Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
Authors: Bryan Sangwoo Kim
Jeongsol Kim
Jong Chul Ye
Venue: NeurIPS 2025 spotlight ()
Keywords: generative models
super-resolution
Primary area: applications

Abstract: Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collapse when asked to magnify far beyond that regime. We address this scalability bottleneck with Chain-of-Zoom (CoZ), a model-agnostic framework that factorizes SISR into an autoregressive chain of intermediate scale-states with multi-scale-aware prompts. CoZ repeatedly re-uses a backbone SR model, decomposing the conditional probability into tractable sub-problems to achieve extreme resolutions without additional training. Because visual cues diminish at high magnifications, we augment each zoom step with multi-scale-aware text prompts generated by a vision-language model (VLM). The prompt extractor itself is fine-tuned using  Generalized Reward Policy Optimization (GRPO) with a critic VLM, aligning text guidance towards human preference. Experiments show that a standard $4\times$ diffusion SR model wrapped in CoZ attains beyond $256\times$ enlargement with high perceptual quality and fidelity.

Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
Bryan Sangwoo Kim
Jeongsol Kim KAIST AI
Jong Chul Ye
(bryanswkim, jeongsol, jong.ye) yet.yyyekkaais.ccc..oo
Figure 1: Extreme super-resolution of photorealistic images by CoZ with up to 64x magnification (top) and 256x magnification (bottom). Fine details such as textures on wall, wrinkles on flag, and leaf veins are clearly seen.
Abstract
Modern single-image super-resolution (SISR) models deliver photo-realistic results at the scale factors on which they are trained, but collapse when asked to magnify far beyond that regime. We address this scalability bottleneck with Chain-of -oom (CoZ), a model-agnostic framework that factorizes SISR into an autoregressive chain intermediate scale-states with multi-scale-awar prompts. CoZ repeat- edly re-uses a backbone SR model, decomposing the conditional probability into tractable sub-problems achieve extreme resolutions without additional training. Because visual cues diminish at high magnifications, we augment each zoom step with multi-scale-awann text prompts generated by a vision-language model (VLM). The prompt extractor itself is fine-tuned using Generalized Reward Policy Optimization (GRPO) with a critic VLM, aligning text guidance towards human preference. Experiments show that standard 4x diffusion SR model wrapped in CoZ attains beyond 256x enlargement with high perceptual quality and fidelity. Project Page: https: bttp//wwaaaaikiiiimmm github i0/ chain -f00/
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
The field of generative modeling has witnessed remarkable progress, enabling the synthesis of highly realistic data across various modalities, including images, text, and audio. A key application benefiting from these advancements is single-image super-resolution (SISR), which aims to reconstruct high- resolution (HR) details from low-resolution (LR) input image. Super-resolution is problem of core interest for effectively bridging the gap between low-cost imaging sensors and high-fidelity visual information; its usages range from enhancing consumer photographs and legacy media to improving critical details in medical imaging, satellite surveillance, and scientific visualization [2|29] 31
The standard approach to SISR is based on the posterior probability distribution:
p vT.))
where the goal is to sample plausible HR image 2H for given input LR image æy. However, the mapping from æ to æ is highly complex and fundamentally ill-posed: a single LR image can correspond to a multitude of plausible HR images. This makes directly modeling the distribution extremely challenging for large magnification factors, and early attempts relying on interpolation or regression often produced blurry results [I]] 14 20 [1], Recent emergence of powerful generative models (e.g., duffusion-basee models) has led to significant advancement in this task, providing strong generative priors over natural images that enable the synthesis of realistic textures and details consistent with the low-resolution input.
Specifically, existing methods leveraging such generative priors largely fall into two categories. One line of work frames SR as an inverse problem, utilizing pre-trained generative model a prior during inference time to find realistic HR image consistent with the LR input [6- 21 [2]. While such inverse problem-solving methods benefit from being training-frres free, they typically require lengthy iterative optimization or sampling processes at inference time to enforce data consistency (i.e., ensuring the downsampled HR prediction matches the original LR input), making them compu- tationally expensive. Another line of work aims to incorporate this data consistency directly into the model's ' training objective, thereby enabling much faster inference [32 43
55 56). Modern state-of-the-art models within this category are capable of producing high-quality super-resolved images, even in a single inference step [48] [6).
However, these fast, trained super-resolution models suffer from a significant limitation: they are inherently upper-bounded by their training configuration and tend to collapse when presented with inputs requiring magnification beyond what they were trained on 23 25 58). This failure occurs because the model's internal representations and learned restoration functions are tightly coupled to the specific scale and degradation seen during training [35. Applying it outside this domain violates its learned assumptions, leading to severe artifacts, blurry outputs, or a complete failure to generate meaningful high-frequency details 12 14 20), This lack of robustness severely restricts the practical applicability of these otherwise powerful models, demanding new models to be trained when the desired magnification factor exceeds what can be currently provided, which is highly inefficient.
In this work, we therefore propose solve a fundamental question: How can we effectively utilize super-resolution models to explore much higher resolutions than they were originally trained Solving this question critical in that it addresses the practical need for flexible and arbitrary- scale super-resolution, allowing users to magnify images to desired levels without being constrained by model training specifics. Furthermore, training models for extremely high magnification factors (e.g.. 16x, 32x) directly is often computationally prohibitive due to memory and time constraints [46]. Enabling the extension of existing, well trained models (e.g., 4x SR models) to higher factors offers a significantly more resource-efficient pathway to achieving extreme resolutions.
To address these fundamental challenges, we present Chain-of-Zoom (CoZ). novel framework for achieving resolutin image generation beyond the training configurations of conventional super-resolution models. Specifically, we introduce intermediate scale-state modeling to bridge the gap between a low-resolution (LR) input and high-resolution (HR) target image. These intermediate
stale-states enable the decomposition of the conditional distribution in Eq. (I) into a series of tractable components, forming the basis of a scale-level autoregressive (AR) framework. Within this framework, models can progressively generate high-quality images at resolutions previously considered unattainable. In particular, building on the scale-level AR-2 model, we further propose multi-scale-awar aware prompt extraction technique. This approach leverages LingoneLanguage Models (VLMs) to extract descriptive text prompts by attending to multiple scale-states throughout the
2

