Title: Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy
Venue: NeurIPS 2025
Accepted: yes
Keywords: auto-regressive image generation

training-free

image generation
Abstract: In this work, we first revisit the sampling issues in current autoregressive (AR) image generation models and identify that image tokens, unlike text tokens, exhibit lower information density and non-uniform spatial distribution. Accordingly, we present an entropy-informed decoding strategy that facilitates higher autoregressive generation quality with faster synthesis speed. Specifically, the proposed method introduces two main innovations: 1) dynamic temperature control guided by spatial entropy of token distributions, enhancing the balance between content diversity, alignment accuracy, and structural coherence in both mask-based and scale-wise models, without extra computational overhead, and 2) entropy-aware acceptance rules in speculative decoding, achieving near-lossless generation at about 85% of the inference cost of conventional acceleration methods. Extensive experiments across multiple benchmarks using diverse AR image generation models demonstrate the effectiveness and generalizability of our approach in enhancing both generation quality and sampling speed.

Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy

Abstract

In this work, we first revisit the sampling issues in current autoregressive (AR) image generation models and identify that image tokens, unlike text tokens, exhibit lower information density and non-uniform spatial distribution. Accordingly, we present an entropy-informed decoding strategy that facilitates higher autoregressive generation quality with faster synthesis speed. Specifically, the proposed method introduces two main innovations: 1) dynamic temperature control guided by spatial entropy of token distributions, enhancing the balance between content diversity, alignment accuracy, and structural coherence in both mask-based and scale-wise models, without extra computational overhead, and 2) entropy-aware acceptance rules in speculative decoding, achieving near-lossless generation at about 85% of the inference cost of conventional acceleration methods. Extensive experiments across multiple benchmarks using diverse AR image generation models demonstrate the effectiveness and generalizability of our approach in enhancing both generation quality and sampling speed. Code is available at https://github.com/krennic999/ARsample .

1 Introduction

Autoregressive (AR) modeling, as the mainstream in language generation [3, 4, 5, 6], has recently demonstrate strong potential in visual generation [2, 1], offering improved scalability [7] and potential for unified vision-language modeling [8, 9]. In this paradigm, images are first quantized into discrete

token sequences [10, 11], which are then generated either token-by-token in a raster-scan order [8, 12], or in parallel through multi-token generation strategies [7, 13, 14].

Unlike diffusion or flow models that regress continuous tokens [15, 16, 17], AR models learn the probabilistic distribution over discrete vocabulary, and sampling strategies (e.g., topK , topp [18]) are required to specify a token, which is essential and significantly impacts the quality and characteristics of generated content. Within the area of language modeling, strategies have been proposed to augment reasoning capabilities and mitigate hallucinations, including logit shaping [19, 20], contrastive decoding [21, 22], leveraging model-specific features [23], and search-based methods [24, 25], which emphasize answer accuracy over generation diversity.

However, a clear distinction exists between image and language: images exhibit lower information density and highly non-uniform spatial information distribution , as shown in Fig. 2 (a), making language-oriented methods suboptimal for image generation. This mismatch often leads to a tradeoff between diversity in image contents and text consistency. As observed in [2, 26], increasing randomness ( e.g., high topK ) helps enrich visual content but compromises structural stability, leading to artifacts, distorted structures, or chaotic textures. Conversely, reducing randomness stabilizes structure and improves alignment, but often yields flat, oversmoothed details, or simplistic background. How to balance randomness and determinism during sampling is thus critical for high-quality image generation. Unfortunately, existing methods typically rely on uniform sampling approaches like fixed topK or topp , overlooking the inherent spatial information imbalance, which limits their ability to achieve high-quality images.

In this work, we aim to leverage the uneven distribution of information in images, and propose a sampling method specifically for autoregressive image generation . We observe that entropy of predicted logits effectively reflects information density in image during generation-low entropy corresponds to large homogeneous regions, while high entropy highlights content-rich areas such as foreground objects and complex backgrounds (see Fig. 3). An intuitive idea is to encourage higher randomness in low-entropy regions, while applying stricter sampling in high-entropy areas. This helps balance image richness with structural stability, and also allocating fewer inference resources to low-entropy regions, which enables further acceleration with minimal impact on generation quality. Unlike [27], which applies entropy to control sampling randomness in super-resolution to modulate stochasticity, our work leverages entropy to guide autoregressive generation dynamics.

Building on this observation, we propose an entropy-aware sampling strategy that adjusts token distributions dynamically during inference. By computing the entropy of each predicted token distribution, we assign adaptive temperatures-injecting more randomness in low-entropy (simple) regions and applying stricter sampling in high-entropy (complex) areas. This improves the balance between image quality, structural stability, and text-image alignment without additional training or inference cost. Moreover, our method generalizes well to a variety of autoregressive frameworks based on discrete token prediction, including mask-based and scale-wise generation. We also extend the entropy-aware idea to acceleration: by incorporating entropy-dependent acceptance in speculative decoding, we reduce inference cost to 85% of standard baselines with minimal quality loss. We summarize our contributions as follows:

Motivated by the observation that image information is sparse and unevenly distributed, which can be reflected by the entropy of tokens, we introduce an entropy-driven sampling strategy tailored for AR image generation that dynamically adapts sampling behavior based on entropy.

