Stochastic Self-Guidance for Training-Free Enhancement of Diffusion Models
Authors: Chubin Chen
Jiashu Zhu
Xiaokun Feng
Nisha Huang
Chen Zhu
Meiqi Wu
Fangyuan Mao
Jiahong Wu
Xiangxiang Chu
Xiu Li
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Models; Classifier-free Guidance
Primary area: generative models

Abstract: Classifier-free Guidance (CFG) is a widely used technique in modern diffusion models for generating high-quality samples. However, through an empirical analysis on both Gaussian mixture models with closed-form solutions and real-world data distributions, we observe a discrepancy between the suboptimal results produced by CFG and the ground truth. The model's excessive reliance on these suboptimal predictions often leads to low fidelity and semantic incoherence. To address this issue, we first empirically demonstrate that the model's suboptimal predictions can be effectively refined using sub-networks of the model itself, without requiring additional training or the integration of external modules. Building on this insight, we propose **$S^2$-Guidance ($S$tochastic $S$elf-Guidance)**, a novel method that leverages stochastic block-dropping during the denoising process to construct sub-networks. This approach effectively guides the model away from potential low-quality predictions, thereby improving sample quality. Extensive qualitative and quantitative experiments across multiple standard benchmarks for text-to-image and text-to-video generation tasks demonstrate that **$S^2$-Guidance** delivers superior performance, consistently surpassing CFG and other advanced guidance strategies. Our code will be released.

Published as a conference paper at ICLR 2026
STOCHASTIC SELF-GUIDANCE FOR TRAINING-FREE ENHANCEMENT OF DIFFUSION MODELS
Chubin Chen
Jiashu Zhu
Xiaokun Feng*
Nisha Huang',
Chen Zhu Xiu Li
Meiqi Wu
Fangyuan Mao
Jiahong Wu
Xiangxiang Chu?
Tsinguu University
AMAP, Alibaba Group CASIA
Project Page: https: http:::///2guildii github. ic/
Guidance
Figure I: Visual results of S2 -uiidaneee versus CFG. Our proposed method S S-GGuidance signif- icantly elevates the quality and coherence of both T2I and T2V generation. Observe (in examples surrounding the center): Our method produces generations with superior temporal dynamies, including more pronounced motion (bear) and dynamic camera angles that convey speed (car). renders finer details, such as the astronaut's transparent helmet and rich facial details, and creates images with fewer artifacts (runner, woman with umbrella), richer artistic detail (abstract por- trait, castle, colored powder exploding), and improved object coherence (cat and rocket, sheep). See Appendix B.5 for our prompts.
ABSTRACT
Classificr-free Guidance (CFG) is widely used technique for improving condi- tional generation diffusion models. However, our empirical analysis of both Gaussian mixture data and real-world image data distributions reveals discrep- ancy between the suboptimal results produced by CFG and the ground truth. The model's excessive reliance on these suboptimal predictions often leads low fidelity and semantic incoherence. To address this issue, we first empirically demonstrate that the model's suboptimal predictions can be effectively rectified using sub-networks of the model itself, without requiring additional training or the integration of external modules. Building on this insight, we propose 5 Guidance (Stochastic Self-Guidancee, novel method that leverages stochastic block-dropping during the denoising process to activate sub-networks for self- guidance. This approach effectively steers the sampling trajectory towards high- quality regions. Comprehensive experiments, including on class- dlass-conditional Im- ageNet generation and across multiple benchmarks for text-to-image and text-to- video generation, demonstrate the superiority of 52-Guidance. Both qualitative and quantitative results show that S 5²-Guidance consistently surpasses CFG and other advanced guidance strategies. Our code will be released.
"qqual contribution.
"Corresonddig author. Projecct lead.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Diffusion models (Song et al., 2020a; Ho et al., 2020) have enabled rapid advances in high-quality text-to-image (Rombach et al., 2022; Podell et al., 2023; Labs, 2024; Wu et al., 2025a) and text-to- video (Wan et al., 2025; Kong et al., 2024) generation. A key driver this success is the advent of conditional guidance techniques, which steer the generation process enhance adherence to given conditions. However, naively applying the conditioning signal often proves insufficient (Dhariwal & Nichol, 2021). Classifier-fis free Guidance (CFG) (Ho & Salimans, 2022) has become the mainstream approach for improving conditional generation. It employs a Bayesian implicit classifier to prioritize conditional probability, enhancing adherence conditions and image quality. However, despite its effectiveness, often results in semantic incoherence and a loss of fine details, as shown in Figure 1.
Recent studies (Chung et al., 2024; Sadat al., 2024; Fan et al., 2025; Kynkäänniemi et al., 2024; Jin et al., 2025) have further explored methods to improve guidance. Although these methods im- prove quality to some extent, they primarily address specific issues while leaving the underlying mechanisms of CFG unexplored. A representative work that begins to explore this issue is Auto- guidance (Karras et al., 2024), which identifies deficiencies in the model's training objective and proposes using weak model for guidance. Subsequent works (Hong et al., 2023; Ahn et al., 2024; Jeon, 2025; Hong, 2024) propose modifying specific attention regions to mimic a weak model for various tasks (Qi al., 2023; Simsar et al., 2024). However, these methods either require training to acquire the weak model or rely on empirical, task-specific modifications to the network, which in turn demand meticulous hyperparameter tuning.
To address this, we first analyze the suboptimal results produced by CFG and the underlying mech- anisms of weak-model guidance. Specifically, analysis begins with toy example on Gaussian mixture modeling. where a closed-form solution allows for precise evaluation against the ground truth (Brown et al., 2022; Pope al., 2021), and is subsequently validated on real-world image data. Furthermore, we observe that applying stochastic block-dropping during the model's forward process produces results highly similar to the weak model used in Autoguidance, Building on this discovery, we propose 5²-Guidance, a simple yet effective approach to address the suboptimal pre- dictions of CFG and guide sampling towards higher quality and fidelity. Unlike prior methods that rely on externally trained or manually tuned weak models, Gu-Guidance leverages the model's 's own intrinsic structure in a training- fenning-freeeee free manner, effectively steering the denoising trajectory away from failure modes to enhance the performance of conditional diffusion models.
Our contributions are summarized as follows:
(i) We first analyze the guidance behavior of CFG and the un- derlying mechanisms of weak- model guidance through a se- ries of toy examples. These amples allow us to visually an- alyze the suboptimal results of CFG. Empirical observations re- veal that the sampling trajectory can be effectively rectified by the model's own sub-networks, which exhibit guidance behavior similar that of weak model.
High
Ours
Quality
Serectiio Nade
is
Bluck
tander are
dandance
Geranabie Mocel
CFG
Figure 2: An illustration of our guidance mechanism on the generation quality manifold. Unlike suboptimal CFG guid- ance (gray), S2-GGide derives a corrective signal (blue) via stochastic block block-dropping, steering the generation update (pur- ple) toward the optimal quality peak (yellow).
(ii) We propose S2-Guidance, a novel method that leverages stochastic block-dropping dur- ing the forward process to acti- vate sub-networks for self-guidance, thereby bypassing the need to construct weak models through additional training or a and-error manual selection process. Furthermore, we demonstrate that in the iterative denoising process, single block-dropping per timestep is sufficient to steer the sam- pling trajectory towards high-quality regions. This approach achieves strong performance while substantially reducing computational costs compared to the naive variant.
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(6) Condition Output
(b) CFG
(c) Autoguidance
(d) Naive Si-Guidance
(c) Si-Guidance
Figure 3: S -Guidance successfully balances guidance strength and distribution fidelity. Com- parison on 1D (top) and 2D (bottom) toy examples. Unlike CFG, which distorts the sample distribu- tion (see red boxes), or other methods that fail to separate modes, 52-Guidance accurately captures both the location and shape of the ground truth distributions (semi-transparentt.
(iii) Our method can seamlessly adapted various diffusion models. Comprehensive experi- ments class- class-conditional ImageNet generation and across multiple benchmarks for text-to-image and text-to-videe vidoo establish the superiority of 3F-Guidance. Both qualitative and quantitative results confirm that 52-Guidance consistently surpasses not only CFG but also other advanced guid- ance strategies.
BACKGROUND
Diffusion Models. Diffusion models (Croitoru et al., 2023; Peebles & Xie, 2023; Esser et al., 2024; Chu et al., 2025b) class of powerful generative models that learn reverse predefined forward process, which gradually perturbs data 2g into Gaussian noise Ty. The reverse process is typically governed by time-reversed stochastic differential equation (SDE) (Song al., 2020b), which relies on accurately estimating a score function Va, log Pr(x.) using a neural network Do- Flow-based models (Lipman al., 2023; Liu et al., 2022: Gat et al., 2024) can also be viewed special class of diffusion models, as they both aim to learn continuous transformation between simple prior distribution and the complex data distribution (Gao & Zhu, 2025). practical appli- cations (Huang et al., 2023a; Zhu et al., 2024; Huang et al., 2024a; Mao et al., 2025), generation is often conditioned on signals e (e.g., text prompts), shifting the objective modeling the conditional score V.n m, log Pe ( (c).
