserves the verbatim coarse structure from the original logits while incorporating a plausible high- high-frequency extrapolation. The full process is detailed in Alg. [ In our implementation, the Discrete Cosine Transform (DCT) serves as the discrete frequency transform.
2.4 EFFICIENT INFERENCE-TING IMPLEMENTATION
A key advantage of the SSG framework is its seamless integration into pretrained VAR-structured models at inference time. Our method operates directly on the residual logits, the pre-activation outputs defining discrete token probabilities. This makes it agnostic to the underlying model archi- tecture, requiring no modifications to model weights or the introduction of new branches. Conse- quently, it is broadly applicable to any VAR-structure models that generate images with discrete tokens. Furthermore, its effectiveness is independent of the specific number of generative steps or the resolutions used, ensuring robust performance across diverse model configurations.
5

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Published as a conference paper at ICLR 2026
The computational overhead of this framework is negligible. As detailed in Alg. 2 DSE lever- ages the raw residual logits cached from the previous step, avoiding any extra forward passes. The entire SSG mechanism, consisting of the DSE step and subsequent linear combination, can be implemented in just a few lines of code. With frequency domain operations adding only a minimal computational and memory cost, SSG enhances structural and semantic consistency while largely preserving the efficiency of the original pretrained model. This makes it practical tool for improv- ing both classcconditional and text-conditional generation without compromising on speed.
3 RELATED WORK
Autoregressive models build on VAEs (Kingma & Welling 2013) by modeling discrete image to-
kens from tokenizers such as VQ-VAE (Van Den Oord al. 2017) and VQGAN (Esser al. al.] 20211)
Maaskedprrediction variants improve quality but incur significant inference compute cost (Li et al.
2024b). VAR (Tian et al. 2024) shifts from next-token to next-scale prediction, with progress in
token design (hybrid (Tang et al. (025), bit-wise (Han et al. 2025)), architecture (Li et al. 2025
Chen et al. 2025b; Voronov et al. 2025), and flow-matching integration (Ren et al., 2025| Liu et al. 2025) Despite these refinements. core issue persists: train-inference discrepancy whereby finite-capacity VAR generators fail to reliably realize the coarse-to tossee-tt-fine hierarchy implied by multi- scale tokenization at inference. Recent methods reduce this gap via refinement mechanisms, where CoDe (Chen et al., 2025b) adds a collaborative refiner, HMAR (Kumbong et al., (025) performs multi-step masked prediction, and Infinity (Han et al., (2025) introduces bitwise self self-correction with redefined tokenization, yet they require model modifications and retraining, increasing memory us- age or latency. In contrast, SSG addresses the train-inference discrepancy at inference time: pro- motes scale-specific novel detail while preserving established coarse structure, aligning VAR with its coarse-to- to-fine hierarchy without architectural changes, additional data, or significant overhead.
Visual guidance improves generation by sharpening the predictive distribution akin to lowering temperature in language models, which reduces entropy and increases faithfulness at the cost of diversity (Tumanyan et al., (023). However, existing techniques incur distinct trade-offs. Classifier- free guidance (CFG) can miss fine spatial details (Ho & Salimans, 202T); auto-guidance requires second model (Karras et al.] 2024);; and autoregressive strategies like CCA require costly fine- tuning (Chen et al., 2025a).
separate family of diffusion controls, including SAG (Hong et al.) 2023). PAG (Ahn et al.) 2024), SDG (Feng al. 2023). and STG (Hyung et al. 2025), provides granular conditioning but is not tailored to the coarse- to to-fine structure of VAR frameworks and typi- cally adds extra inference steps, increasing latency. In contrast, we introduce training- free guidance tailored to VAR-structured models that uses no additional data and adds negligible overhead.
4 EXPERIMENTS
We evaluate SSG via four questions: (1) Does it improve VAR models across scales to be competitive
with other leading generative families? (Sec. 4.2) (2) Is it robust across advanced tokenization
schemes? (Sec. 4.3p (3) Does it enhance high-frequency detail, as motivated in Sec. 2.2 (Sec. 4.4)
(4) Is the frequency-domann domain DSE implementation effective, as discussed in Sec. 2.3 (Sec. 4.5
Table 1: Performance gains from SSG on VAR models across scales on ImageNet 256x256.
Model
FID,
sFID
ISt
Pret
Rect
#Para
#Step
Time
VAR-d16
3.42
8.70
275.6
0.84
0.51
310M
10
0.5
+SSG (Ours)
3.27
8.39
285.3
0.85
0.50
310M
10
0.5
VAR-d20
2.67
7.97
299.8
0.83
0.55
600M
10
0.6
+SSG (Ours)
2.49
7.60
305.2
0.83
0.56
600M
10
0.6
VAR-d24
2.39
8.18
314.7
0.82
0.58
1.0B
10
0.7
+SSG (Ours)
2.20
6.95
324.0
0.83
0.59
1.0B
10
0.7
VAR-d30
2.02
8.52
302.9
0.82
0.60
2.0B
10
1.0
+SSG (Ours)
1.68
8.50
313.2
0.81
0.62
2.0B
10
1.0

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Published as a conference paper at ICLR 2026
Table 2: Visual Generative model comparison on ImageNet 256 256 benchmark. Metries in- clude Fréchet inception distance (FID), inception score (IS), precision (Pre), and recall (Rec). Model parameters (#Para), inference steps (#Step), and inference time relative VAR-d30 are reported. f:
Taken from VAR Tian et al. 2024). :: Taken from HART (Tang et al. 2023 $: Reproduced.
Type
Model
FID
1S1
Pret
Rect
#Para
#Step
Time
GigaGANt Kang et al. StyleGAN-XLT (Sauer al.
2023
3.45
225.5
0.84
0.61
569M
1
GAN
2022
2.30
265.1
0.78
0.53
166M
0.3
LDM-4-Gf Rombach el al.
2022
3.60
247.7
400M
250
DiT-XL/2f (Peebles & Xie
2023)
2.27
278.2
0.83
0.57
675M
250
45
Diff.
L-DiT-7B+ (Alpha- VLL.M
2024
2.28
316.2
0.83
0.58
7.0B
250
45
DusuSSM-XL Yan al.
[2024
2.28
259.1
0.86
0.56
660M
250
DiffiT (Httamizadeh et al.
2024)
1.73
276.5
0.80
0.62
561M
250
MaskGIT
(Chang et al.
2022)
6.18
182.1
0.80
0.51
227M
8
0.5
Mask.
M: Li et al. 2024b
2.31
281.7
208M
64
10.0
MAR H
Li et al.
2024b
1.78
296.0
479M
64
13.4
VQGAN= (Esser et al.
2021
18.65
80.4
0.78
0.26
227M
256
19
RQTransfomerr (Lee el al. 2022)
7.55
134.0
3.8B
68
21
AR
GIVT-Causal-L+A schhanne el al.
2024
2.59
0.81
0.57
304M
256
LlamaGen-3B (Sun al. 2024
2.18
267.7
0.54
3.1B
VAR-CoDe N=9 (Chen et al.