In contrast to conventional sampling methods like topK or topp , our approach enhances image quality and structural stability without modifying the model or increasing inference cost, and benefits multiple types of AR generation frameworks.

We further extend the entropy-aware perspective to speculative decoding, achieving a 15% reduction in inference time while maintaining visual fidelity across multiple benchmarks.

2 Related works

2.1 Autoregressive image generation

Early work [28] generates images directly at pixel level. Later approaches adopt a two-stage pipeline: images are first quantized into discrete tokens [10, 11], then generated with Transformers in raster

Top-k

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(2) k=16384; CFG=7.5

(3) k=1; CFG<2

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Top-k

Greedy

Random

(c) Quantitative Evaluation Across Settings

(b) Qualitative Comparison Across Settings

Avg. Spectral of Segments

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order [29, 30, 31, 32, 33, 34]. Recent efforts scale this paradigm with larger models and stronger conditioning. LlamaGen [1] provides class and text-conditioned baselines; Lumina-mGPT [2] and Anole [35] fine-tune Chameleon [12] for improved text-conditioned generation. Unified frameworks further bridge understanding and generation [8, 9, 36, 37] in a single Transformer. Meanwhile, image tokenizers have evolved for better reconstruction [38, 39, 40, 41] or multimodal integration [42, 43].

While proven effective, the vanilla autoregressive paradigm suffers from slow and rigid next-token prediction. To improve efficiency, recent studies explore more strategies, including multi-token prediction via random masking [14, 44, 45, 46], coarse-to-fine modeling [7, 13, 47, 48, 49]or hybrid approaches [50, 51]. Nonetheless, vector-quantized models still rely on sampling from token distributions, making generation quality sensitive to the sampling strategy.

2.2 Sampling strategies in autoregressive models

Transformers model the probability distribution over tokens, requiring specific sampling strategies to obtain concrete outputs. Common approaches in language modeling include topk [52] and topp [18] sampling, which truncate the candidate space by rank or cumulative probability. EDT [20] dynamically adjusts temperature based on entropy to balance diversity and precision. Other approaches explore repetition penalties [53], contrastive decoding [22], speculative decoding [54, 55], and search-based techniques [56, 25, 57, 58] to reduce hallucination or speed up inference.

In visual generation, a higher degree of randomness is often needed to produce more realistic and detailed content. LlamaGen [1] and Lumina-mGPT [2] demonstrate that much larger topk values than those used in language models help avoid over-smoothed and low-detail outputs. Recent methods [26, 59] apply speculative [54] or parallel decoding [60, 61] to accelerate image synthesis. PURE [27] designs a topk strategy based on token entropy and detail levels to improve autoregressive super-resolution. However, they overlook the highly uneven spatial information distribution in images during generation, and do not tailor decoding for autoregressive image generation.

3 Methods

In this section, we first introduce basic of autoregressive image generation in Sec. 3.1. Starting from the difference between image and text generation, we present our method from an entropy-based perspective by adjusting token-level randomness during generation (Sec. 3.2, Sec. 3.3), and further extend this view to acceleration (Sec. 3.4).

Entropy

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(a) Visualization of Generated Image & Entropy

(c) Temp. Sensitivity by Entropy Level

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9.50.81.215 2.02.5

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3.1 Preliminaries and motivation

Autoregressive image generation. In a typical autoregressive generation process, an image I ∈ R H × W × 3 is quantized into a set of discrete tokens ( x 1 , x 2 , ..., x h × w ) , where each token x i ∈ [ V ] , V denotes the size of the VQ-VAE codebook. The image tokens are generated sequentially by a transformer, with the i +1 -th token x i +1 conditioned on the previously generated tokens. This process is modeled as ∏ hw -1 i =1 p ( x i +1 | x 1: i ) , where x 1: i = ( x 1 , x 2 , ..., x i ) , and p ( x | x 1: i ) represents a categorical distribution over token at position i +1 . In text-conditioned generation tasks, the full image sequence is generated conditioned on a prefix of text tokens. At each step, a sampling method such as topK or topp is applied to select a token from p ( x | x 1: i ) . And choice of sampling strategy can significantly affect the quality of generated image, as illustrated in Fig. 2 (b).

Difference between image & text generation. Compared to text, the information density in images is lower and highly non-uniform . For example, images often contain large regions of solid color or visually similar content, while nearby tokens in text are typically distinct. To illustrate the difference, we segment the embedding sequences of both images and texts into equal lengths, compute the average frequency spectrum per segment, and visualize the distributions in Fig. 2 (a). The average frequency spectrum distribution of image segments is more dispersed, with a large amount of low-frequency areas; whereas textual segments demonstrate more compact and uniform distributions.

This discrepancy poses a challenge for sampling: fixed parameters like topK or topp , though effective in language generation, perform suboptimally when simply applying them across all image tokens. They fail to account for spatial variability, resulting in regional artifacts: overly deterministic sampling may lose fine details, producing flat regions, excessive smoothness, or simplistic backgrounds; while excessively random sampling compromises semantic consistency and structural coherence, causing artifacts, distorted limbs, or chaotic textures.