Classifier-free Guidance (CFG). CFG (Ho Salimans, 2022) has become the cornerstone for controllable generation (Huang al., 2023b; Wang et al., 2024b; Fang al., 2025; He al., 2025; Ma et al., 2024b; 2025a) by offering a simple yet effective mechanism to enhance conditioning. In- stead of only using the conditional prediction Do(x,le), CFG forms guided score by extrapolating from an unconditional one (xrl()):
(a Do(x; () (Do c) (o(a)ld))
where À is the guidance scale. However, despite its effectiveness, this approach suffers from notable drawbacks (Sadat al., 2024; Hong al., 2023; Karras al., 2024), including semantic inconsis- tencies and significant loss of fine-grained details, as illustrated in Figure
Weak-model Guidance. A promising direction to improve CFG is to leverage an auxiliary "weak" model to refine the guidance signal. For instance, Autoguidance (Karras et al., 2024) employs a separately trained, degraded version of the full model, but such models are often infeasible to obtain for large-scale pretrained models. To circumvent this, recent works simulate weak model
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14l Condooe Oppa
91 Aategaile
60 Natre 5 C-CEERRR
05 YoRalaa
Figure 4: S -uuudideeee avoids the distributional collapse of CFG on CIFAR-10. I-SNE shows generated features (points) vs. real data (contours). CFG (b) exhibits severe collapse, whereas S Guidance (e) preserves the distribution's 's structure while ensuring class separation. See (f) for qualitative examples.
by modifying the model's architecture or perturbing its internal states. For instance, some studies rely on heuristic perturbations like sicentionggudddd blurring of predicted samples (Hong et al., 2023; Ahn et al., 2024); SEG (Hong, 2024) later proposes an alternative from an evergy-based perspective; and other works develop strategies for specific tasks (Jeon, 2025; Hyung et al., 2025) However, these perturbation techniques often rely on task-specific, hand-crafted architectural modifications, which limits their generalizability. In contrast, as shown in Figure 2, our S 52-Guidance introduces a novel and flexible approach. We guide the sampling process by dynamically activating sub-networks via stochastic block dropping, thereby avoiding the need to construct a weak model through auxiliary training or manually designed perturbation schemes.
3 METHODOLOGY
3.1
VISUALIZING AND REVISITING WEAK-MODEL GUIDANCE
We begin by visualizing the suboptimal outcomes of CFG using Gaussian mixture data (Ho & Sali- mans, 2022), toy example with closed-form solutions. This allows us to systematically observe the discrepancies between predictions and ground truth. Building on the analysis of how weak-model guidance (Karras et al., 2024) improves results, we identify its limitations and propose incorporat- ing stochastic sub-networks into the CFG framework, providing a novel approach to enhance model performance.
CFG improves conditional generation by implicitly amplifying the conditional probability density, raising it to power greater than one (Bradley Nakkiran, 2024). Figure 3 illustrates 1D toy example (top) aimed at learning a Gaussian Mixture distribution with modes at -4 and 4. While CFG significantly improves the baseline conditional output, it also introduces a notable drawback: as highlighted by the red box, the mode of the generated distribution is slightly shifted from the ground truth. A similar shift occurs in a 2D toy example (bottom), where samples are scattered into unintended regions. These findings suggest that, although CFG enhances sample quality, its distributional fidelity remains suboptimal. Autoguidance, as representative of weak-model guid- ance (Karras al., 2024; Hong al., 2023; Hong, 2024; Ahn et al., 2024), is designed to guide the model toward well-learned, high-probabilityy regions by leveraging a weak model. As shown in Figure 3 (middle)) AutoGuidance improves the peak near -4 but remains limited. Its improve- ment stems from the construction of a weak model, with the extent of enhancement depending on the weak model's effectiveness. Such models are typically created by reducing model capacity or training epochs.
However, this approach faces practical limitations that restrict its broader applicability. First, rely- ing on externally designed weak models poses scalability challenges, as obtaining reduced version trained for fewer epochs alongside a large-scale pretrained model is often impractical. Second, as highlighted by (Karras et al., 2024), selecting appropriate weak model is constrained by various factors. Once chosen, the weak model affects the entire denoising process, limiting the flexibility of guidance. A poorly designed weak model fails effectively prevent low-quality outputs (Hong, 2024), as shown in Figure 3 (c), where guided outputs still deviate notably from the target distribu- tion.
This raises an important question: Can we eliminate the reliance on externally prescribed weak mod- els while still identifying error-prone regions? Prior works (Lou et al., 2024; Avrahami et al., 2025;
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Yuan al., 2024) have shown that mainstream generative architectures, such as DiT (Peebles & Xie, 2023; Chu et al., 2024), exhibit significant redundancy, as outputs across different transformer blocks often show high similarity (Chen et al., 2024). Inspired by this, hypothesize that sub- networks within such architectures can function as weak models, capturing outputs similar to the full model but with more pronounced errors. By leveraging these sub-network predictions, we aim to refine existing CFG, effectively steering the model away from suboptimal outputs. The following subsections present a detailed description of our approach along with its empirical validation.
3.2 NAIVE STFEE5S-GUNN -UUDDACCE
Building on the preceding observation, our key insight is that we can leverage the model's own sub- networks intrinsically steer the denoising trajectory away from potential failure modes, thereby refining the suboptimal results of CFG.
As revealed in Autoguidance (Karras et al., 2024), problems in generative models depend vari- ous factors (e.g., network architecture, dataset properties, etc.), making it difficult to pinpoint which components play decisive role. Therefore, it is challenging a priori define an optimal sub- network that best captures low-quality regions. Motivated by (Gal & Ghahramani, 2016), a naive solution is leverage as many diverse stochastic sub-networks as possible weak models. These weak models then guide the main model away from low-quality regions dur- ing each forward pass by steering it away from their outputs. We refer to this approach as Naive Stochastic Sub-network Guidance (Naive 52-Guidance). Intuitively, this can be understood as ap- plying stochastic "dropout" to different blocks, constructing various sub-networks that capture di- verse low www-robabilltty regions.
construct multiple
Specifically, for a given binary mask m, sampled via stochastic block-dropping from the induced distribution p(m), the weak model's prediction is defined as:
(x, c, m) Dp 0 m).
where determines which blocks of the network parameters 0 are activated, forming latent sub- network during each forward pass. Naive 5²-Guidance is then expressed as:
D (x, () = Do(x: ()) A(Do(z c)) Do (x+ d))
(Do(x, c, m:) c,, Do(#, c)).
(3)
where m, ~ p(m) the binary mask for the i-th stochastic sub-network, w controls the strength of the self-guidance, referred to as the S Scale. Do(xz c. m;) represents the prediction from the i-th sampled sub-network. and the self-guidance signal is defined as its deviation from the full-model prediction. N denotes the total number of latent sub-networks sampled during each forward pass.
For the sampling distribution p(m), crucial consideration is to ensure its effectiveness and gener- alizability across different models. Our approach is predicated on the principle of identifying and preserving the model's structurally critical components. Based empirical analysis, we exclude these key blocks from the dropping process and then sample a proportion of the remaining blocks to be dropped. Detailed ablations regarding the stochastic sampling design and its stability are provided in Section 4.
To validate our hypothesis. we conduct experiments on toy examples with ID and 2D Gaussian mixture data, as well as on real-world datasets (see Appendix B. for more details). As shown in Figure (d), compared to the original CFG, our Naive 52 -3-GGidaece not only leads to predictions that better fit the target distribution but also mitigates the drift phenomenon, thereby improving fidelity. This demonstrates that our method effectively refines the suboptimal results of CFG. Fur- thermore, compared to Autoguidance, 5²-Guidance eliminates the need for explicitly constructing weak models. By adopting this simple yet effective approach, it avoids generating results that lie in intermediate regions, thereby reducing mode confusion. These results provide strong empirical evi- dence that leveraging Naive Sirve.S2-Guuri -Guidaneee can significantly enhance both the quality and robustness of conditional generation.
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HPSv2.1 (%)
T2I-
221-ComBBench (%)
Qalign
Model
Method
Anime
Concept
Paint.
Photo
Avg.
Color
Shape
Texture
HPSv2.1
T21-Comp.