2025b
1.94
296
0.80
0.61
2.3B
10
HMAR-d30 (Kumbong el al. VAR-d30$ (Tian of al. 2024
2025)
1.95
334.5
0.62
2.4B
VAR
2.02
302.9
0.82
0.60
2.0B
10
1.0
+SSG (Ours)
1.68
313.2
0.81
0.62
2.0B
10
1.0
4.1 EXPERIMENTAL SETTINGS
We evaluate plass-conditional ImageNet generation at 256 x 256 and 512 512 (Deng al. 2009).
primarily using Fréchet Inception Distance (FID) (Heusel et al., 2017 to assess fidelity and diversity, along with Inception Score (IS) (Salimans al. 2016) and spatial FID (sFID) (Nash et al. 202
For text-to-image (T2I), we use the MJHQ-30K benchmark (Li et al. 2024a) and assess semantic fidelity and prompt alignment with FID (Heusel et al., (017)) and CLIPScore (Hessel et al. 202T) We also report inference latency to quantify SSG's computational overhead across all models.
Our analysis focuses on VAR-struc Astruuuut models, which exemplify the next-scale paradigm (Tinn ct al. 2024| Tang et al. 2025: Han et al. 2025). To contextualize Tab. 2 we also compare against leading models from other paradigms: high-fidelity diffusion (Di+uSSM-XX -L-G (ann et al. 2024).
DiffiT (Hatamizadeh al. 2024)), GANs (StyleGAN-XL (Sauer et al. 2022 autoregressive
(LlamaGen-3B (Sun et al. 2024)), and masked models (MAR-H (Li et al. 2024b)).
For fair comparison, report metrics with CFG enabled whenever supported. Reproducibility of VAR-family checkpoints posed challenges: for VAR (Tia et al.] [024). the released weights underperform the results reported in the paper; for HART (Tang et al. (025), public discussions note difficulty matching reported scores; and Infinity lacks official MJHQ-30K results. Thus, we re-evaluated all VAR baselines on single NVIDIA A6000 under unified protocol, and all gains are measured by applying SSG to these runs under identical settings. The SSG strength follows linear decay, 3x = 31 k-1 (Sec. 2.2 where is the initial scale and K the number of steps.
4.2 TRAINING-FREI ENHANCEMENT OF NEXT-SCALE GENERATIVE MODELS
Evaluating SSG across scaled VAR models reveals consistent performance gains that am- plify with model capacity (Tab. T.. On class-conditional ImageNet 256 256, the FID duction grows from 0.15 for VAR-d16 to a substantial 0.34 for the larger VAR-d30. Cru- cially, these improvements are achieved without altering model parameters or increasing in- ference latency. This confirms SSG achieves a scalable enhancement, improving with the representational power of the base model. This scaling trend culminates in our re- sult on VAR-d30 (Tab. D where SSG achieves an FID of 1.68, surpassing competi- tors including DiffIT (1.73 at 256 steps) and MAR-H (1.78 at steps; 13.4x slower).

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Published as a conference paper at ICLR 2026
While methods like HMAR-d30 achieve higher IS through costly retraining and archi- tectural modifications. SSG improves the base- line IS without modification to the pretrained model. This demonstrates the primary strength of SSG: achieving superior fidelity with signifi- cant efficiency by enhancing, not replacing, the original model.
Table ImageNet 512 512 conditional gener- ation. Inference times are relative to VAR-d36. t: Quoted from VAR. I: Estimated via linear scaling of steps (4x) and pixels (4x) ) from the reported time of the 2 model. S: Reproduced.
Type GAN BigGANf
Model
FID4
IST
Time
8.43
177.9
The effectiveness SSG extends to 512x512
DiT-XL/2f
3.04
240.8
81
resolution (Tab. 3). where it improves VAR- d36, reducing FID by 11.5% 2.39 while in- creasing IS by 10.3% to class-leading 320.6.
Diff.
DuRuSSM-XL-G
3.41
255.1
DiffiT
2.67
252.1
MaskGITT
7.32
156.0
0.5
While MAR-L attains a lower FID (1.73), does at prohibitive cost, with esti- mated inference time ~214 longer than our SSG-enhanced VAR. This performance passes VAR variants like HMAR-d24 and dif- fusion baselines such Diff'T. By mitigating the train-inference discrepancy and improving spatial coherence (Sec. 2.J). SSG offers a supe- rior performance-efficiing trade-off.
Mask.
MAR-L
1.73
279.9
214.4
AR
VQGAN+
26.52
66.8
25
HMAR-d24
2.99
304.1
VAR
VAR-d36$
2.70
290.6
1.0
+SSG (Ours)
2.39
320.6
1.0
4.3 GENERALIZATION ACROSS DIVERSE TOKEN ARCHITECTURES
To demonstrate the generalizability of SSG, we first test it on a text-conditioned model with different token structure: HART-0.7B, which tokens. shown in Tab. E SSG improves FID by 13.9% while maintaining stable CLIPScore. This confirms that our method enhances spatial fi- delity while preserving semantic alignment.
Table 4: T21 Comparison using MJHQ30K
Model
FID.
CLIP
Time (s)
uses hybrid continuous-disent
Scoret
HART-0.7B
8,46
0,2819
1.06
+SSG (Ours)
7.28
0.2834
1.07
Infinity-2B
10.01
0.2754
1.83
+SSG (Ours)
9.68
0.2767
1,86
We further challenge SSG Infinity-2B,, model with both bit-wise tokenization and built-in bit-wise self-correction (BSC) mecha- nism to mitigate teacher-forcing. SSG still delivers a 3.3% FID improvement with a stable CLIP- Score. This result confirms the benefits of SSG are orthogonal to such model-specific corrections and validates its role in addressing the core train-inference discrepancy of VAR-structured models.
The versatility demonstrated both hybrid and bit-wise tokens stems from the core design of SSG: it operates on the universal, pre-quantization logit space, making it agnostic to the token structure. By enhancing spatial fidelity while preserving semantic integrity across diverse architectures. SSG is a fundamental and broadly applicable enhancement to the coarse-to-fine generation paradigm.
4.4 ANALYZING THE SCALE- WISE REFINEMENT MECHANISM
We empirically assess the role of SSG as a scale- wise refinement mechanism by analyzing the spec- tra of residual logits from VAR-d16. Figure a) plots the relative change in the magnitude of Fourier-transformeed latents under SSG, revealing threshold at the Nyquist frequency of the previ- ous step. Above it, SSG increases spectral energy to synthesize novel high-frequency details; below it, SSG suppresses redundant low-frequency updates the curve stays near below zero (green line). This redistribution empirically supports the refinement mechanism in Sec. 2.2
We evaluate the impact of SSG on the quality-diversity trade-off by sweeping sampling tempera- tures for VAR-d16 and plotting FID vs. IS (Fig. 4p). Across the sweep. SSG demonstrates strong robustness by consistently improving the Pareto frontier: at comparable IS it attains lower FID, and at comparable FID attains higher IS, achieving the lowest FID and the highest IS observed. This indicates that SSG improves peak fidelity and maximum diversity without degrading the trade-off.