Relationship between entropy & image contents. We demonstrate that the entropy of predicted token distribution serves as an effective indicator of local information density in an image. Specifically, we compute the entropy ϵ of log-likelihood over all V codebook entries at each generation step as:

As shown in Fig. 3, regions with simple content ( e.g., solid colors) typically exhibit lower entropy, while more complex foreground ( e.g., objects, structures, and textures) areas have higher entropy. Low-entropy regions correspond to peaked distributions over a few tokens, indicating high model confidence. Conversely, high-entropy regions display more uniform distribution, reflecting greater uncertainty in token selection and higher information density. These observations validate entropy as a reliable proxy for measuring information density in images.

3.2 Entropy-aware dynamic temperature

Building on the observation in Sec. 3.1, we further investigate how regions with different entropy levels affect image quality and the optimal sampling strategy. Under a simple experimental setup, we adopt [1] to analyze the entropy distribution of logits during generation, discretize it into intervals,

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and adjust token temperature within each interval to control sampling randomness. We then examine the relationship between image quality and text alignment via FID and CLIP-Score as indicators, results can be seen in Fig. 3 (c). Our findings are as follows:

Across most entropy intervals, adjusting randomness leads to a trade-off between image quality and text alignment, especially for tokens in the entropy range of [2,8].

In high-entropy regions (>5), lower randomness helps improve text-image consistency.

In regions with extreme low entropy (<2), increasing sampling randomness consistently improves visual quality, while having negligible impact on text alignment.

These findings suggest that token-level sampling should be entropy-aware: during inference, tokens with lower entropy should be assigned relatively higher randomness to enhance the quality and visual richness of generated image, while high-entropy tokens should be sampled more cautiously to preserve clear structure, details and text alignment.

To better adapt to real-world inference, we introduce a dynamic temperature mechanism that adjusts sampling randomness on a per-token basis. Specifically, after computing the entropy of predicted distribution at each position, we determine a temperature value with predefined mapping function:

where ϵ denotes the entropy at current token position, T 0 represents the maximum temperature, θ sets the lower bound, and α controls the decay rate of temperature with increasing entropy. See Sec. 4.4 for further analysis and discussion. Subsequently, the resulting temperature T is then applied to rescale the predicted logits as follows:

Then, by applying softmax (˜ p i ) , the differences between logits of different tokens are amplified (when T <1) or reduced (when T >1), which makes the probability distribution more concentrated or spread out, achieving the dynamic adjustment of sampling based on the region's content distribution.

3.3 Adaptation to more AR models

Additionally, many recent methods deviate from strict next-token prediction and instead adopt paradigms such as mask-prediction or scale-wise generation. We show that the proposed entropybased strategy remains effective in these settings. After obtaining multiple token-level logit distributions from the transformer, we directly apply Eq. (2) to them. As shown in Table 1, this approach consistently improves performance across standard evaluation metrics. Moreover, for different paradigms, specific designs can be incorporated to further enhance performance:

Mask-prediction models. For mask-based models such as [44, 45], a full probability distribution over all image tokens is obtained at each forward step. After sampling, a dynamic masking mechanism based on token confidence is applied. We observe that this masking strategy also has a significant impact on the quality of the generated results (see Fig. 4). Specifically, a soft categorical distribution is used to select k tokens from all candidates to be accepted at the current timestep t :

Baseline

Ours

A boy in an orange t-shirt plays a video game.

A pink cupcake in a red liner with a star on top.

Baseline

Ours

A bride and groom cutting a wedding cake.

A large ornate clock with gold colored trim.

Baseline

Ours

Gold hexagonal glasses on a book.

Plush royal blue slippers with cozy texture.

where p is the predicted token probability, T is the dynamic temperature defined in Eq. (2), g ∼ Gumbel (0 , 1) is sampled from standard Gumbel distribution.

Here, TopK ( conf , k ) returns the k -th highest confidence score, and tokens with lower confidence are masked out. M t and M t -1 represent the accepted token masks at the current and previous timesteps, respectively, while ˜ M t -1 denotes the element-wise negation of M t -1 . The number of accepted tokens k at each timestep t is determined by a predefined scheduler. This design further encourages randomness in low-entropy regions while enhancing accuracy in high-entropy regions, leading to improved image quality.

Scale-wise models [7, 13, 47, 48] generate tokens within each scale simultaneously. We find that assigning greater randomness to earlier scales while reducing randomness at later scales yields more accurate results without compromising image richness. Specifically, we define a temperature term that decreases as the scale increases. For the tokens at s -th scale, T s is calculated as:

where s denotes the scale index and s ∈ { 1 , 2 , . . . , S } ; p s is the logits of tokens at s -th scale, with shape of h s × w s × V . T is dynamic temperature defined in Eq. (2). β controls the decay rate of T s across scales and is set to 0.3 in experiments.

A man wearing cowboy gear is sitting on a horse.

Bird sitting on top a chain link fence.

A black dog sitting next to a mirror-like pail.

Baseline

+Prob.

+Masking

Baseline

+Prob.

+Masking

A close up picture of a brown bear's face.

A man with a plaid hat, tie, dress shirt and glasses on.

Baseline

+Prob.

+Scale-wise

Baseline

+Prob.