CFG
31.55
30.87
31.22
28.27
30.48
53.61
51.20
52.45
4.66
4.74
CFG++
31.57
30.76
30.96
27.54
30.21
46.39
47.18
46.33
4.68
4.73
SD3
APG
30.77
30.18
30.53
27.12
29.65
45.28
46.27
46.84
4.68
4.73
CFG-Zero
31.99
31.17
31.42
28.54
30.78
52.70
52.84
53.37
4,66
4.77
SEG
31.20
30.56
31.07
28.74
30.39
58.20
57.68
57.17
4.33
4.45
Ours
32.14
31.32
31.70
29.19
31.09
59.63
58.71
56.77
4.65
4.74
CFG
32.34
31.51
31,50
27.93
30.82
51.29
47.71
47.39
4,63
4.66
CFG++
31.99
31.02
31.36
27.32
30.42
38.05
37.52
34,87
4,65
4,58
SD3.5
APG
31.43
30.74
31.12
27.07
30.09
35.67
37.86
35.67
4.68
4.65
CFG-Zero
32.77
31.91
31.95
28.27
31.23
52.01
46.99
48.36
4.66
4,70
SEG
31.77
31.30
31,40
28.34
30.71
57.59
55.52
54.03
4.41
4,45
Ours
32.89
32.15
32.28
28.94
31.56
57.57
51.23
50.13
4.70
4.74
Table 1: Quantitative comparison in T2I generation. Our method establishes new state- of-the-art, demonstrating significant improvements even on highly competitive benchmarks. On HPSv2.1, benchmark where score margins are typically narrow, S -Guddance consistently outper- forms all baselines across every individual dimension. This lead is even more pronounced on T2I- CompBench, where our approach shows substantial gains compositional attributes like Color and Shape. Notably, Sh- 32-Guidance also achieves the highest near-highest aesthetic scores (Qalign) on both benchmarks, demonstrating its superior performance in visual quality. Higher scores (1) are better. Best results are in bold; second-best best are underlined.
3.3 S -UUDDANC IS SUFFICIENT
However, Naive S2-Guidance incurs significant computational overhead, which severely limits its practicality. In the process of constructing sub-networks, we find that constraining stochastic block- dropping within specific range allows sub-networks, even those generated by dropping different blocks, to consistently guide the model toward the ideal distribution (Figure 9).
Therefore, we propose a simplified approach: performing a single stochastic block-dropping opera- tion at each timestep for self-guidance. We refer to this approach as 52-Guidaace,, which achieves highly competitive results, At timestep f, 52 Guidance is expressed as:
(x,, = Da(#: ()) ((Do(m ()) Do (xr ())
w( Do(az c, m. - Do(ar lc).
(4)
The overall algorithm is summarized in Algorithm
We empirically validate the proposed S²-Guidance on toy examples with ID and 2D Gaussian mixture data, as well as on real-world datasets. As shown in Figure 3 (c), S2-Guidanee performs comparably to Naive $²_ Guidance. On both 1D and 2D Gaus- sian mixture distributions, it produces results that closely align with the ideal distribution, while exhibiting efficiency without significant degra- dation. Moreover, as illustrated in Figure (e, f), S S"-Guidance achieves highly competitive performance on real-world datasets, highlighting its practical effectiveness. To further analyze the stochastic block- dropping strategy, conduct a detailed experimental study in Section 4.5. Our empirical analysis reveals that, when the block drop ratio is maintained around 10% the network's 's blocks, the result- ing sub-netw nub-networks consistently enable the model to achieve better performance. This strategy proves effective across mainstream DiT (Peebles & Xie, 2023) architectures, leveraging the redundancy in the outputs to dynamically construct diverse stochastic sub-networks. Unlike explicitly constructed
Algorithm S.2-Guidance
Require: Trained denoiser Do, initial noise ar, guidance scale x, 52 scale w, number of timesteps T..
1: for = .,..., do
2:
m4 <- GennerateStochasticMsss]] Generate mask
3:
Duncona < Do( d,t)
4:
Deand < Do(xe, c,
5:
D, - Do(æ,, c, m+) # Sub-network prediction
6:
D +- Duncond + X(Deond Duncond) wD,, Deood)
7:
x,- + SchedulerStep(2 2,,
8: end for
9: return 200

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LPG
Zera
CPG
Sedrt
Telkang
Not glacs windo
hougaer Sulliane and
Figure 5: Sruuuineece consistently generates superior images in both aesthetic quality and prompt coherence. While existing guidance methods often produce artifacts, distorted objects, or fail to follow complex prompts (see boxes), our approach yields clean, coherent, and visually pleasing results without such flaws.
Breathaakang close-up of weren frezen i8 tile as golden threads of light ucave around creating dynasic flowing patters of
Figure 6: 5²-Guidance generates temporally coherent and physically plausible videos, over- coming key failures of CFG. Top Row: CFG struggles with plausible motion, depicting a truck that unnaturally slides sideways instead of driving forward (red boxes). Our method renders stable and realistic scene. Bottom Row: CFG fails to capture the full prompt, as the light does not weave "around her face" (red box) and lacks "glowing particles" (blue box). 52-Guidance faithfully pro- duces dynamic, visually rich scene adhering the complex description.
weak models, which enables the creation of sub-networks independently at different timesteps. This dynamic diversity introduces self-guidance throughout the diffusion process, allowing predictions evolve iteratively and steering the outputs toward higher-quality results.
selected affect the entire denoising process, stochastic block-dropping
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Benchmark. We perform comprehensive evaluations across three tasks: class-conditional image generation, text-to-image (T2I) and text-to-video (T2V) generation. For class-conditional genera- tion, we use ImageNet at a 256 x 256 resolution. For T2I evaluation, we use two popular bench- marks: HPSv2.1 (Wu et al., 2023b), benchmark designed to evaluate alignment with human pref- erences across 3,200 prompts in four styles, and T21-CompBench (Huang et al., 2023a) for assessing performance in complex scenes. In addition to the benchmark- speciffic evaluation metrics, we em- play Qalign (Wu al., 2023a) to compute aesthetic scores for more comprehensive assessment. For T2V evaluation (Liu et al., 2024; Ling et al., 2025; Feng et al., 2025a; Chen et al., 2025c), we adopt the standard prompts and evaluation metrics provided by VBench (Huang et al., 2024b).
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Total
Quality Score
Semantic Score
Suhject Consistency
Background Consistency
Aesthetic Quality
Iniaging Quality
Object Class
Appearance
Model
Method
Score
Style
CPG
80.29
84.32
64. 16
96.53
95.46
60,52
67.65
77.06
20.15
CFG+
80.35
83.58
67.43
96.70
93.28
59.02
69.14
70.06
19.75
WB
APG
70.83
77.13
45.61
96.45
95.39
49.42
64.39
59.02
20.01
STG
78.78
83,92
58.19
95.03
96.04
59.03
65.59
68.20
21.51
CPG Zero
80.71
84.51
65.55
96.33
94.56
59,69
69.05
78.16
20.31
Ours
80.93
84.74
65.70
96.57
95.80
60.52
68.19
78.09
20.59
Wan14B
73.76
97.66
14
Ours
82.84
84.89
74.65
94.21
97.56
68,78
67.77
89,08
22.27
Table 3: Quantitative comparison on VBench. SS-Guidance consistently outperforms mainstream methods on both Wan-1.3B and Wan- models. While evaluated on all 16 dimensions, this table shows representative subset of 9 key metrics. Our method achieves the highest Total Score and demonstrates significant improvements. Best results are in bold; second-best are underlined.
Baselines. For T2I task. employ the high- terh-performing Stable Diffusion 3 (SD3) (Esser al., 2024) and SD3.5 (AI, 2024). For T2V task, we utilize the latest Wan- 1.3B and Wan- 14B models (Wan et al., 2025). Furthermore, demonstrate the versatility of our guidance approach, conduct a comparative analysis not only against original CFG but also with five state-of-the-ar methods: CFG+ (Chung et al., 2024), CFG-Zero (Fan al., 2025), APG (Sadat al., 2024), STG (Hyung et al., 2025) and SEG (Hong, 2024). See Appendix B.2 for additional evaluations and Appendix B . for implementation details.
4.2 CAASS-CONDITIONAL IMAGENET GENERATION
Method
18
FID.
Evaluated on ImageNet 256 x 256 with a pretrained SIT-XL.
Baseline
125.13
9.41
model (Ma et al., 2024a), 52 S2-Guidance demonstrates clear su- periority over both CFG and other advanced guidance strate- gies (many of which are not designed for advanced flow-based models and thus struggle perform well (Fan et al., 2025)). As shown in Table 2, our method achieves the best perfor- mance, attaining both the highest Inception Score of 259.22
w/ CFG
258.09
2.15
w/ ADG
257.92
2.37
w/ CFG++
257.04
2.25
w/ SEG
258.35
2.29
w/ CFG-Zero
258.87
2.10
Ours
259.12
2.03
for image diversity and fidelity, and the lowest FID of 2.08 for perceptual quality and distributional alignment.
Table 2: Quantitative evaluation
on ImageNet 256 x 256 dataset.
4.3 TEXT-TO-IMAGE GENERATION
The quantitative comparisons are presented in Table 1. On HPSv2.1. 32-Guidance achieves the best performance not only in average scores but also across all individual dimensions, demonstrating the effectiveness of our method. By steering the sampling trajectory away from suboptimal paths inherent in CFG, Su-Guidaece achieves better alignment with human preferences. The performance on 121-CompBench further highlights the strength of approach, showcasing its effectiveness in handling complex generation tasks. Moreover, the high aesthetic scores confirm our method's ss ability to produce images with superior visual appeal.
The qualitative comparisons are presented in Figure 5. Compared to CFG and other methods, S Guidance achieves significant improvements in both visual quality and semantic coherence: it pro- duces higher-quality images with finer details and better semantic alignment with text descriptions, a result consistent with our toy examples.