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Published as a conference paper at ICLR 2026
Step
Step
0.15
Moon Diff.
0.15
Mean Diff.
ALog Magnitule
Magninud
0.10
Nyquist (k 6)
+1 Std. Dev.
0.10
Nyquist (k 7)
#1 Std. Dev.
14
Without SSG
0.05
0.05
With SSG
0.00
0.00
12
0.0
2.5
Radial Bins (Freq.)
0.0
Radial Bins (Preq.)
2.5
7.5
HDD(
10
Step - Step
Step - Step 10
0.30
Magnitale
0.20
Meen Dirt.
Magnitude
0.25
Mean Dif.
0.15
#1 Std. Dev.
0.20
#1 Std. Dev.
Nyquist
0.15
Nyquist (k
0.10
0.10
0.005
0.0b
240
260
280
300
320
340
0.00
0.00
1S(1)
0.0
2.5
2.5
5.0
Bins (Freq.)
Bins (Freq.)
(a)
(b)
Figure 4: Frequency-Domain Refinement and Performance. (a) Analysis of the A log magnitude of Fourier-trant tranffrrransf latent embeddings. SSG redistributes the spectral energy by suppressing redundant low frequencies while selectively boosting the essential high-frequency energy beyond the Nyquist frequency (red line). (b) SSG achieves a consistently better FID vs. IS trade-off across sampling temperatures, indicating improved quality-diversee profile. See Fig. for the full trade- off graph over all evaluated sampling temperatures.
Table 5: Ablation of SSG on VAR-d16, covering expansion type and pprior formulation. f: baseline without SSG implementation; $: zero padding replaces extrapolation from mm
Expansion Type
Eprine Formulation
Bg Decay Schedule
FID.
ISt
Relative Latency
Baseline
N/A
N/A
3.42
275.6
1.0
Spatial
Nearest Neighbor
4.02
229.1
1.0
Spatial
Linear
3.79
234.8
1.0
Frequency
DSE
3.34
277.6
1.0
Frequency
DSE
X
3.63
287.8
1.0
Frequency
DSE (Ours)
3.27
285.3
1.0
4.5 ABLATION STUDIES
Prior formulation. Tab. 5 contrasts the baseline (no SSG) with spatial- and frequency-domain formulations of Eptorr VAR-d16. Spatial priors (nearest, linear) underperform the baseline in both FID and IS. Switching frequency-domain DSE improves results: DSE achieves FID 3.34 and IS 277.6, surpassing both baseline and spatial variants. Our full DSE prior yields the best balance (FII 3.27, IS 285.3) unchanged latency, supporting the frequency-domain design.
Decay schedule. A fixed Be (no decay) causes overguidance, producing exaggerated features rec- ognizable to Inception yet off-distribution. This raises IS to 287.8 while degrading FID to 3.63. A linear decay schedule, however, stabilizes refinement and achieves superior trade-off, yielding best FID of 3.27 while maintaining a high IS of 285.3. See Appx. for further (% scaling analysis.
5 CONCLUSION
We present Sealed Spatial Guidance (SSG), training-free, logit-space guidance for VAR-structured models. SSG amplifies semantic residual formulated with a frequency-domain prior using DSE, mitigating the train-inference discrepancy and reinforcing the intended coarse-to-fin to-fine hierarchy. Across VAR baselines and tokenizers, SSG delivers consistent gains in fidelity and diversity with negligible latency, competitive with or surpassing recent diffusion and masked models. We expect SSG be simple, model-agnostic building block for future work in next-scale generation.

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Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Scaled Spatial Guidance (SSG) is an inference-time technique that enhances pretrained generative models. While it can improve fidelity and controllability, the same capabilities could be misused by unauthorized actors. Risks include making deceptive or misleading media more convincing, with potential harms to privacy, reputation, and public trust.
Because SSG operates on existing models, it inherits their capabilities and limitations, including biases and harmful content patterns present in the underlying data. Our experiments therefore rely on publicly available, well-established models that include safety filters and community-vettad usage policies. SSG is not a safety filter itself: it should be deployed only alongside robust prompt and output moderation, provenance signals where appropriate, and human oversight for sensitive uses.
This work is intended for academic research and constructive applications. We explicitly prohibit malicious or unethical use, including the generation of deceptive content or content intended to cause harm. We encourage careful documentation of assumptions, adherence model licenses and safety settings, and the development of clear ethical guidelines to ensure the responsible advancement of guidance methods and the broader generative modeling community.
REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our research. To facilitate this, we will make our source code for Scaled Spatial Guidance (SSG) publicly available. The appendix provides compre- hensive implementation details, including prompts used across experiments, hyperparameter settings for all experiments, and the specific publicly available pretrained models used in our evaluation.
ACKNOWLEDGMENTS
This work was supported by Institute of Information communications Technology Planning & Evaluation (IITP) grant funded by the Korea (OVI) NN..8S-0022-112011 84, Develop- ment and Study of AI Technologies Inexpensively Conform Evolving Policy on Ethics).
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APPENDIX
A COARSE-STATE APPROXIMATION AND FREQUENCY HEURISTIC
We assume the established coarse structure satisfies fi-1 ~ L(fk) and that [(x: fi-1 L(fk) - for small (approximate stepwise sufficiency). The -owhhigh-rrequeccy split leveraging ideal low pass filter (L) and high pass filter (H) (L + H = Id) is used for intuition; by Data Processing Inequality (DPI), filtering only reduce Mutual Information (MI).
B EXPANSION OF THE VAR-IB OBJECTIVE
Here, we provide detailed derivation for the expansion of the VAR-specific Information Bottleneck objective. We begin with the objective as defined in the main text:
LVAR-IB = max (I(z fx fk-1) - I(fa-1:24)
(9)
The simplification uses the expansion of the conditional mutual information term, I(au; fK fE-1) We leverage the chain rule for mutual information (Cover & Thomas 2006), which is expressed
T(A: B о) = (, (
(10)
This expression is applicable when the variables form a Markov chain A + B + C. This con- dition implies that C' is conditionally independent of A given B, which simplifies the joint mutual information term [(A; B, C)) to [(A; B). In our context, the variables are 2k: B fk. and C' ff--1- The required Markov chain is therefore 23 - fK - fx-1. This Markov condition holds if our coarse state term fk-1 is a deterministic function of the final, high-resolution output fx (i.e.. f-- = [)fx)). This leads us to elaborate on deterministic conditioning.
Deterministic conditioning (exact chain rule). Let Cs == L(fx) denote the low-pass projection of the final output. Since Cg is deterministic function of fx. the chain rule holds exactly:
T(=y fx Ckk) = [(=xi fx) [(zui Ck).