+Scale-wise

3.4 Autoregressive acceleration

We further explore the use of entropy to accelerate autoregressive generation. Existing speculative decoding approaches [26, 59] typically generate multiple candidate tokens via a draft model, followed by a verification step using a target model. When the draft and target share the same model, the process reduces to comparing the confidence scores from two consecutive iterations. Specifically, the probability at the ( j -1) -th step, p ( x | x ( j -1) 1: i -1 ) , and the j -th step, p ( x | x ( j ) 1: i -1 ) , are compared. The acceptance probability of the token x i is then computed based on these two distributions:

In practice, a token is accepted if p > r , where r is drawn from a uniform distribution U [0 , 1] , which naturally balances randomness required for sampling diversity and accuracy during generation.

To make the process entropy-aware, we propose a simple modification to the acceptance rule. Since low-entropy regions are more predictable and allow higher randomness, while high-entropy regions require stricter verification, we scale the threshold r by an entropy-based factor ϵ/e , where e is constant. This dynamic adjustment enables more efficient generation based on local uncertainty.

To improve stability, we rewrite r as 0 . 5+( r -0 . 5) , treating it as 0 . 5+ U [ -0 . 5 , 0 . 5] , where the noise term controls acceptance randomness. We scale this term with a decaying factor (1 -λ · ϵ ) , where ϵ is the entropy, making high-entropy tokens more deterministically verified, while low-entropy areas retain near-uniform. Combining both strategies above, the final acceptance rule can be formulated as:

where ϵ is entropy at the current token, e and λ are constants set to 8 and 16, respectively.

A horse that is walking

around a field.

A middle aged black

woman standing behind

SJD

Ours

A cute baby calf with

soft brown fur.

Two teddy bears laying

side by side on a quilt.

A scenic river view with a

palace in the background.

A toddler holding a hair-

brush above his head.

Steps: 1609 (74.45s)

Steps: 1898 (85.01s)

Steps: 1543 (71.92s)

Steps: 1887 (84.26s)

Steps: 1491 (68.38s)

Steps: 1909 (86.29s)

Steps: 683 (27.16s)

Steps: 553 (22.93s)

Steps: 602 (25.01s)

Steps: 541 (22.56s)

Steps: 653 (26.86s)

Steps: 580 (23.72s)

a table full of bananas.

This dynamic acceptance criterion allocates inference budget more efficiently-being more permissive in confident regions and stricter in ambiguous ones, thereby reducing inference time with minimal performance loss. For detailed metrics and comparisons, please refer to Sec. 4.3 and Sec. 4.4.

4 Experiments

4.1 Implementation details

Four representative models are selected for comparison: vanilla AR model LlamaGen [1] and LuminamGPT [2] based on next-token prediction, mask-based model Meissonic [44], and scale-wise model STAR [13]. All models are evaluated under their original inference settings (e.g., CFG=4 and topK =2000 for Lumina-mGPT, CFG=7.5 for LlamaGen). We use LlamaGen's official Stage-1 model to evaluate the sampling strategy, while Stage-2 is used only for acceleration analysis due to its poor performance (FID 53.42, CLIP-Score 21.47), which makes quality differences hard to observe.

FID and CLIP-Score are tested on the MS-COCO 2017 [64] validation set to evaluate the image quality and prompt-following capability. Moreover, DPG-bench [65] and HPS [66] are adopted to assess the semantic fidelity and perceptual quality of the generated images. All experiments are conducted on A100 GPUs.

4.2 Sampling quality

As shown in Table 1 and Fig. 5-6, our dynamic temperature sampling strategy effectively adapts to regions with varying information density in the image, leading to more stable structures and clearer details in the generated outputs. Depending on the inherent sampling mechanism of each model, our method yields varying degrees of improvement across different approaches. In particular, it achieves an approximate 4-point gain on DPG for both Meissonic and LlamaGen, along with a notable enhancement in visual quality. In addition, integrating our approach with the masking- and scale-wise strategies described in Sec. 3.3 can further enhance generation performance. See Table 1 for results with '+Prob' (applying dynamic temperature to logits only), and '+Masking / +Scale-wise' (applying temperature based on mask or scale).

𝑇

!

𝛼

𝜃

(a) Ablation of Parameters in Eq. (2)

(b) Combination of Proposed Method with top-

𝑘

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25.95

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4.3 Inference acceleration

By integrating with existing vision-based speculative decoding schemes and leveraging entropy to automatically control the acceptance condition, our method saves about 15% inference cost with almost no loss in image generation quality compared to the approach in [26], as shown in Table 2 and Fig. 7. The entropy-based approach significantly reduces the number of inference steps and latency, while still maintaining comparable image quality to the original speculative decoding method.

4.4 Ablation study and discussion

Parameters in Eq. 2. The impact of parameters in entropy-aware dynamic temperature is provided in Fig. 8(a). It is observed that smaller ϵ or θ values lead to higher FID and lower CLIP-Score, primarily due to decreased randomness and overly deterministic sampling. Meanwhile, FID shows a trend of initially decreasing and then increasing as α increases. This is because α governs the proportion of different temperatures. When α is too small, most tokens are assigned very low temperatures, causing the FID to increase. Conversely, when α is too large, the image content becomes overly chaotic, resulting in increased FID. CLIP-Score consistently decreases as α increases.