4.4 TEXT-TO-VIDE GENERATION
The quantitative comparisons presented in Table 3. achieves the highest Total Score (80.93), outperforming all basclines. We further conduct experi- ments the larger Wan-14B model, demonstrating significant improvements compared to CFG.
On the Wan-1.3B model, 52-Guidance
The quantitative comparisons are presented in Figure 6. Our method generates videos with sub- stantially improved quality and coherence. The examples highlight that S2- 5²-Guidance effectively addresses two critical failures of original CFG: the loss of physical plausibility in object motion and the inability to adhere to complex, compositional prompts. Consequently, our approach yields
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teing
(1)
(h)
(6)
(9)
Figure 7: Comprehensive ablation analysis of Guiddaneee (a) Comparing aesthetic scores of S Guidance and CFG across various guidance scales (A). (b) Analyzing aesthetic scores of SR-Guidance across various self-guidance scales (w). (c, d) Comparison of our stochastic block- dropping strategy against dropping single, fixed block for the SiT and DiT architectures, respec- tively. Performance is measured by the FID-IS trade-off, where the lower-right corner indicates better balance. (e) Ablation on the application range of 5²-Guidance. The x-axis represents the width of the central interval of of noise levels where block-dropping is applied (e.g.. value of 0.2 corresponds to the central 20% the denoising process).
videos that not only more physically realistic but also demonstrate superior prompt coherence, faithfully realizing the user's creative intent.
We further perform ferred over all baselines in terms of both visual quality and prompt alignment. Full details are presented in Appendix B.3.
study for both T2I and T2V generation. Our method is significantly pre-
4.5 ABLATION STUDY
Performance across Different Guidance Scales. We conduct experiments to compare S?- Guidance with CFG across various guidance scales, focusing on aesthetic scores. As shown in Figure 7 (a), 52-Guidance consistently outperforms CFG across wide range of scales. Unlike CFG, which shows significant performance variance depending on the scale, our method exhibits stability and achieves high performance with minimal sensitivity to guidance scales. Notably, in most cases, our method even surpasses the best performance achieved by CFG, demonstrating its robustness.
Analysis of Block-dropping Strategy. We conduct series of experiments thoroughly analyze the effectiveness and robustness of our block- chook-ig strategy. First, to investigate the importance of individual block, we perform experiments on diverse model architectures. We drop single, spe- cific block throughout the entire denoising process to obtain the sub-network prediction and compare its performance against our method. shown in Figure (c,d), dropping the initial block consis- tently leads to performance degradation across both models. However, for the remaining blocks, this block-wise ablation does not yield universal rule. We find that the optimal block to drop varies sig- nificantly across different architectures, challenge also highlighted by AutoGuidance (Karras al., 2024). contrast, our method eliminates the need for such complex tuning. Its simple stochastic strategy automatically outperforms most meticulously selected fixed configurations. Furthermore, inspired by (Kynkäänniemi et al., 2024), we analyze the optimal application interval for block- dropping. As shown in the Figure (e), applying block-dropping within the central 80% interval of noise levels yields robust performance. Our method reduces FID compared to CFG and often outperforms the top- performing configurations derived from prior block-wise ablation.
We conduct experiments to analyze the scale of S²-Guidance w as shown in Figure 7 (b). When is set smaller value, improves the aesthetic score. However, since CFG has already produced suboptimal result, using a larger w tends to overadjust, leading to a decline in quality.
Effeet of 52 Scale w.
Analysis of Se-Guidance and Naive 82 -P-Guidance. We compare applying block-dropping once versus multiple times per sampling step. Empirically, increasing the number of applications yields diminishing returns in aesthetic scores (see Figure in Appendix). We therefore conclude that a single application per timestep is sufficient, striking effective balance between high performance and computational efficiency. See Appendix A.I for the theoretical analysis and Appendix B. 2 for further visualizations.

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Effect of Drop-Ratio. We investigate the impact of the drop-ratio the SD3.5 model with 24 blocks. As shown in Table 4, when the number of dropped blocks is limited to 3/24 (approximately 10%), the aesthetic score remains stable relatively high level. However, dropping more blocks leads to a gradual decline in performance. Empirically, we observe that a drop-ratio of about 10% significantly improves performance.
Visual Analysis of Block-Dropping Impact To intuitively address concerns about how dropping blocks affects the final output, we provide a visual analysis Figure 15 (Appendix B.2). This figure presents the results of an extreme test case on the SiT-XL model for the ImageNet 256x256 task. In this setup, for each of the 28 generated images, a single, fixed transformer block was dropped for the entire duration of inference process. As can be observed, the resulting images exhibit remarkable visual consistency and coherence, with no single dropped block leading to severe artifacts or collapse in quality. This provides compelling visual evidence of the model's 's inherent robustness against block-level perturbations.
Stochastic Block- Block-Dropping
Num.
Aes.
0
4.618
1
4.652
2
4.643
3
4.616
4
4.531
Table 4: Effect of the number of dropped blocks aesthetic scores.
Computational Cost and Peak Memory We conduct direct comparison of FLOPs, runtime, and peak memory requirements against standard CFG. The benchmark, performed on a text-to- image task with 28 inference steps, summarized in Table 5. The results show that our 5²-Guidance incurs an overhead of approximately 40% in both runtime and computational cost. While this entails a notable overhead, we posit that is justified by a superior performance-effien efficiencfff trade-off, as we demonstrate in the subsequent analysis. Notably, peak GPU memory allocation remains unchanged. This is because the two forward passes within each denoising step-one for the full model and one for the wub-network-ane are executed sequentially. The memory from the first pass is released before the second begins, ensuring the peak memory footprint does not exceed that of a single standard CFG evaluation.
Method
Total Runtime (s) Transformer FLOPs (TFLOPs) Peak GPU Memory (GB)
CFG Ours
29.2
168.4
3-3..
40.2
237.6
<3388
Table 5: Computational cost and memory comparison.
Serformance-Efffciienn Trade-off While our method entails a computational overhead, we ar- gue it is justified by a superior cerrformance-efffcciee trade-off, analyzed in Figure 14 (Appendix B.2). This figure plots the HPS Score against a normalized computational cost, where the cost for S²-Guidance is scaled by factor of 1.4 account for its 440% overhead per step. The results clearly show that our method establishes a more favorable performance-<ffiieee frontier, consis- tently achieving higher performance for any given computational budget. For instance, Su-Gddd with only 20 inference steps (equivalent cost of 28) surpasses the HPS score of standard CFG with 60 steps. This analysis compellingly demonstrates that our approach is more practical and advanced choice for maximizing generation quality within given computational budget.
CONCLUSION
In this work, we propose 52- -uuiddd,,,,, training-free stochastic self- guidanee method that enhances diffusion transformers by improving the CFG mechanism. We first conduct an empirical analysis of CFG, revealing that often generates suboptimal results. Building on this insight, we introduce S Guidance, which leverages stochastic block-dropping during the forward pass to effectively guiding the model away from potential low-quality predictions, thereby improving fidelity. Theoretical anal- image, text-to-image and text-to-vidd video
ysis and extensive experiments, including generation across multiple models and benchmarks, demonstrate that 52 -Guddance delivers superior performance, consistently surpassing CFG and other advanced guidance strategies.
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Chenyang Zhu, Kai Li, Yue Ma, Longxiang Tang, Chengyu Fang, Chubin Chen, Qifeng Chen, and Xiu Li. Instantswap: Fast customized concept swapping across sharp shape differences. arXiv preprint arXiv:2412.01 197, 2024.
A APPENDIX
OVERVIEW
This appendix is divided into three main parts, covering method details, experimental supplements, and future work.
Appendix A
The first part focuses the details and discussions of the methodology, including:
A principled derivation of Naive 5²-Guidance from a Bayesian perspective.
A detailed analysis berween ---iddaance and Naive 52 -Guidddaeee
Appendix B
The second part provides supplementary information about experiments, covering:
Explanation of the toy example and additional experimental results.
More comprehensive evaluation and ablation study.
User study.
Further implementation details of the experiments.
Detailed Prompts for the Experiments.

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Appendix C
The third part briefty discusses future work and potential applications.
A EXTENDED DISCUSSION AND ANALYSIS OF OUR METHODS
A.I A PRINCIPLED DERIVATION OF NAIVE S2-GUIDANCE FROM A BAYESIAN PERSPECTIVE
In this subsection, we provide principled theoretical foundation for our proposed Naive Stochastic Sub-network Guidance (Naive 5d 3 method. We move beyond a heuristic interpretation and formally derive our approach by drawing direct line to the principles of Bayesian inference, established in the seminal work "Dropout (2016). Our central argument is that Naive S²-Guidance is not merely inspired by Bayesian ideas, but can be derived as principled mechanism for correcting the predictions of deterministic model by leveraging its own epistemic uncertainty.