(11)
Substituting C as the established coarse structure yields
LVAR-IB max 3 (I(ziifk)) 152x
To connect with the VAR state, the coarse-state approximation fx-1 Ck (see Appx. A With the wwwhigh-frequene decomposition fK L(fk) + Hffx), where L(-) and H(-) are deterministic filters (hence I(: L(fx)) and ((f)) are well-defined and non-increasing by DPI), identifying Cs = L(fx) yields the intuitive form used in the main text.
Therefore, using the exact identity in Eq. (Appx. A), the conditional term satisfies
11 and the coarse-state approximation fa- R L(fk)
I(zk fx fa-1) = [(z)fkk) I(zu: fx-1).
(12)
Substituting this back into the objective and collecting terms yields
LVAR-IB max 3 (1eas f() (z2s fi-1)) (fv 2x)
24
max (1(zzi fk) (1 fa-1) 1 (ZRi fk-1)
max BI(Z (T] fx) (B + 1)T(eni fr-1).
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C MAP INTERPRETATION OF THE SURROGATE
C.I STOCHASTIC-CHANNNE JUSTIFICATION THE DOT-PRODUCT SURROGATE
Where randomness enters. At step & we sample rk Cat((q)) with q' softmax( (E' /T); then 2 = emb(rx) and fa ((f are deterministic. Hence shaping '" shapes the stochastic node.
First-order IB-aligned ascent. Consider the power-tilted contrast
Cold') (1+ + Bg Po cx)l s By [log Ppeior (r fa-1)).
with logits &a and fprior for the two heads. Evaluated at soltmax((x/TT)
VeC, VeCC (softmaxx p=4x 7 A. (up mean shift removable by softmax invariance),
so a small logit update á obeys C. R const + f T AR. Adding a quadratic proximity term - 1
yields
max
$ 8 Ds - 11811
= Db. 8'= lx + 3 (3 s/T),
which is the SSG update. Thus the dot product ( 4 is the natural first-order ascent direction for the categorical sampling channel.
Robustness to logit preprocessing (DSE, temperature). In practice, the base/prior logits may be obtained after deterministic preprocessing: & (fx),, Éprior (fprior, e.g., frequency- aware interpolation (DSE) for the prior or temperature rescaling. Since sampling remains rą Cat(softmax((? /T),, stochasticity still enters only via the categorical, and the first-order derivation applies with the processed novelty direction ÅS = la - Eprior Scalar rescalings (temperature) reparameterize 3 via B s/T.
More generally, for locally linear map L' se J l' around la. the quadratic proximal step becomes
max 7 J
M M5,
M M 0,
with solution 8* = + M- Choosing M = 1 (our L2 proximity) and J e I recovers
+ B Åg. Thus DSE-based construction of ppiir and temperature modify the effective direction and step size but do not alter the bocchasic-channee justification or the closed-form SSG update.
C.2 PROXIMITY REGULARIZZTION 1.2 VS. DISTRIBUTIONAL TRUST REGIONS
Our state-redundancy term uses an 1.2 proximity regularizer on logits, - -11 fxl1 Two remarks:
Tikhonov view. This is a Tikhonov (weight-decay-style) trust region in logit space that stabilizes updates and yields the closed-form solution L' Ex + A
Distributional alternative. One can instead impose a distributional trust region via a KL penalty between the base distribution @k softmax((x/T) and the guided distribution q' softmax e.g..
XKL 4') or - -AKL qu).
This aligns the constraint in probability space but generally eliminates the simple closed form for & and requires iterative updates. For small steps, a second-order expansion of KL around & reduces to quadratic in L la. recovering an L2-type proximal form (up positive semidefinite metric induced by the softmax Fisher information). We adopt the L2 surrogate for its simplicity and closed- form optimizer while noting KL-based trust regions compatible alternative.
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C.3 MAP INTERPRETATION
We then can view the guided logits l' as obtained by MAP:
log p(
evidence)
log p()) p(l) & exp( - -x
log -likelihood surrogate
log peior
The likelihood surrogate & exp(8 (s)) rewards alignment with the novelty direction A. =
4 prioe. while the Gaussian prior anchors near the base logits lg. Maximizing the log-posterior gives exactly
E(E') (()) 211e" - ex12.
D FULL DERIVATION OF SCALED SPATIAL GUIDANCE
We begin with the Information Bottleneck (IB) objective, which seeks compressed representation X of an input X that is maximally informative about a target Y:
LIB min I(X;X) B BI(XYY)
(13)
x
where (::-) denotes mutual information and 8 > 0 trades off compression and relevance.
Instantiation for VAR at step k. For sequential coarse-to-fine generation, set X fr-1 (previ- ous state), X 2μ: (residual be generated), and Y fx (final output). Since we care about novel information about fK beyond fk-1, we use conditional mutual information, yielding
LVRR-IB = max 3 (zui ZNz fK 1a-1)) - (f( Z4
(14)
Chain-rule simplification. Under deterministic conditioning of the coarse state (Appx. A B I(A; B C*) I(A; B) - I(A; C) with C' deterministic function of B. Since fk-1 is an approxi- mately deterministic low-pass of fx.
LAR max
[[T( fk) T(zu: f6-1) - T(zRi fk-1)
zk
max 8 81(xxi (8 18)+
(15)
2k
Frequency-domain reduction. Decompose the final output into ideal low- and high-frequenee components, fK [(fx)= ((fx). Approximating additivity of information across disjoint bands, I(zii fk) se [2x +(fk))++((()) H(fx) ff))) )), and identifying the coarse state with the low-frequency part, fk-1 L(fk), we obtain the full intermediate steps:
LVAR-IB No max ((((s) + I ((x: - (8 1) L(fk)
(16)
max 31(zx BB1 B1(zxiL(fk)) ((xi
(17)
BI(zx: + ( 3- I(zx:L(fk))
(18)
= max B 3I( - L(fx))
(19)
Thus, the ideal residual 24 should be informative about high- figh-freguency content while uninfor- mative about already esttaiseett wow-frequeccy structure.
Logit-level surrogate and closed-form guidance. At step k. the model samples residual token Tk from residual logits & I its embedding yields 2g. We construct MAP-style surrogate term that follows a proxy for high-
aligned with Eq. (19) with two parts: (i) a target-informatieente frequency detail, the semantic residual DS le EpriOrs where éprir carries coarse information
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from the previous step; and (ii) state redundacdd penalty that keeps guided logits close to the base k.. For guided logits L
((e") ((e) A 2 ex E' R
(20)
The objective is strictly concave in l' (Hessian -1) and admits unique maximizer obtained by setting the gradient to zero:
Vel(C) = 3 A - -x' = 0
= EN B As.
(21)
Scaled Spatial Guidance.
Allowing the trade-off to vary by step, B + BR. yields the SSG update SSG
l + 3x = lx + Bu (fx- fprior).