Acceptance rate in Sec. 3.4. We propose to dynamically control the acceptance rate in existing speculative decoding methods based on the entropy of predicted distributions. By adjusting both the scale and randomness of the threshold r , we reduce latency while maintaining quality. As shown in Table 3 and Fig. 9, controlling only scale of r ('scale') reduces inference cost but degrades performance, especially image quality. In contrast, jointly tuning both scale and randomness ('+random') achieves a better trade-off, enabling high-quality generation with minimal inference overhead.

Compatibility with different AR models. Our sampling method brings notable performance gains for some models-for instance, DPG in LlamaGen and Meissonic outperforms baseline by over 3 points. In contrast, well-trained models like Lumina-mGPT benefit only marginally. This discrepancy stems from factors such as generation paradigm ( e.g. , inherent sampling limitations of mask-based methods discussed in Fig. 4), training datasets and iterations ( e.g. , whether has been thoroughly trained on large-scale data). Nevertheless, these models can still exploit entropy for further acceleration.

Combination with topK and CFG. We further analyze the performance of our method when combined with topK sampling and CFG, as shown in Fig. 8(b); results with topp and temperature are in the supplementary. By incorporating proposed method, FID metric becomes less sensitive to sampling parameters, enabling better fidelity while maintaining image-text alignment.

Factors affecting entropy. Unlike text generation with fixed tokenization rules, autoregressive image generation relies on pretrained tokenizers, and the underlying model differences-including

parameter scale, data quality, and training corpus-lead to varying entropy distributions and optimal sampling parameters. Empirically, higher CFG and larger resolutions lead to lower average entropy (see Table 4). Further analyses are provided in supplementary material.

Discussion of failure cases. Although our entropy-based dynamic sampling strategy brings notable performance improvements, we also observe several failure cases where the relationship between semantic information and the entropy map becomes less consistent (see Fig. 10). In some cases, regions such as human faces exhibit unexpectedly high entropy, while complex backgrounds receive lower entropy values. Consequently, adjusting temperature based on such entropy patterns may lead to structural distortions and overly smooth details. This ambiguity may potentially limit further performance gains, especially for models that have been carefully optimized.

5 Conclusion

In this work, we first point out the need for different sampling strategies in autoregressive image and text generation, given their distinct information distributions. Starting from this perspective, we find entropy effectively represents image information density, offering new possibilities for improving and accelerating image generation. As our method involve parameter adjustments without training, this approach could be further integrated into training or fine-tuning frameworks, potentially accelerating training, boosting inference speed, improving stability, and reducing hyperparameter dependence.

6 Acknowledgments

This work was supported by the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC. The AI-driven experiments, simulations and model training were performed on the robotic AI-Scientist platform of Chinese Academy of Sciences.

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A Introduction

Wefirst provide additional details of our method, including parameter settings and further descriptions, in Sec. B. Then, we present extended experimental results in Sec. C. In Sec. D, we conduct deeper analyses on entropy in relation to model behavior and image content, along with more visualizations of entropy maps. Sec. E discusses potential limitations and future directions. Lastly, we include more visual comparisons of the proposed method in Sec. F.

B Additional details of our method

B.1 Hyperparameter settings in Sec. 3.2

In Sec. 3.2, we propose to dynamically control the sampling temperature based on entropy. However, due to significant differences between base models, it is difficult to apply the same parameters across all settings. Therefore, we list the detailed parameters for each model in Table 5. For undertrained models such as LlamaGen stage1, higher randomness is required at low-entropy stages to avoid generating large areas of repetitive tokens. In contrast, well-trained models benefit from a smoother temperature schedule.

B.2 Detailed description of speculative decoding in images

We accelerate inference based on existing speculative decoding schemes [26] in Sec. 3.4, thereby further reducing inference cost without sacrificing output quality. Due to space constraints, we did not elaborate on the baseline speculative decoding methods in the main text. Here, we provide more details.

This method aims to accelerate auto-regressive text-to-image generation by allowing multiple tokens to be generated in parallel without training. Inspired by speculative decoding, SJD introduces a probabilistic acceptance criterion that compares the confidence of draft tokens from two consecutive iterations. In each iteration j , given a draft token x ( j ) i , SJD computes its acceptance probability based on the ratio between two conditional probabilities:

where r ∼ U [0 , 1] . Accepted tokens are fixed, while the others are resampled from a calibrated distribution:

This allows high-randomness sampling, crucial for image diversity, while significantly reducing decoding steps. SJD operates in a windowed, iterative manner and supports optional spatiallyinformed token initialization to further improve efficiency.

C Additional experimental results

C.1 Generation performance on an additional dataset

Since the COCO2017 dataset used in the main experiments contains only 5,000 images, it may lead to slight estimation bias in the FID computation, as FID becomes more reliable with larger sample sizes. To assess the potential misjudgment of model performance caused by limited image numbers, we further evaluated the metrics on a larger dataset, COCO2014, as shown in Table 6.

C.2 Effect of random seeds on model performance

Due to the autoregressive nature of our model, each token is sampled from a probability distribution, making the generated images sensitive to random seeds. Specifically, we observed that metrics such as FID, CLIP-Score, DPG, and HPS may vary with different random seeds. To further analyze this effect, we randomly selected 10 seeds from the range [0 , 1 e 6] , ran the generation model 10 times under these conditions, and computed the mean and standard deviation of the results. As shown in the table below, random seeds have little impact on the performance gain introduced by our method, further confirming the robustness and effectiveness of our approach. See Table 7.