Bayesian Approximation" by Gal & Ghahramani
A.1.1 FOUNDATIONAL BAYESIAN FORMULATION
Let D G be our training dataset. A fully Bayesian approach generative modeling would seek to compute the true posterior predictive distribution for new sample given condi- tion e:
pD]a c, D, D) - / p(D)z,,, ())(O)
(5)
) is the true posterior distribution over these param- eters, and p(D (re c, 0) is the likelihood of a specific prediction D given parameters 0. The true posterior is given by Bayes' theorem:
where O E 2 are the model parameters, (D)
D) ((D(O)p(O)
(()))(()) p(D) Jo (D 0' ((')) "do'
(6)
The integral in the denominator, known the marginal likelihood or model evidence, is intractable
for deep neural networks. To circumvent this, we employ Variational Inference (VT), introducing tractable approximate posterior distribution (o) (parameterized by Φ) to approximate p(0)D). We
minimize the Kullback-Leibles (KL) divergence between these two distributions:
Φ² arg min (L){(())l
arg min / (0) log ( d0
(8)
p(0)D)
(0) log (o())
arg min
4
d0
((D)))p(()
= arg min (g (l))) - (0) (Dg)()(0)))
(10)
Minimizing this objective equivalent to maximizing the Evidence Lower Bound (ELBO), LELBO- The work of Gal & Ghahramani (2016) provides the theoretical grounding for interpreting stochastic network perturbations, such as dropout, as a form of this Bayesian optimization.
In work, generalize this concept from neuron-level dropout to block-level dropout. Each binary mask m; ~ p(m) applied via stochastic block dropping effectively samples a specific set of weights 0 0 o m, from this approximate posterior, which we denote simply as (()).
A.1.2 MONTE CARLO ESTIMATION OF THE APPROXIMATE POSTERIOR PREDICTIVE
The prediction of a single sub-network, Do c, m.). is legitimate sample from the approximate posterior predictive distribution, P. (D] e):
Da(#; c, m.) & D(x, c; 0,), where (,~g(0)
(11)
The first moment of this distribution, the posterior mean pposts theoretically defined as the integral over the variational distribution:
Apost (2t c) 2 Eycor[D(x c; 0)] / D(aa | ())))())d
(12)
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Since this integral is analytically intractable for deep neural networks, rely Monte Carlo integration to estimate it. The empirical average computed by our algorithm serves as this estimator:
N Do(az |c, c,m.).
Appost (e
c)
R
(13)
N
Computing high-dimensional integrals via empirical averaging over diverse predictive hypotheses is standard practice in deep learning. This paradigm is supported by extensive literature, including explicit methods like Lakshminarayanan et al. (2017); Huang et al. (2017); Gal & Ghahramani (2016); Lei al. (2023); Wen et al. (2020). These works collectively establish that aggregating predictions from stochastic sub-states or ensemble members effectively approximates the predictive posterior. Our algorithm direct application of this principle to the sub-networks induced by block-dropping.
This posterior mean, Mpost represents the "center of mass" of the model's belief. The second central moment, the variance, quantifies the epistemic uncertainty:
(ar, I c; 0)] = Ey(o) [D( 0) -ppot)
1
(Do(a) P (xx))
(14)
N
Our central hypothesis is that low-quality generative outputs often arise in regions of high epis- temic uncertainty. In such regions, the posterior mean, ploost often corresponds to a "safe," but ultimately low-quality output (c.g.. blurry artifact). The deterministic MAP estimate, Do(x; c), however, might be unjustifiably confident in these very regions.
A.1.3 DERIVING S2- -GUIDANCCE AS AN UNCERTAINTY --WARE CORRECTION
Based on this hypothesis, we formulate principled correction to the Classifier Guidance (CFG) prediction, DerG- The standard guidance is:
Dero(x2 | e) = Do(x, ()) ADoo(r, c) Dz 6)).
(15)
We define our corrected prediction, "sra" c). as the solution to an optimization problem where seek prediction that remains faithful to the original guidance while being repelled from the center of uncertainty. Let us define a correction vector AD. We propose that this correction should be in the direction opposite to the posterior mean, which acts as the locus of uncertainty-induced artifacts:
AD -w ( c),
(16)
where w is a scalar controlling the magnitude of the repulsion. The corrected prediction is thus the linear superposition of the original guidance and this correction term:
D (x+ c) A Dera (x: c) + AD,
(17)
(wr c) - w Mpooa (Zt c)2
(18)
Du(az ()) + A(Da(Iz (I, c) Do d))
Standard CPG
(ey (D c; 0)
(19)
Uncertainty Aware Repulsion Term
Substituting the Monte Carlo approximation from Eq. 13 into Eq. 17, recover our full Naive S2-Guidance formulation:
D (z. () Do(xz () + X(Do(z (c) -Do(0 Φ))
N
w
Do c. m.).
(20)
N
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1.1.4 THEORETICAL INTERPRETATION AND DECOMPOSITIONS
This derivation provides much deeper understanding of Naive S 32-Guidance.
Decomposition of Predictive Components. We can rearrange Eq. 19 analyze the contribution of each component to the final prediction:
m (xt c) = (1- (1-)) () + AD |
w Mpoot (Ite c)
(21)
ADo(ar (c) + D) (
MAP Guidunce
Unconditional Prior
W Hpost (Zt lc).
(22)
Bayesian Correction
This shows a clear trade-off: we leverage the strong guidance from the conditional MAP estimate (Da(zz c)) and the unconditional prior (Do(a, Φ)). but temper both with a Bayesian correction term that represents the consensus diverse committee of model hypotheses. acts regularize the overconfidence of the single MAP estimate.
A Gradient-Space Perspective. In diffusion models, the guidance is applied in the noise predic- tion space. Let co co(x,,)) c) be the model's noise prediction. The standard CFG-guided noise ECFG is:
Ccra(z,, c) = co(xe; Φ) + X(co(z1, co
co(xt, 6)).
(23)
Our method introduces correction term directly in this space. Let (Do c) = E (co (20, be the posterior mean of the noise prediction. Our corrected noise prediction becomes:
0)]
E c) = (cFa( c) - w "pusal (Iet, c)
(24)
=crr((x) w
Σ N
co 6.)
(25)
This reveals that Naive 5 S performing a direct modification of the guidance vector at each step of the denoising process. The repulsion from the "center of uncertainty" is not an abstract concept but a concrete vector subtraction in the high-dimensional noise space.
Connection Negative Ensemble Distillation. Our method can be framed as novel form of negative distillation applied inference time. Standard ensemble distillation trains single model to mimic the average output an ensemble. In contrast, Naive S²-Guidance uses the ensemble's average prediction (/pps)) not target to be imitated, but anti-target to be actively repelled. mechanism is a new and principled way to harness the wisdom of an
This "distillation-reeeuee ensemble without collapsing its mean.
In summary, Naive S²-Guidance a theoretically grounded method that leverages the principles of Bayesian model averaging and uncertainty quantification. operationalizes the insight that high- quality generation requires not only strong conditional guidance but also mechanism to actively avoid regions of high model uncertainty. Our derivation shows that subtracting the Monte Carlo av- erage of stochastic sub-networks is direct and principled way to implement this avoidance, thereby correcting for the inherent limitations of single, deterministic generative model, as shown in Fig- ure 9.
A.2 COMPARATIVE ANALYSIS S²-GUIDANCE AND NAIVE SPUGUIDANCE
Our investigation into the behavior of sub-networks reveals crucial property. We find that when the stochastic block- dlckkdrroppnng ratio constrained within a specific range, the guidance provided by different sub-networks appears remarkably consistent. As illustrated in Figure 10, even when different blocks are dropped to form distinct sub-network configurations, their individual guidance effects the model's output distribution exhibit strong similarity.
4

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4.670
4.665
Average Aesthetic Score
4.660
4.655
4.650
4.645
1.610
4.635
Ours
4.630
1
5
10
15
20
25
30
Num of S3-Guidance Per Timestep
Figure 8: Aesthetic score gains brought by increasing the number of forward passes with stochastic block dropping at each time step.
This consistent behavior motivates to formalize the relationship between the two methods using the principle of unbiased estimation.
Let 0 be the model parameters and p(m) be the distribution of binary masks. Following Gal & Ghahramani (2016), the stochastic block-dropping process induces variational distribution (()) over the parameter space. We define the Theoretical Expected Guidance (the population mean) as the exact predictive mean under this induced distribution:
g" a w
(6)) (D(s, c; 0)] = w Em-p(m) [Do(ze m)].
(26)
Naive 52-Guidance approximates this target using a Monte Carlo average of N i.i.d. samples:
N
G Naive
Do | c, m.).
(27)
By the linearity of expectation, it holds that E[G g*.
In contrast, our simplified 52-Guidance employs stochastic guidance term from a single sample (N = 1):
G Guidance w Do(2, (z; m.), where m, p(m).
(28)
We formally derive that 2 Guuddnne is also an unbiased estimator of the same theoretical target G":
( GG Guiddance = pm Do I c,)) g
(29)
Since g -GGidance N[Gvaive]] = G*, both methods are mathematically unbiased Monte Carlo estimators of the same target, differing only variance. While G Guidance naturally exhibits higher variance per step compared to the ensemble average GNaive. the iterative nature of diffusion sampling effectively performs temporal integration. This smooths out the stochastic noise over the trajectory (as confirmed by our variance analysis in Appendix B), confirming that a single stochastic sample is sufficient and theoretically justified.