(22)
This closed-form guidance mirrors the high- vs. Eq. while incurring negligible overhead.
low-frequency information trade-off in
Table 6: Infinity Table, latency measured for generating with batch size=1
Method
FIDJ
ImageReward"
CT.IP Scoret
HPSv2.1
GenEval
Latency(s)
Infinity-2B
10.01
0.952
0.275 0.277
30.46
0.683
1.83
+SSG (Ours)
9.68
0.964
30.61
0.690
1.86
E ADDITIONAL MODEL EVALUATION
In this section, we additionally report metrics that reflect human preference and prompt alignment: ImageReward (Xu et al.) 2023), reward model trained on human preferences; 1PSv2.1 (Wu al.) 2023), a score for aesthetic quality and prompt alignment; and Geneval (Ghosh et al. 2023). a multi- dimensional benchmark for generative model evaluation. Also, re-report FID and CLIP Score from Tab. 4 Overall, adding SSG to the baseline Infinity model provides overall improvement in all metrics, while adding only minimal latency overhead. The detailed result is in Tab. 6
F ANALYSIS OF GUIDANCE PARAMETER SCALING
This section analyzes the trade-off between key generation metries. We vary the guidance parameter 3& and plot the FID IS to examine the balance between distribution fidelity and sample quality.
5.0
Performance Trend
Metric Trade-offs. The plot the left reveals a clear trade-off between FID and IS. Initially, increasing the guidance strength improves both metrics, achieving an optimal point. However, further pushing for higher IS values beyond this point leads to sharp degradation in FID, in- dicating loss in overall sample diversity and fidelity. We test 3r values over the range (0.2, 2.4] with step size of 0.2.
4.5
FID(J)
4.0
3.5
3.0
240
260 IS(t)
280
300
Figure 5: The trade-off between FID and IS of the guidance parameter 3g- The curve illustrates that optimizing solely for can be detrimental to generation quality as measured by FID.
The results in Fig. 5 were obtained by applying SSG to the VAR-d16 model. To ensure an optimal balance, select the Br from the point just before the FID score begins to degrade significantly.
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Artisan studio style. 'SSG' logo stamped into small, imperfect ceramic tag, tied to the front of sludded apron. Organic, wabi-sabi, handcrafted.
Grumpy cat boxer.
1nfmitty2B-1024px
HIART-7B-1024px
A photo teen girl walking on a city street at night, street photography, 4K, ultra HDD, 3D shading beautiful, radiant, unity 3d, detailed, reali stii 3d shading, natural lighting.
A human palm with coin
Infinity-B--10244
HART-7B-1024px
265, Toy poodle
953, Pancapple
Tasby Cal
160. Outer
461 Herr Glass
Turtee
VAR-d36
VAR-d36
4B0
VAlb- 424
420
VAR:
416
VAIL
Bloskast
424
416
Prompt/Class Conditions
Models
Figure 6: Prompt and class used to generate Fig. and exact model used leveraging SSG per image.
G DETAILED PROMPTS AND SPECIFICATIONS FOR FIG.
This appendix provides the exact prompts and class conditions used generate the images in Fig. [ We report both class-conditional and text-conditional models, evaluated at resolutions from 256x250 1024x 1024. Model specifications are summarized in Fig. Fig- reproducibility. Display size in Fig. iis proportional to native resolution; 256 image occupies one quarter of the area of 1024x x T024 image.
H SPECTRAL FIDELITY AND HIGH-FREQQEENY ROBUSTNESS
rigorously verify the perceptual impact of SSG, we extend our analysis to the pixel level. We compute average spectral energy profiles using 50,000 samples generated by VAR-dI with and without SSG, comparing them against the 10,000 ImageNet validation images used for metrics in Tab. [ to ensure statistical robust- ness. The resulting spectral analysis in Fig. focuses on the frequency range 10t to 102, cor- responding meaningful fine textures rather than basic structure or extreme noise. In the band below x 10 SSG consistently ex- hibits higher spectral energy than the baseline, effectively enhancing fine details. Crucially, at frequencies beyond 5.5 x 10 the baseline di- verges from the reference curve, suggesting the possible amplification of artifacts noise. In contrast, SSG maintains tighter alignment with the reference dataset, demonstrating that it regulates the generation process match the true distribution rather than blindly amplifying noise.
Spectral Amplitude Ratio to Dataset
1.10
Dataset (Reference)
Without SSG
1.05
With SSG
1.00
0.95
0.90
10
5.5 x 10
10a
Spatial Frequency
Figure 7: Spectral Amplitude Ratio Analysis. Images generated with SSG consistently adhere better the distribution of the reference dataset.
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I EXTENSION TO OTHER HIERARCHICAL GENERATIVE MODELS
While SSG is intentionally tailored for the ex- plicit multiscale hierarchy of VAR, the un- derlying information theoretic perspective in- troduced in Sec. 2.2 is not inherently re- stricted to this architecture; it holds poten- tial for broader coarse-to-fine generative frame- works, such diffusion and other autoregres- sive models. These paradigms, which progress from noisy clean representations or accumulate semantic information hierarchically, present nat- ural anchors for guidance analogous to SSG. To empirically explore this concept, we performed preliminary case study by applying an SSG-inspired formulation directly the pre-sampling space of VQ-Diffusion (Gu et al. 2022). Evaluating metrics over 10,000 samples across 1,000 ImageNet classes at 256 x 256 resolution, our initial results in Tab. demonstrate performance improvements. Specifically, SSG integration yielded 0.21 reduction in FID and an 7.5 increase in IS, all while incurring negligible overhead to inference time. Despite the marginal improvement due to the con- ceptual and preliminary nature of this application, these findings strongly suggest that the theoretical establishment SSG can indeed benefit broader paradigms exhibiting coarse-to-fine behavior, en- couraging further research in this direction.
Table 7: Preliminary Generalization SSG
to Other Architectures. Generation quality (FID/IS) and inference efficiency (Steps/Time).
Model
FID
1S1
Steps
Time (s)
VQ-Diffusion
9.39
158.3
100
7.3
+SSG(Adapted)
9.18
165.8
100
7.3
Table 8: Latency Comparison of Models With and Without SSG. $ Zero-padding replaces ex- trapolation from L'mpepp
Model
Without SSG
With SSG
mean
std
params
mcan
std
params
VAR-d16
0.273
0.0303
310M
0.279
0.0313
310M
VAR-d20
0.320
0.0398
601M
0.324
0.0288
601M
256x256
VAR-d24
0.384
0.0288
1.0B
0.390
0.0279
1.0B
VAR-d30
0.530
0.0346
2.0B
0.536
0.0372
2.0B
512x512
VAR-d36
1.28
0.0279
2.4B
1.29
0.0326
2.4B
HART-d20
1.06
0.0280
732M
1.07
0.0236
732M
T21
Infinity-2B
1.83
0.0136
2.2B
1.86
0.0125
2.2B
VAR-d16(Ncarest Neighhour)
0.278
0.0297
310M
VAR-d16(I.inca)
0.278
0.0278
310M
Ablations
VAR-d16(DSE;)
0.276
0.0317
310M
VAR-d16(DSE with static B&)
0.279
0.0332
310M
Extension
VQ-Diffusion
7.27
0.0893
594M
7.27
0.0829
594M
J LATENCY COMPARISON
We report wall-clock inference time (Tab. g and relative latency (Tab. Tab. Tab. Tab.