C.3 Entropy & topp and temperature

In the main text, we analyze the relationship between our entropy-based sampling strategy and existing sampling parameters such as CFG and topK . By combining our method with these parameters, we observe improved robustness, reducing sensitivity to hyperparameter choices and yielding better FID and CLIP-Score. Here, we further examine other sampling parameters-top-p and temperature-which are rarely used in autoregressive models due to their tendency to distort the output distribution and severely degrade either FID or CLIP-Score. Comparative results between our method and the baseline are shown in Fig. 11.

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C.4 Additional comparison with topK and CFG

In Sec. 4.4 of the main paper, we discussed the differences between our method and existing sampling strategies (TopK and CFG) using LlamaGen. Here, we provide additional comparisons on other models to analyze the relationship between entropy-aware temperature and these conventional sampling approaches, results are presented in Fig. 12 and Fig. 13. Consistent with our observations in

Sec. 4.4, the proposed strategy mitigates performance fluctuations caused by hyperparameter choices (e.g., CFG and top-K), leading to a better balance between fidelity and text-image alignment.

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C.5 Additional evaluation of performance regarding temperature

In the main text, we analyzed how adjusting the sampling temperature of tokens in different entropy ranges affects the generation quality for LlamaGen. Here, we further extend the study to more models. See Fig. 14.

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D Additional discussion about entropy

D.1 Entropy & generative models

D.1.1 Visualization of entropy & images

Due to space limitations in the main text, we did not provide extended entropy visualizations and analysis. Here, we include additional entropy maps for LlamaGen and Lumina-mGPT. See Fig. 15 and Fig. 16.

D.1.2 Mask-prediction models

We provide additional entropy-based analysis of the mask model. Since the generation involves multiple timesteps, where a subset of tokens is accepted at each step based on previously generated content, we compute the entropy of accepted tokens at each timestep and aggregate them into a final entropy map. As shown in Fig. 17, applying the proposed entropy-based temperature leads to a more spatially balanced entropy distribution and enables richer image content while maintaining generation stability.

In addition, we further analyze the average entropy of tokens accepted at each timestep, as shown in Fig. 18. As discussed in the main text, due to the confidence-based token selection strategy, tokens accepted in earlier steps tend to have lower entropy, since they are more likely to receive high

U

confidence scores. In contrast, tokens accepted in later steps (>60) exhibit significantly higher entropy. Moreover, more tokens are accepted in these later stages, which increases the risk of violating the autoregressive assumption that spatially adjacent tokens should be sampled as independently as possible. This may lead to degraded image quality. Therefore, adopting a more conservative sampling strategy for these high-entropy tokens could help improve the overall generation quality.

D.1.3 Scale-wise models

For the scale-wise model, the generation process constructs a complete image by predicting logits maps at multiple scales. Each scale is conditioned on the residuals from the preceding scales, meaning that the sum of the feature maps generated at all scales is passed through the detokenizer to form the final output. In this generation paradigm, different scales exhibit distinct roles. Specifically, as described in [67], the earlier scales are responsible for generating the main structure of the image, while the later scales refine the result with fine details such as texture. We visualize the entropy maps of each scale during generation, as shown in Fig. 19. From scale 8 to scale 12, the model

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tends to focus more on the foreground, with significantly higher entropy observed in the regions corresponding to the primary subject. In contrast, at scale 13 and 14, there is no clear bias between foreground and background, indicating a more uniform attention across the image.

In addition, we compute the average entropy for each scale, as shown in Fig. 20. The later scales exhibit relatively higher entropy, while the earlier scales tend to have lower average entropy (however a decreasing trend is observed in the final two scales). This further indicates that different scales carry varying amounts of information.

D.2 Entropy & generated contents

In practice, the logits are not simply positively correlated with the complexity of image content. We observe that regions with clear, well-defined content do not always exhibit high entropy; instead, their

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entropy typically falls within a moderate range (e.g., between 2 and 8). The more deterministic the content, the lower the entropy tends to be. In contrast, regions with entropy lower than 2 or higher than 8 often correspond to simple backgrounds or overly complex, unfaithful details. Especially for regions with entropy above 8, the generated details are frequently meaningless. This also explains why adjusting the logits in these low- and high-entropy areas, as discussed in our motivation experiment, does not significantly harm text-image alignment.

D.3 Is entropy the best indicator for information?

From a theoretical perspective, the entropy of logits reflects the model's confidence in predicting the current token. When the model is sufficiently trained-or when its capacity is strong-it may produce low entropy even in semantically important regions. In practice, we observe cases where foreground objects (e.g., faces) yield lower entropy than complex backgrounds. This suggests that the model's confidence is not solely determined by information density, but also by the number of plausible token candidates in a region. For instance, highly structured areas like faces tend to have a unique correct token and thus low uncertainty, despite containing rich semantic information. In contrast, cluttered textures such as grass or foliage may allow for more varied token predictions, resulting in higher entropy.