Further experiments, such repeating the process with small number of samples (as shown in Figure 8), corroborate this perspective by demonstrating diminishing returns, validating the practical efficiency of our approach.

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Figure 9: Visualization of Denoising Trajectories on the 1D Bimodal Gaussian Data. Each panel shows the paths taken by different guidance methods generate samples targeting the ground truth modes at -4 and 4. The y-axis represents the denoising timestep (from start to end), and the x-axis shows the predicted sample value. While standard CFG and Autoguidance improve upon the unguided baseline, they consistently fail to reach the ground truth. In contrast, both Naive S2-Guidance and our final S--Guidance method successfully steer the generation process to the correct endpoints. The more direct paths of our methods indicate more accurate guidance signal throughout the entire denoising process.
B MORE DETAILS ABOUT OUR EXPERIMENTS
TOY EXAMPLES
B. MORE RESULTS Toy EXAMPLES
To further analyze the guidance mechanisms, we visualize the full denoising trajectories for the ID Bimodal Gaussian Distribution in Figure The figure illustrates that while standard CFG and Autoguidance improve upon the unguided baseline, their final predictions consistently deviate from the distributions centered -4 and 4. This visually demonstrates the mode-shifting problem discussed in the main paper.
stark contrast, the paths both Naive Se-Guidance and our final S3-Guidance method are more direct and successfully converge to the correct endpoints. This suggests that our self- signal effectively corrects the generation path at each timestep, preventing the model from settling in the suboptimal regions favored by other methods.
B.1.2 NAIVE S -GGGIDAN VERSUS S SPGGUDDANCE IN TOY EXAMPLES
In our methodology, we first proposed Naive S2-Guidance, which averages the predictions from multiple stochastic sub-networks create robust negative guidance signal. However, this approach carries significant computational cost. To address this, introduced our final, more efficient $a. Guidance, which uses only a single stochastic sub-network per timestep.
To validate that this simplification does not cause meaningful performance degradation, we con- duct direct comparison on the 2D Gaussian mixture. As illustrated in Figure 10, the sample distri- butions generated by both methods qualitatively indistinguishable across multiple independent runs. Both approaches effectively guide the generation process to the correct modes and prevent the mode collapse issues seen in standard CFG (see Figure 3 in the main paper).

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Given the negligible difference in performance, the substantial computational advantage of S Guidance makes it the far more practical and efficient choice than the naive variant. This finding strongly supports adoption of the simplified approach our final method.
B.1.3 DETAILS OF TOY EXAMPLES
Below are the implementation details for the experiments on both synthetic and real-world data, as referenced in the main paper. All experiments were conducted using class-balanced datasets to assess the performance of our method.
1-D Bimodal Gaussian Distribution: This experiment was designed test the model's ability to stably and completely capture both modes of a bimodal distribution. The ground- truth data an equally-weighter mixture of two Gaussians. The diffusion model, param- eterized by standard neural network, was trained for iterations reconstruct the target distribution. Analysis involved visualizing the final sample distribution and denoising tra- jectories to show that S Guiddnnce consistently covers both modes, whereas the baseline may exhibit instability or mode preference (see Figure the main paper).
2-D Gaussian Mixture (4-Modes): This experiment assessed the model's capacity to generate samples from a disconnected, multi-modal manifold. The data consisted of an equally-weighted mixture of isotropic Gaussians, with means located at (-4, -4), (-4, 4), (4, -4), and (4, 4). The analysis focused on the final distribution and denoising paths to demonstrate that -Guidddee successfully captures all 4 distinct modes, improv- ing upon the baseline's s mode coverage.
Real-Image Data (CIFAR-10): To validate S -Guiddancc on nigh-dimensional data, we used a classebalanced dataset from CIFAR-10, consisting of 5,000 'horse' images and 5,000 "car' images. The diffusion model employed a neural network parameterization common for image tasks. The primary goal of the analysis was to assess the quality and class-separability of the generations. To this end, we generated 3,000 images and visual- ized their CLIP (ViT-B/32) features in 2-D using t-SNE. The resulting plot demonstrates that 52-Guidance produces more distinct and well-separated class clusters compared to the baseline, indicating higher-quality and less ambiguous generations (see Figure in the main paper).
B.2 EXTENDED EVALUATIONS
Experiments using Flux. In addition to the main experiments conducted with SD3 and SD3.5, we further evaluate our method using Flux (Labs, 2024), a ttate-of-the-ar (SOTA) model for text-to- image generation. Note that Flux a CFG-distilled model, meaning that directly applying classifier- free guidance (CFG) may lead to different results. We use a De-distilled version of Flux (Labs, 2024) in our experiments. Additionally, we follow the same benchmark setting as HPSv2.1 to ensure consistency and comparability.
HPSv2.1(%) 1
Method
Qalign
Anime
Concept Art Paintings
Photo
Avg.
CFG CFG (1.4x NEE)
31.29
29.85
30.03
28.16
29.84
4.65
31.59
30.10
30.35
28.47
30.13
4.68
Ours
31.48
30.21
30.48
28.88
30.26
4.70
Table 6: Quantitative evaluation of CFG and our approach using Flux under the HPSv2.1 benchmark. The HPSv2.1 grouping evaluates different styles, while Qalign measures aesthetic quality. Higher scores (t) are better. Best results are in bold.
The results in Table 6 show that method consistently outperforms the baseline CFG across different categories, including Anime, Concept Art, Paintings, and Photo. Specifically, we observe average improvement of 0.42, highlighting the robustness and effectiveness of our approach.
For more qualitative results, please refer to Figure 12 and Figure 13. These comprehensive results demonstrate the effectiveness of our proposed approach across various scenarios.

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Analysis of Variance from Stochastic Dropping To assess the stability of our method, quan- tify the output variance introduced by the stochastic dropping of network blocks. In our experiment, generate multiple images for the same prompt while keeping the initial noise seed fixed, thereby isolating the variance attributable solely to the stochastic dropping process. The quantitative results, presented in Table 7, demonstrate that the run-to-run variance is negligible. As shown, 52-Guidance exhibits a variance on the order of 10 and a coefficient of variation of less than 1%. This indicates an extremely high degree of stability and output consistency, confirming that the stochastic element does not compromise the reliability of the generation process.
Method
Mean (%)
Var.
Std. Dev.
Coeff. of Var.
CFG S2-Guidance
30.48
30.86
x 10-6
0.0026
0.84%
Table 7: Analysis of variance from stochastic dropping with a fixed initial seed.
B.3 USER STUDY
To quantitatively evaluate the perceptual quality and prompt fidelity of our method, we conducted comprehensive user study comparing -u-Guidance against four strong baselines: CFG (Ho & Salimans, 2022), APG (Sadat et al., 2024), CFG++ (Chung et al., 2024), and CFG-Zero (Fan et al., 2025). The evaluation was performed on images generated from diverse of diffusion models to assess the generalizability of our approach.
We recruited 14 participants with expertise in computer vision and generative AI. For each evalua- tion instance, participants were presented with a text prompt and the corresponding images generated by all five methods, displayed in randomized order to prevent bias. Participants were instructed to evaluate the results based on three key criteria:
Detail Preservation: The clarity, sharpness, and richness of details in the generated image.
Color Consistency: The naturalness, harmony, and realism of the colors.
Image-Tery Text Alignment: How well the generated image accurately reflects the content and intent of the text prompt.
For each criterion, participants were asked to select the image (or images) they found to be the most successful. This design allows for multiple selections if participant deems more than one result to be of high quality for given aspect, thereby capturing more nuanced assessment of performance.
The results of the user study are presented in Figure 11. The findings demonstrate clear and consis- tent preference for our proposed method, 52-Guidance, across all evaluated metrics. Specifically, in the Detail Preservation category, S 3F-GGidance was preferred in 32.5% of cases, significantly outper- forming the runner-up, CFG (18.3%). A similar dominant trend is observed for Color Consistency, where 52- S²-Guidance achieved a 29. 29.6% preference rate. Furthermore, for Image-Text Alignment, our method was chosen 31.1% 1% of the time, again marking substantial lead over all baselines.
Aggregating the votes, the Overall preference for 52-Guidance stands at 3 31.0%, confirming its com- prehensive superiority. This strong performance in human evaluations validates that 52-Guidance not only improves guidance from a theoretical standpoint but also translates to tangible and percep- tually superior generation quality that is easily recognized by human users.
B.4 IMPLEMENTATION DETAILS
To ensure fair comparisons, the implementation details of our experiments are as follows: For the text-to-image comparisons, we used SD3 and SD3.5 (Esser et al., 2024; AI, 2024) with the guidance scale set to 7.5. For our scale parameter use a guidance scale of 5.0. Similarly, our scale parameter w is set to 0.25. All other hyperparameters set the default configurations of the respective models. For the baseline comparisons, follow the original implementations provided in their official repositories. Specifically, APG (Sadat al., 2024) and CFG++ (Chung et al., 2024) are implemented using the
set it to 0.25. For the text-to-video comparisons, we
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versions that are integrated into the Diffusers framework. All experiments conducted on NVIDIA H20 GPUs with 96GB memory.