Tab. and Tab. 77. Due VRAM limits on our available GPU (NVIDIA A6000), all reproduced measurements use batch size 1. Accordingly, table entries marked & (reproduced) are normalized to our locally measured VAR-d30 wall time at bs= 1, while entries without use relative times taken from the literature, which are normalized to VAR-d30 originally reported (typically at bs= 64) (Tab. 2 and Tab. 3) Thus, each relative time is computed against a VAR-d30 baseline measured under the conditions its source. The exact numbers can be found in Tab.
Especially, note that for isolating the incremental cost of the SSG operation. This choice does not compromise validity: all entries remain comparable because each is normalized to a VAR-d30 baseline measured under matched conditions.
is applied only to VAR (across scales) for internal comparisons and
Results are averaged over 100 runs, reporting the sample mean (mean), standard deviation (std), and the model parameters (params) both before and after applying SSG.
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K
TEMPERATURE SCALING DETAILS
18
Without SSG
16
With SSG
FID(
12
10
180
200
220
240
260
280
300
320
340
IS(1)
Figure 8: Full-Scale FID vs. IS Trade-off. This plot extends Fig. 4 (b) by showing the complete trade-off curves, averaged over 5 runs with error bars for both FID and IS. The curve with SSG consistently demonstrates a better quality-diversity profile, achieving both lower minimum FID and higher maximum IS compared to the baseline across the full range of evaluated temperatures.
To ensure reproducibility for the results shown in Fig. 4 (b), we specify the temperature values used. For the baseline model (without SSG), we swept the temperature from 0.5 1.2. For method (with SSG), we used range of 0.7 to 1.5. Both evaluations were performed in increments of 0.1.
Figure presents the full-scale FID IS trade-off curve, which encompasses all data points used for Fig. (b). This evaluation spans the temperature range from 0.5 to 1.5 0.1 increments, yielding 1l data points in total. This plot explicitly includes the average of N independent runs across random seeds, with the uncertainty of both the FID and IS metrics indicated by error bars. As clearly observed in the full-scale result, the case with SSG (orange) demonstrates a superior trade- off profile than the baseline (blue) across the entire operational spectrum. The points achieved with SSG successfully form Pareto frontier, attaining both lowest FID and the highest IS on the curves. Crucially, the best FID recorded by our SSG is lower than the best baseline FID, with this substantial improvement falling outside the error bar range the optimal baseline point. Furthermore, for any comparable data points, SSG consistently yields better FID and IS, which robustly substantiates our initial claim that SSG provides a consistently better FID vs. IS trade-off.
L ADDITIONAL RELATED WORKS
Diffusion models are a central paradigm for visual generation (Ho et al. 2020 Nichol Dhariwal 2021). Early work such as latent diffusion (Rombach et al. (202)) employed U-Net backbones to iteratively denoise latent representations. While U-Nets provide strong multi-scale feature extrac-
tion, capturing long-range dependencies can be challenging, motivating transformer-basee designs, such as DiT and U-ViT (Peebles Xie 2023 Bao et al. (0023). Transformers offer improved global interaction modeling and scale effectively, yielding fidelity gains with model size (Chen al. 2024
Ma et al. 2024 Li et al. 2024a). Recent rectified- flow methods aim for faster, few-/single saaunngllstee generation [Esser et al. [2024] Batifol et al. 2025), yet iterative denoising remains major computa- tional bottleneck in common pipelines, with substantial inference costs memory and time (Peebles)
& Xie 2023 Rombach al. 2022 Yan al. 2024 Hatamizadeh et al. 2024
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Without SSG
(k-2)" Image
(k-1)" Semantic Residual
(k-11) Image
k Semantic Residual
kth Image
With SSG
Figure 9: Progressive Detail Enhancement with SSG. Without SSG (top), the semantic residuals
lack progressive detail, leading to artifacts like disconnected legs (red box). With SSG (bottom), the tthh residual introduces finer, structurally coherent details, such as the clearer beak (green box) and properly connected legs (red box) not present at (k- 1)th better realizing coarse-to-fin fonee nature.
M FURTHER QUALITATIVE COMPARISON ON FINE DETAIL GENERATION
This section provides a further qualitative examination of Fig. 9 Applying SSG not only adds fine detail but also improves overall visual coherence by placing those details consistently within the object structure, yielding more complete and perceptually stable entities.
We present additional qualitative evaluations of VAR models from d16 to d36 at 256x256 and 512x512 512 in the class-conditional setting. The results in Fig. show that SSG consistently enhances fine detail and completes entities across VAR scales.
To further validate the improvements in generative quality in text-conditional generation using T2I models including HART (Tang et al., (025)) and Infinity (Han et al.] [2025). we present additional qualitative evaluations. These results demonstrate that while SSG improves image fidelity, it also enhances the capability of these models to generate the precise details described in the input prompt.
This is further illustrated in Fig. 12 and Fig. 13
We also analyze failure cases where SSG yields limited improvements. Fig. 14 iillustrates these limi- tations, categorized by (a) poor initial states and (b) challenging ambiguous conditions. In column (a) (top), a VAR-d36 generation, SSG restores the main guitar structure but fails to render fine details including the strings. This is constrained by inherent model tokenization limits and poor initial state with a severely distorted guitar that SSG cannot fully correct. For the HART model (middle), the prompt provides only vague screen-specifie details. SSG offers minimal improvement, bounded by intrinsic model limitations in rendering this content, particularly when it is uncertain what to refine from the vague initial state. In the bottom example (HART), SSG successfully enforces the "a 7 year old brown skin girl" prompt detail and removes artifacts, yet fails to perfectly render the bird. This demonstrates that the corrective capability of SSG may be limited when starting from severely misaligned initial state.
Column (b) in Fig. 14 highlights failures related prompt comprehension or inherent ambiguity within a class. For class-condes class-conditional generation (VAR-d30, top), objects, such as the sea cucumber, that naturally fuse with the background are not distinctly generated. This occurs because SSG does not force such objects to be distinct, respecting their inherent nature to blend into the background. For the HART model (middle), given highly ambiguous prompt like "Cosmic Death," SSG merely shifts the output from "Death" to "Cosmos" but cannot resolve the conceptual ambiguity, reflecting a model-level text understanding failure. Similarly, in the bottom HART example, the text encoder fails to parse specialized medical jargon, capturing only the word "goat". While SSG successfully removes most artifacts, it cannot compensate for the fundamental inability of the encoder to interpret the specialized prompt.