Based on the above analysis, entropy may need to be combined with additional indicators to more accurately characterize the information distribution within an image. Specifically, more precise token-wise handling can be achieved by incorporating the similarity among top-ranked tokens in the logits distribution. For instance, if the entropy is low but the top tokens are not similar, the prediction can be deemed accurate; however, if the top tokens are highly similar under low entropy, the randomness at that position may need to be further increased. Conversely, under high-entropy conditions, a set of similar top tokens may indicate the existence of genuinely diverse possibilities. Moreover, analyzing the similarity of logits between adjacent tokens could help identify tokens that

require more precise predictions-for example, if a token's probability distribution significantly differs from that of the previous token, it may warrant stricter sampling, regardless of its entropy level. We leave these directions for future exploration.

Moreover, since dynamic temperature only adjusts the randomness of the probability distribution (i.e., the variance of the logits) but not the location of its peak, further combining it with CFG may help achieve better performance.

E Future works & limitations

E.1 Broader impacts

This work is the first to explore the decoding problem in autoregressive visual generation, highlighting the inherent differences between image and text generation. While our approach may not be fully complete and still leaves room for improvement, we hope it can inspire future research to further investigate this issue and develop decoding strategies tailored specifically for visual generation, ultimately advancing unified multimodal generation.

E.2 Future works

Currently, we propose a training-free sampling strategy for image generation by adaptively controlling sampling randomness based on the distribution of predicted logits. However, this approach is sensitive to hyperparameters, and due to significant differences across backbone architectures, optimal settings vary across models. Moreover, as a simple inference-time method built upon pretrained models, its performance gains may be limited for certain models.

In the future, this strategy could be integrated into the training framework for further performance improvement or acceleration. For example, it may be combined with early-exit mechanisms to allocate computation dynamically across tokens, or used to guide training by leveraging entropy to focus more on informative regions, thus accelerating convergence.

E.3 Limitations

The proposed method mainly mitigates issues caused by inconsistent token sampling strategies under varying information densities, but it does not enhance the intrinsic generation capability of autoregressive models. The performance gain is model-dependent. If the base model is trained with techniques that promote diverse token distributions, such as noise injection during training, or is well-trained on large-scale datasets, the improvement tends to be limited. Moreover, for weak base models, such as LlamaGen Stage 2, the method may offer little or no performance gain.

F Additional visual comparison

Due to space constraints in the main paper, we did not provide additional visualizations. Here, we include further results illustrating the entropy-aware sampling behavior for LlamaGen, Lumina-mGPT, Meissonic, and STAR, as well as acceleration visualizations for LlamaGen and Lumina-mGPT (see Fig. 21-Fig. 25).

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EH

ASAR

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose to measure entropy and use it to set temperature dynamically to improve speed and quality of autoregressive image generation model. They did some initial experiment with CLIP score and FID vs temperature for different entropies and observed that \n\n1. In high-entropy regions, lower randomness helps improve text-image consistency.\n2. In regions with extreme low entropy, increasing sampling randomness consistently improves visual quality, while having negligible impact on text alignment.\n\nBased on these observations, they propose to set temperature dynamically according to entropy to improve the sampling process. Further, the authors also demonstrate the effectiveness of the method with mask-prediction models and scale-wise models. Finally, they also use entropy to improve speculative decoding and therefore accelerate the inference speed.\nExperiments show promising results and validated the method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\n1. The idea is novel. \n2. The paper is well written and is easy to follow\n3. In addition to the main results, authors have done various ablation studies to validate the proposed method\n\n\nWeakness:\n\n1. Need to provide more details about the initial finding for Figure 3(c). Ideally use different model and dataset to make sure the conclusion is solid.\n2. \"Mask-prediction models\" section needs some improvement. I did not get why \"This design further encourages randomness in low-entropy regions while enhancing accuracy in high-entropy regions\"\n3. The 15% speed up doesn't seem to be a large improvement for acceleration.\n\nQUESTIONS:\n1. Could you explain more why \"regions with simple content (e.g., solid colors) typically exhibit lower entropy while more complex foreground (e.g., objects, structures, and textures) areas have higher entropy\" the entropy here sounds similar to frequency, is there any connection?\n2. For figure 3(c), could you add more details of how the experiment is performed? Which dataset is used and which model is used to generate the distribution?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

comments: SUMMARY:
The paper proposes a new approach to improve the inference of autoregressive (AR) image generation models by leveraging entropy-based sampling strategies. The authors demonstrate that image tokens exhibit low information density and non-uniform spatial distribution. To address this, they introduce an entropy-informed decoding strategy including (a) a entropy-based sampling temperature for better quality of generated images, and (b) an entropy-aware acceptance rules in speculative sampling for better acceleration. The prpopsed method is validated on COCO2017val benchmark and various frameworks.

STRENGTHS AND WEAKNESSES:
# Strength

- The proposed method demonstrates consistent performance and improved inference speed across multiple generation models, including standard AR, mask-based, and VAR models.

- The paper provides thorough quantitative and qualitative analyses that illustrate the entropy characteristics of visual generation models that use discrete tokens and demonstrate the effectiveness of the proposed method.

- This paper is well-written and easy to follow.

# Weakness

- The paper lacks a theoretical analysis of whether the proposed speculative sampling strategy (Section 3.4) preserves the original token distribution or quantifies the error bound of potential quality loss.

- This paper lacks the evaluation on the COCO2014val benchmark which contains 30k images to provide precise FID values.