B.5 DETAILED PROMPTS FOR FIGURE
This section provides the prompts used generate the visual results presented in Figure The examples are referenced by their grid position in the figure (row, column).
(Top, 1) Astronaut in space (Video): "An astronaut flying in space."
(Top, 2) Floating Castle (Image): "A magnificent castle sitting high on a floatinn island above the clouds. Fluffy clouds surround the base of the island and form the text 'S2 Guidance Is All You Need' in a romantic, swirling style. The castle is adorned with towers, golden lights twinkling in the windows, and vines of blooming flowers climbing its walls. The scene is lit by a warm, golden light glowing from the sun, with a starry heaven faintly visible on the horizon."
(Top, 3) Abstract Portrait (Image): "The bold dramatic strokes of the painter's brush created a stunning abstract masterpiece a work of emotional depth and intensity."
(Top, 4) Cat with Rocket (Image): "A cat sitting besides a rocket on a planet with a lot of cactuses."
(Top, 5) Sports Car Driving (Video): "a car accelerating to gain speed."
(Bottom, 1) Woman with Colored Powder (Video): "A close-up of a beautiful woman's 's face with colored powder exploding around her, creating an abstract splash of vibrant hues."
(Bottom, 2) Woman with Umbrella (Image): "A woman sitting under an umbrella in the middle of a restaurant." "
(Bottom, 3) Man Running Beach (Image): "A man is running his hand over smooth rock at the beach."
(Bottom, 4) Clay Sheep (Image): "a red book and an ivory sheep."
(Bottom, 5) Bear Climbing Tree (Video): "a bear climbing tree."
C
FUTURE WORK
Our method is training-free and plug-and-ple requiring no additional fine-tuning or retraining of the underlying model. This enables seamless integration into existing pipelines and allows practi- tioners improve generation quality immediately at inference time. A promising direction is to further explore and generalize the proposed self-guidance mechanism, which designed to mitigate uncertainty regions within the model and thereby correct suboptimal trajectories. This capability suggests broad applicability to tasks where small deviations can lead to noticeable quality degrada- tion. In the image domain, (Zhu et al., 2024; Wang et al., 2024a;b) accuracy, where faithful execution of edit instructions is critical; (ii) human-preference alignment objectives (Chen et al., 2025a; Ma al., 2024c; Huang et al., 2025a); and (iii) high-fidelity image synthesis (Lei et al., 2025; Chen et al., 2025b) under challenging prompts. In the video domain, the same principle may translate improved motion co- herence and temporal smoothness, potentially reducing temporal artifacts (Wang et al.; Mao et al., 2025: Su et al., 2025; Wu et al., 2025b; Ling et al., 2025; Feng et al., 2025a; Zhu et al., 2026; Ma et al., 2025b). Finally, our observations are grounded in the redundancy of transformer architec- tures, suggesting potential applicability beyond DiT architectures. An important future direction to investigate whether analogous self-guidance mechanisms LLMs and MLLMs, as well as enable broader potential applications (Yao et al., 2025; Yu et al., 2025; Xie et al., 2024; Wang et al., 2025; 2026; Wei et al., 2023; Feng al., 2025b; Fang al., 2024; 2026; Jiang et al., 2025; Xie et al., 2026; Huang et al., 2025b; Chu et al., 2025a; 2021a;b; Huang et al., 2025c).
reliable internal guidance may improve (i) image video editing
improve faithfulness, robustness in

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Naive Si-Guidance
Si-Guidance
Figure 10: More Visual Comparisons of Naive Si-Guidance and S7-Guidance on the 2D Gaus- sian Mixture. Left: Naive S3-Guidance. Right: S2-Guidance. Each row corresponds to different random seed. The generated sample distributions are virtually identical, demonstrating that the per- formance gain from the computationally intensive naive approach is minimal. This justifies our adoption of the more efficient Sr-Guidance method.
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Detail Preservation
Color Consistency
32.5%
30.0%
30.0%
29.0 29.8%
25.0%
25. 25.0%
Preference 196)
Preference (96)
20.0%
18.3%
20.0%
19.8%
1B 3%
15.3%
15.5%
13.0%
15.0%
0.0%
0.0%
00
AMG
DrG
0rt-2er
Qune
00
APG
Or0-Zwn
Qum
Overall
Image Text Alignment
31.0%
30.0%
25.00
Preference (%)
Pruference (50
28.0%
10.7%
17.7%
16.0%
15.0%
14.7%
25.2%
10.0%
10.2%
5.0%
0.0%
CFG
ARG
DRG+
Cri-Zee
Ourg
96
oreet
cro-te
Figure 11: Human preference evaluation results for S=-Guidance against baseline methods. The bar charts show the percentage of times each method was selected as the best three criteria: Detail Preservation, Color Consistency, and Image- Text Aligmment, along with an Overall aggre- gated score. Our method, S2-Guidance, significantly preferred by human evaluators across all categories, achieving preference of over 29% in every dimension and surpassing 30% overall. This demonstrates its robust ability to generate perceptually superior images.

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CFG MIRCE
APG
CFG++
Zero
LOII
Ours
comical magazine poster ancient go pa lace.
CFG
APG
CFG++
Zero
Ours
giant sitting Starcraft map.
CFG
APG
CFG++
Zero
ours
WITI Fanicol tir
CFG
APG
CFG+
Ze re
Ours
The Image is titled "The End of the wor Ld" by Raymond Br Briggs.
CFG
APG
CFG++
Zero
Ours
A dining area features a wood table, chairs brown re refrigerator, light silver cabinet
CFG
APG
CFG++
Zero
Ours
lue backpack and a red car
CFG
APG
CFG++
Zero
Durs
wan on the Left microwave
Qualitative comparison S²-Guidance with baseline methods. Our method consis- tently generates images with superior visual quality, better prompt alignment, and fewer artifacts across a variety of prompts. For instance, 52-Guidance excels at stylistic replication (row 4), com- plex concept combinations (row 5).
Figure 12:
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CFG
APG
C
Zero
ours
small bathroom with small brown toilet next to white sink
CFG
APG
CFG++
Zero
O
O
U
An oval sink and rectangular mirror
CFG
APG
CFG++
Zero
Ours
An Ultraman preparing to take flight. Zero
CFG
CFG++
Frontal traitait of pink haired girl ing white tshirt and smi Ling.
CFG
APG
CFG++
Zero
Ours
Ànime Costa Laana by Studio Ghibli.
CFG
APG
CFG++
Zero
Ours
man wearing
Ba tman costume lds green glowing orb.
CFG
APG
CFG++
Zero
Ours
A red sun and yellow sunset
Figure 13: Further qualitative comparisons. Our approach demonstrates robust improvements in
both prompt fidelity and aesthetic quality. Key advantages include accurate attribute binding (e.g., "oval sink and rectangular mirror" in row 2), faithful character and style generation (rows 3-5), and superior handling of lighting and composition (rows 6, 7). 5²-Guidance consistently avoids the conceptual blending and visual artifacts that affect other methods.
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Performance and Efficiency Comparison
HP5 Score
CFG
0.200
50
Computational
Figure 14: Performance-Efficinnyy Trade-off' Analysis. This figure compares our method against CFG by plotting HPS Score function of computational cost. (Curves positioned further toward the top-left indicate superior methods.) The x-axis represents a normalized computational cost, where the cost for CFG equals its inference steps, while the cost for our method is scaled by factor of 1.4 to reflect its ~40% computational overhead. The plot illustrates that our method offers significantly better trade-off. For instance, our method with just inference steps (equivalent cost a 28) already achieves a higher HPS score than CFG at 60 steps. This demonstrates that our method yields substantial quality improvements for a comparable or even lower computational budget.
Figure 15: Impact dropping a single, fixed transformer block SiT-XL. Each of the 28 images corresponds to dropping specific block for all timesteps the ImageNet 256x256 task. The visual consistency across the grid demonstrates the model's robustness against block-level perturbations.