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N HUMAN PERCEPTUAL EVALUATION
To validate the perceptual quality and semantic fidelity, we conducted blind A/B choice hu- man evaluation. This evaluation involved 28 participants, ranging from non-experts ex- perts in the visual generation field, who as- sessed 15 item pairs sampled across VAR- structured models, specifically VAR, HART, and Infinity. The results Fig. demonstraate significant preference for the SSG-enhanced images. These images were favored 71.0% of trials, compared to only 11.9% for the base- line. This robust subjective preference confirms that the superior spectral fidelity, coupled with stronger alignment to the given class or text conditions, directly translates into significant and robust improvement in perceived quality. The 17.1% tie rate indicates that the improvements provided by the SSG might be difficult to distin- guish for non-expert evaluators those instances, suggesting that SSG's enhancement often targets fine-grained details which, while objectively superior, require closer inspection to fully perceive.
11.9%
With SSG
17.1%
Tie
Without SSG
71.0%
Figure 10: Human Evaluation (A/B Test). The SSG-enhanced method demonstrates superior per- ceived quality compared to the baseline
O REPRODUCTION NOTES FOR REPORTED TABLES
We document the sources of all reported numbers. Unless otherwise noted, values in Tab. Tab. Tab. 33 and Tab. | are taken from the original papers. The mark reproduced denotes results we computed due to issues with the released VAR pretrained weights (Tian et al.) (2024); see Sec. 4.1
details. For Tab. all entries are our reproductions, due to problems detailed in Sec.
P LIMITATIONS
SSG operates within the logit space during inference. Therefore, architectures that do not expose logits at the sampling stage, such latent diffusion (Rombach et al. (022) flow matching (Lip- man et al. 2023), which operate in continuous feature space by predicting noise or velocity vectors, respectively, require substantial modification to apply SSG, even though the underlying principle remains applicable.
THE USE OF LARGE LANGUAGE MODELS (LLMS)
We used LLMs solely for editorial assistance, to polish grammar mostly and converting paper- written mathematical expressions into 1TTX (including formatting proofs in the appendix). The model did not generate ideas, claims, or experimental content, and it was not used for data analysis code design beyond minor formatting. All technical statements, equations, and results were authored and verified by the authors.
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Class/Model
Without SSG
With SSG
023, Vulture
VAR-d36 512x512
158, Toy Terrier
VAR-d30 256x256
362, Badger
VAR-d24 256x256
980, Volcano
VAR-d20 256x256
355, Llama
VAR-d16 256x256
Qualitative evaluation of VAR across scales. Applying SSG enhances fine-detail gen- eration consistently multiple scales.
Figure 11:
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Prompt
Without SSG
With SSG
Stranger things dogs, all characters are dogs that look like the human actors, promotional poster photo
14 year old boy affectionately covers with blanket an elderly man with curly gray hair, sleeping on couch, sci fi apartment, cyberpunk style, night, neon lights, realistic, 8k
Hyper realistic photo of hippiebabypig taking selfie
4 people riding horses
Figure 12:
Qualitative Evaluation using HART. The of SSG not only improves the quality of the generated images but also results stronger alignment with the input prompt.
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Prompt
Without SSG
With SSG
Stained glass river
Super close macro shot of violet Salvia officinalis blossom hyperrealistic sunny bright shining photography
Photo of a halfbody portrait of a bearded pirate smoking Cohiba cigar. The pirate has hostile expression and is wearing a pirate hat with
skull on it. He has carrings and should resemble a character from Pirates of the Caribbean.
Portrait of God when he is creating the Sardinian island starting from his foot print, expressionist oil painting
Figure 13: Qualitative Evaluation using Infinity. The of SSG improves overall image quality. Most importantly, it captures the precise details depicted in the input prompt.
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Without SSG
With SSG
Without SSG
With SSG
Class 546 (Electric Guitar)
Class 329 (Sea Cucumber, Holothurian)
Ux product interface design of laptop screen confirm app download, behance, blender
Cosmic death,
A goat with signs of dynamic instability, history of fall, sink and counter, adjacent level compression, motion degraded, extremity strength 4 of 5, clear radicular pattern, describe central back, degenerative change, all other components within normal limits, many additional chronic findings above
Aerial cinematic photo year old brown skin girl top of eagle flying in the sky, Narnia style. Wes anderson color palette. Sunrise natural light.
a
Figure 14: Qualitative Evaluation on Failure Cases. The corrective capability SSG bounded by initial states or task ambiguity. (a) Cases where SSG cannot fully recover from poor initial states stemming from tokenization issues or weak text-prompt alignment. (b) Limitations due to prompts being highly specialized or ambiguous, when objects are inherently fused with the background.
26


Reviews:
[{"review_id": "AU2giwh4On", "reviewer": "Reviewer_snkW", "summary": "This paper proposes a training-free guidance mechanism for Visual Autoregressive (VAR) models by leveraging the inherent scale-structured generation order. Inspired by information bottleneck principles, the authors introduce Scaled Spatial Guidance (SSG), which adjusts token generation dynamics across scales to correct distortions and enforce structure. Experiments across multiple architectures and benchmarks demonstrate consistent improvements in synthesis quality, suggesting that scale-aware inference adjustments can serve as an effective plug-and-play enhancement for VAR models.", "questions": "**On generalization of the method**\nCould the proposed method extend beyond VAR to other hierarchical generative frameworks such as diffusion guidance or hierarchical autoregressive models in below? If not directly, what modifications might be required?\n- Parallel Multiscale Autoregressive Density Estimation, ICML 18\n- Generating Diverse High-Fidelity Images with VQ-VAE-2, NeurIPS 19\n- Locally Hierarchical Auto-Regressive Modeling for Image Generation, NeurIPS 22\n\n**On frequency-domain evidence**\nThe paper claims enhanced high-frequency fidelity (e.g., Fig. 7), but the evidence is mostly visual. It would be beneficial to provide spectral analysis:\n- Compare the average spectral amplitude of the dataset vs. VAR w/o SSG vs. VAR+SSG.\n- Test Gaussian blur sensitivity: if SSG enhances high-frequency components, performance should degrade more under blurring.\n- The paper On the Frequency Bias of Generative Models (NeurIPS'21) may serve as a useful analytical framework.\n- Adding spectrum visualizations alongside Fig. 8/9/10 could provide direct support.\n\n**On Appendix L temperature settings**\nAppendix L reports different temperature ranges for baseline w/ and w/o SSG (0.5–1.2 vs. 0.7–1.5). Would it be fairer to evaluate both methods over the same temperature range (e.g., 0.5–1.5) and report best results or display curves for each? Since temperature sweeps were performed, sharing plots would clarify any performance variance across sampling strengths.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper identifies that the generation order of VAR models implicitly forms a scale-guidance structure and introduces a training-free guidance method that exploits this property.\n- The proposed approach is grounded in the information bottleneck perspective, and the authors empirically support that the method can correct distorted signals.\n- Extensive experiments across various models and datasets demonstrate consistent improvements, highlighting the robustness and generality of the method.", "weaknesses": "- While the method appears generic, the presentation is heavily specialized for VAR, making the broader applicability unclear.\n- The approach, although training-free, feels ad-hoc and may not fundamentally address scalability and representation issues in visual tokenization; it would strengthen the contribution to explore how SSG could be incorporated into tokenization or model design directly.