- The paper does not investigate whether the proposed entropy-aware temperature could benefit NLP models.

QUESTIONS:
Please refer to weakness.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis research tackles the problem of inefficient and suboptimal sampling methods in autoregressive image generation models. The key insight is that image tokens exhibit lower and more unevenly distributed information density across different spatial areas compared to text tokens. To solve this issue, the authors introduce an entropy-based sampling strategy with two main components: first, a dynamic temperature adjustment system that varies the level of sampling randomness according to the spatial entropy patterns in token distributions; and second, an entropy-based acceptance criterion for speculative decoding that aims to speed up inference while preserving generation quality. The researchers test their approach on various autoregressive image generation frameworks, including next-token prediction, masked token generation, and scale-based models. Results demonstrate enhanced performance in both image quality metrics (such as FID, CLIP-Score, DPG, and HPS) and computational efficiency during inference.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- **Clear Motivation**: Empirical analysis (Figure 2a) demonstrates spatial non-uniformity in image token distributions, justifying entropy-aware methods\n- **Effective Method**: Dynamic temperature mechanism well-linked to spatial entropy distributions, showing model-agnostic improvements across multiple AR generation models\n- **Comprehensive Evaluation**: Thorough experiments with multiple metrics (FID, CLIP-Score, etc.) and complete ablation studies demonstrating method efficacy\n- **Practical Value**: Entropy-aware speculative decoding reduces inference steps and latency by ~15% without quality loss\n- **Clear Visualizations**: Effective illustrations of method improvements and comparisons with existing techniques\n\n## Weaknesses\n\n- **Limited Novelty**: Significant overlap with prior entropy-based decoding work in text (e.g., EDT), with insufficient differentiation of novel aspects\n- **Generalizability Concerns**: Experiments mainly focused on COCO2017 and few AR models; limited discussion of generalization to different datasets and non-natural image tasks\n- **Missing Failure Analysis**: Insufficient discussion of scenarios where the method might degrade performance, especially in high-entropy edge cases\n- **Weak Theoretical Foundation**: Lacks theoretical justification for entropy-to-temperature mapping, relying primarily on empirical settings\n- **Statistical Significance**: Missing confidence intervals and significance testing for reported improvements\n- **Implementation Details**: Some key implementation aspects under-explained, potentially hindering reproducibility\n\nQUESTIONS:\n- Clarification on Entropy Calculation: In Section 3.2, is the entropy always calculated per-token across the entire vocabulary regardless of mask/scale, or is it pooled or smoothed spatially in any way for efficiency or noise reduction? If so, could the authors elaborate on implementation trade-offs and robustness?\n\n- Extension Beyond AR Models: The method is said to be broadly applicable to “AR frameworks based on discrete token prediction,” but could the authors comment (with any preliminary evidence or arguments) on expected utility in hybrid AR-diffusion or continuous generative models, where tokenization or spatial entropy may be ill-defined or noisy?\n\n- Failure Case Analysis: Could the authors provide additional visual or quantitative analysis of cases where the entropy-aware method causes degeneration or perceptual artifacts, especially in ambiguous or extremely high-entropy settings?\n\n- Statistical Robustness: Are the improvements (especially in FID and DPG) significant over multiple runs or seeds? Please report confidence intervals or standard deviations if available. Would such variability affect conclusions?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors hypothesize that given the lower info density of image tokens and being spatially non uniform, entropy is a good indicator for balancing diversity and accuracy in the generations. Based on this premise, they propose a new decoding approach which, simply put, tries to increase diversity in easier regions of the image, while prioritising accuracy in the detailed regions. The authors present detailed evaluations and ablations to justify the various engineering choices they make in their approach\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThis is a well written paper that methodically identifies the distinction between text and image token distributions, and tries to address them through the lens of entropy. The method is clearly explained and intuitively makes sense. The paper also includes experiments on a variety of methods involving next token generation, masked generation and scale wise generation. The paper also makes a lot of engineering choices, which are well addressed in the ablations. The latency benefits as well as improved metrics through a purely new decoding strategy is a positive step for autoregressive visual generation.\n\nWeaknesses, Questions and Suggestions:\n\n1. Eqn 6b, $p_s = p_s / T_s$ is slightly confusing, i believe the LHS should have a \\tilde perhaps like Eqn 3, which is then carried forward appropriately?\n2. I appreciate the visualization of entropy maps after the proposed methods has been applied in the supplementary, can you also add histograms from similar regions (background and foreground) after your method -- as in Fig. 3a. Do you see a more uniformly distributed histogram now given that for low density regions, diversity has been prioritised?\n3. I understand that easier regions have better diversity using this method, but in the visuals, it also seems that the dense information regions (foreground) also is better in some sense, increasing the overall image quality, even though the accuracy factor has more emphasis here? Can the authors shed some light onto what specifically happens for the foreground part, I would have expected those areas to be almost similar as the base method. \n4. For completeness, can the authors compare their improvements with the current SOTA visual generation methods as well, just as a reference to understand where their improvements in AR generation stands as compared to diffusion/flow methods that currently lead the field.\n\nQUESTIONS:\nPlease see the strengths and weaknesses section. I would be happy to raise my score if my concerns are properly addressed.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}