14


Reviews:
[{"review_id": "ZVzEqAinnd", "reviewer": "Reviewer_dNUy", "summary": "The paper introduces $S^2$-Guidance (Stochastic Self-Guidance), a training-free guidance algorithm for diffusion models. The authors have empirically shown that Classifier-Free Guidance (CFG) can produce suboptimal results. To address this issue, they proposed activating subnetworks of the same model through stochastic block dropping during denoising to obtain a “weak” prediction that corrects the CFG direction. The proposed $S^2$-Guidance improves fidelity across class-dependent ImageNet, text-to-image, and text-to-video generation, outperforming CFG and current guidance algorithms.", "questions": "- How large in the variance of the single stochastic block-dropping operation?\n- How dose $S^2$-Guidance affect long composition prompts, text rendering, or high-frequency textures?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is well-written and easy to understand.\n-  Activating an internal “weak” predictor by randomly omitting blocks during inference is simple but effective. Unlike distillation-based methods and external weak models, this algorithm is training-free and plug-and-play. It can therefore be applied directly to the generation process, which is currently based on CFG.\n- An additional stochastic forward pass per step keeps complexity low compared to many other alternatives with guidance or self-ensemble, making the ratio between quality and computational effort efficient in practice.", "weaknesses": "- In Appendix A, the authors provides an analysis of $S^2$-Guidance and Naive $S^2$-Guidance. The authros \"posit\" that $S^2$-Guidance is an approximately unbiased estimator of the \"expected guidance\" $G_{Naive}$. However, the \"unbiasedness\" of $G_{S^2-Guidance}$ is assumed, not shown. In addition, \n$\\mu_{post}$ denotes a Bayesian posterior mean, but the algorithm actually only defines an empirical average of the predictions of the sub-network obtained by randomly dropping modules, which cannot follw eq. (25). \n- The author must argue that the variance of $G_{S^2-Guidance}$ is small, or specify concentration bounds so that one guidance per step is reliable.\n- A randomly drawn sub-network leads to a deviation in the trajectory. Sensitivity and stability under randomness have not yet been sufficiently investigated in the paper.\n- For videos, stochastic guidance can lead to temporal flickering or shifts in object identity. Therefore, please add temporal stability metrics to verify the quality of the generated video.\n- The proposed $S^2$-Guidance requires an extra forward pass per step. Thus, FLOPs and peak memory requirements must be reported.", "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": "IqsSpzQRLG", "reviewer": "Reviewer_amct", "summary": "This paper introduces S²-Guidance, a training-free modification to classifier-free guidance (CFG) for diffusion models. At each denoising step, the method forms a stochastic “weak” sub-network by randomly dropping some transformer blocks and subtracts its prediction (scaled by ω) from the standard CFG result. The goal is to mitigate CFG’s over-confident or semantically inconsistent generations while preserving distributional fidelity. The paper reports consistent improvements on several text-to-image and text-to-video benchmarks with transformer-based backbones.", "questions": "1. Applicability to UNet/CNN architectures: please explain concretely how the stochastic sub-network would be constructed for UNet-style diffusion models (e.g., which modules are droppable without breaking shapes/skip connections) and provide some experiments in this direction to support the generality claim.\n2. Compute efficiency: please report runtime or FLOP ratios relative to standard CFG for a typical sampling trajectory, and include wall-clock time where possible.\n3. Theory: in the Bayesian derivation, why should “repulsion from the posterior mean” improve sample fidelity or score accuracy? \n5. Output variance: does the stochastic mask introduce noticeable variance across runs with identical seeds?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Training-free inference procedure that does not require auxiliary models.\n- Broad experimental coverage across multiple transformer-based diffusion backbones (images and video) with clear qualitative examples.\n- Ablation studies on several design choices (ω, drop ratio) and comparisons against a range of CFG variants.\n- The idea is easy to understand and implement for transformer architectures, and the empirical results are consistently positive.", "weaknesses": "- Computational overhead: per denoising step the method requires an additional denoiser call (three evaluations vs. two for standard CFG). The efficiency claim is mainly relative to a heavier “naive” multi-sample variant rather than to CFG. The phrasing of the term \"efficient\" in the main text is therefore slightly misleading.\n- Architecture dependence: all demonstrations are on transformer-based diffusion models and the mechanism relies on dropping entire residual blocks with fixed shapes. It is unclear how to apply the same stochastic sub-network idea to UNet/CNN backbones without nontrivial changes.\n- Theoretical framing is largely interpretive. The Bayesian idea discussed in the appendix is suggestive but not shown to tighten score estimation or likelihood in a formal sense.\n- Scope of validation: no compute/runtime comparisons versus CFG are provided, and the variance introduced by the stochastic dropping is not discussed.", "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": "ezuLIQKuL0", "reviewer": "Reviewer_2Qir", "summary": "This paper extends the idea of autoguidance, which generates negative predictions via stochastic dropout and improves sampling through extrapolation, by introducing block dropout to form negative predictions and formulate guidance. While selectively dropping certain blocks improved performance, it introduced a block selection problem, determining which blocks to drop. The authors address this issue by applying stochastic dropout during the sampling process, proposing S2-Guidance, and demonstrate improved performance on both GMM toy examples and real datasets.", "questions": "- In Eq. (3), the guidance is formulated by subtracting the ω-scaled negative prediction, differing from the standard extrapolation-based CFG formulation.\n- What is the rationale behind this design? Would applying similar ω-scaled subtraction to other bad-version guidance methods yield comparable effects?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The comparison and visualization of different guidance methods on GMM data are clear and well-presented.\n- Using block dropout instead of parameter dropout contributes to inference efficiency, akin to structured pruning, compared to conventional dropout.\n- The observation that the specific choice of dropout block has limited impact on performance is important.\n- Replacing manual block selection with stochastic dropout simplifies usage while maintaining or even improving general performance, making the approach more practical.", "weaknesses": "There are several concerns regarding the use of block dropout. Dropping entire model blocks could severely degrade prediction quality if applied to critical blocks, potentially corrupting the model’s behavior. While AutoGuidance adopts parameter-level dropout, this paper employs the stronger perturbation of block-level dropout without providing sufficient motivation or references for this design choice. \n\nIndeed, as mentioned around L250-L254, applying naive S2-Guidance to key blocks drastically reduces model performance, suggesting that the block dropout mechanism’s impact is under-analyzed. Providing a visualization of how predictions change when each block is dropped would substantially strengthen the motivation and understanding of this method. \n\nFurthermore, in L161-, the paper claims to offer a more general solution compared to previous weak model guidance methods, which relied on hand-crafted designs. However, since the proposed method still requires optimizing the dropout probability and potentially identifying key blocks to avoid, it may also involve a certain level of manual tuning, making the claim of being fully general somewhat overstated. \n\nAlthough S2-Guidance removes explicit block selection by introducing stochastic dropout per timestep, the paper does not discuss whether dropping key blocks during random selection leads to performance degradation or whether the method produces consistent performance across different runs. A discussion or variance analysis on this point would be valuable. In addition, since this method perturbs model weights, the paper should further clarify the advantages and motivation of block-level dropout over the stochastic parameter dropout used in AutoGuidance. \n\nFinally, the approach resembles PAG (Ahn et al., 2024), which applies attention masking to generate degraded samples and extrapolate them for guidance. It would be beneficial to include a comparison with PAG, as such a baseline could reinforce the paper’s empirical claims.\n\nMinor\n\n- In L158, the description of Hong et al. (2023) is inaccurate: the method performs attention-guided blurring of predicted samples, not blurring of attention maps themselves.", "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": "OVaZYmAw8G", "reviewer": "Reviewer_PEh4", "summary": "This paper proposes a general and elegant strategy to enhance generative performance by mitigating issues of low fidelity and semantic incoherence. Specifically, the authors empirically demonstrate that suboptimal predictions of diffusion models can be effectively rectified using the model’s own sub-networks, without requiring additional training or the integration of external modules. Building upon this observation, the paper introduces S²-Guidance, a novel method that leverages stochastic block-dropping during the denoising process to activate sub-networks for self-guidance. The experiments conducted on three tasks show the great performance of the proposed method.", "questions": "None", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear and Insightful Motivation. The paper identifies a fundamental limitation in classifier-free guidance (CFG)—its tendency to over-amplify conditional signals and distort the underlying data distribution. The authors’ analysis of this phenomenon using Gaussian mixture toy examples is both illustrative and rigorous. This strong diagnostic foundation naturally motivates the development of S²-Guidance as a self-correcting mechanism.\n\n2. Elegant and Minimalist Design. The proposed method achieves a notable balance between simplicity and effectiveness. By leveraging stochastic block dropping within the model itself, S²-Guidance creates intrinsic “weak sub-networks” that serve as self-guiding components. This design eliminates the need for externally trained weak models or additional data, while maintaining competitive performance and extremely low computational overhead. The minimal intervention required for implementation highlights the method’s engineering practicality and reproducibility.\n\n3. Strong Empirical Validation and Robust Generalization. The experiments convincingly demonstrate that S²-Guidance enhances both sample fidelity and distributional consistency across diverse models and tasks. The method consistently improves over CFG and performs on par with, or better than, external weak-model guidance approaches—all without additional training or tuning. The results underscore its robustness and broad applicability to various diffusion architectures.\n\n4. Well-Written and Structured Presentation.", "weaknesses": "1. Although the proposed method is simple yet efficient, its novelty remains limited. The authors introduce a more general form of the “weak” model; however, it still primarily targets essential problem-solving within existing frameworks rather than presenting a fundamentally new paradigm.\n\n2. While the inclusion of the additional sub-optional model contributes to performance improvement, it also introduces extra computational cost and resource consumption.\n\n3. The improvements in the text-to-video experiments are primarily reflected in enhanced visual quality. However, as shown in Table 4, the temporal consistency of the generated videos slightly degrades—particularly in terms of subject and background consistency. This suggests that the proposed method may offer limited benefits for video generation while demonstrating stronger advantages in image-level synthesis.", "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)