\n- The abstract emphasizes improving high-frequency details, but the validation for this claim is limited and not rigorously demonstrated.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7LWhtzzJTd", "reviewer": "Reviewer_YsKt", "summary": "This paper introduces Scaled Spatial Guidance (SSG) — a training-free, inference-time enhancement for visual autoregressive (VAR) models. VAR models generate images via next-scale prediction in a coarse-to-fine hierarchy but often suffer from train–inference discrepancies, where fine scales redundantly reconstruct coarse details instead of adding new high-frequency content. SSG addresses this by guiding each generation step to focus on the semantic residual—the high-frequency information not captured by previous scales. This is achieved through Discrete Spatial Enhancement (DSE), a frequency-domain prior construction that preserves coarse structure while isolating finer details. Applied directly to logits during inference, SSG improves image fidelity and diversity across multiple VAR baselines (VAR-d16 to VAR-d36), achieving up to 11.5% FID improvement at 512×512 resolution with negligible latency.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. SSG works purely at inference on logits, requiring no retraining, fine-tuning, or architectural changes.\n\n2. The method is derived from an information bottleneck perspective, offering a principled justification for why emphasizing high-frequency residuals improves coarse-to-fine generation.\n\n3. The frequency-domain DSE module is simple and elegant.\n\n4. Performance is good. SSG consistently improves VAR across various model sizes and input resolution, with only negelectable cost.", "weaknesses": "1. The proposed method is closely tied to the coarse-to-fine next-scale structure of VAR models. While this focus is well-motivated, it somewhat limits the generality of the contribution. It remains unclear whether SSG can extend to broader autoregressive or diffusion-based generation frameworks. Discussing how the information-theoretic insights or the frequency-domain prior could inspire guidance mechanisms beyond VAR would strengthen the paper’s broader impact.\n\n2. Most examples highlight success cases where SSG clearly improves detail. Including a few failure or neutral cases could offer a more balanced understanding of when the guidance might be less effective.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bhv0zUZeim", "reviewer": "Reviewer_oX8V", "summary": "This paper proposes a simple, training-free guidance trick for next-scale visual autoregressive (VAR) models called \\emph{Scaled Spatial Guidance} (SSG). At sampling step $k$, the method constructs a coarse ``prior'' by upsampling the previous step's logits via a frequency-domain procedure (\\emph{Discrete Spatial Enhancement}, DSE), and then applies a closed-form logit update\n\\[\n\\ell^{\\text{SSG}}_{k} = \\ell_{k} + \\beta_{k}\\bigl(\\ell_{k} - \\ell_{\\text{prior}}\\bigr),\n\\]\nintended to emphasize the predicted high-frequency residual while keeping the coarse structure aligned with the autoregressive hierarchy. The approach requires no retraining, adds negligible overhead, and is reported to improve FID/IS across several VAR baselines and tokenizations on ImageNet (256/512) and MJHQ-30K, without increasing the number of sampling steps.\n\n\nWhile practical and easy to bolt on, the novelty feels incremental relative to existing guidance/contrastive sharpening ideas (e.g., CFG-style logit shaping, spectral emphasis, residual boosting). The theoretical framing relies on strong heuristics---coarse-state Markov assumptions and a clean low/high-frequency separation in logit space---that may not hold for learned decoders; likewise, DSE implicitly assumes band-independence that is only approximate. The method appears sensitive to the quality of the transported prior and the schedule $\\{\\beta_k\\}$ (and temperature), yet the paper offers limited analysis of failure modes (e.g., misaligned priors), robustness, or hyperparameter stability. Empirical scope is narrow (primarily ImageNet and a single T2I set), comparisons depend on in-house reruns under one protocol (raising fairness/reproducibility concerns), and there is no statistical testing, human preference evaluation, or detailed profiling of latency/memory across resolutions/hardware. The technique is appealing as an inference-time tweak, but stronger evidence (broader datasets, stress tests on compositional/OOD cases, ablations on DSE variants, instability/degeneration analyses, and transparent baseline parity) would be needed to elevate its contribution.", "questions": "In Fig. 4(b), did you sweep identical temperature grids for baseline and +SSG, and report matched points with CIs over multiple seeds? If not, can you replot with a shared grid and error bars to substantiate the “consistently better FID–IS trade-off” claim?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "SSG is a one-line logit update with a clear algorithmic recipe (Alg. 1–2) and no retraining or architectural changes; it operates directly on residual logits and drops into a wide range of VAR-style decoders.", "weaknesses": "SSG feels close to existing guidance/contrastive logit-shaping (e.g., CFG-like sharpening, residual boosting), with limited conceptual leap.\n\nThe motivation for DSE/SSG leans on orthonormal transforms yielding “independent and non-interfering” bands, enabling “exact, lossless reconstruction” and a clean separation of low/high frequencies for prior construction. That’s a strong assumption in learned logit spaces and likely violated by aliasing, context coupling, and tokenization quirks; the paper doesn’t test how departures from this assumption affect outcomes.\n\nAblations show nearest/linear spatial priors worsen both FID and IS versus baseline (no SSG). This suggests SSG can hurt quality unless the frequency-domain prior is used, narrowing practical robustness and increasing configuration risk. Yet the paper doesn’t map when a user might inadvertently choose a weaker prior (e.g., for speed).\n\nThe method explicitly depends on the transported/upsampled prior. Misalignment (e.g., spatial drift, aliasing, or tokenization artifacts) can produce oversharpening, texture hallucinations, or suppression of semantically important low-frequency structure.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cAgIpr2biE", "reviewer": "Reviewer_oyJp", "summary": "This paper proposes a training-free inference guidance method for Visual Autoregressive Generative Models (VAR), called Scaled Spatial Guidance (SSG). This method aims to address the problem of VAR deviating from the coarse-to-fine hierarchical generation pattern during inference. From an information theory perspective, the authors point out that each generation scale should only supplement high-frequency semantic residuals not covered by the previous scale. To this end, SSG guides the model's logits during inference by calculating residuals with the frequency domain prior constructed in the previous step and amplifying high-frequency components. Experiments show that SSG significantly improves image sharpness and structural consistency while maintaining the same inference speed.", "questions": "Please see the weakness", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The observations in this paper are highly relevant to the proposed method and make sense.\n\n2. The proposed Discrete Spatial Enhancement effectively addresses the information loss caused by linear interpolation.\n\n3. Comprehensive experimental data show that SSG can consistently improve the generation performance of various variable models without compromising the generation speed.\n\n4. Ablation experiments validates the effectiveness of the proposed method.", "weaknesses": "1. Most of the experimental results in this paper come from class-conditional image generation. I believe that including more experimental results from text-to-image VAR models as the main experimental content would better demonstrate the practical value of the method. For example, adding more visual comparisons on infinity models to illustrate the visual improvement of the proposed method, or further expanding Table 4.\n\n2. The proposed method seems to be applicable only to next-scale prediction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)