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HR
SR Model
UR
Imput
HR (4x)
HR (16x)
Model
SR Model
Red pendo.
Fun,
VEM
close-up. fore,
VLM
Aoft,
Figure 2: (a) Conventional SR. When an SR backbone trained for a fixed up-scale factor (e.g., 4x) is pushed to much larger magnifications beyond its training regime, blur and artifacts are produced. (b) Chain-of-Zoom (ours). Starting from an LR input, a pretrained VLM generates descriptive prompt, which-togethee with the image-is fed to the same SR backbone to yield the next HR scale-state. This prompt-and-upscale cycle repeated, allowing a single off-the-shelf model to climb to extreme resolutions (16x-256x) while preserving sharp detail and semantic fidelity.
zooming process, enabling semantically aligned and coherent super-resolution. This is from the observation that extreme resolutions, conditioning provided by the original signal ær, becomes insufficient, thus leading to unreasonable hallucinations by the SR model cases.
Furthermore, to obtain text prompts of even richer detail that aligns with human preference, fine-tune the prompt-extraction VLM under a novel RLHF pipeline leveraging GRPO 34]. A core part of this pipeline is the utilization of a critic VLM score the outputs of the prompt extraction VLM, thus guiding it to produce prompts more aligned to human preference. Incorporated into the CoZ framework, final VLM model successfully guides the super-resolution process towards reasonable high-quality results.
In summary, contributions are as follows:
We present Chain-of-Zoom, scale-level autoregressive framework that decomposes super- resolution into a sequence of intermediate scale-states and multi-scale-awe aware prompts, enabling any existing SR model reach much higher magnifications without retraining.
We propose novel RL pipeline for tuning prompt-extraction VLMs with GRPO. This pipeline incorporates appropriate reward functions and critic reward model to endue multi-scale aware reasoning capabilities the prompt-extraction VLM.
2
Related Work
Multi-Scale Image Generation and Super-Resolution. Unconditional multi-scale generators syn- thesize ever-larger images by passing coarse outputs through successive refinement stages. Cascaded Diffusion Models [17 pioneer this coarse-to-fine pipeline, while AnyresGAN [3, Scalespace- GAN 47, Generative Powers of Ten 45). ZoomL.D [53), and Make-a-Cheap-Seen Sccheap-Scaling [16] share weights across latent zoom levels to reach megapixel resolutions. Because they are generation based, these methods do not enforce consistency with given low-resolution input. For true SR, PULSE 22 searches a GAN latent space, and Zoomed In, Diffused Out (28) alternates diffusion denoising with explicit up-sampling, but both do not explore extreme resolutions in this work.
Autoregressive Factorizations. Classic autoregressive models such as PixelCNN, PixeIRNN [39 and VAR (88 predict spatial tokens sequentially within fixed resolution. Pixel Recursive SR [0 extends this to super-resolution by autoregressing over pixels after each enlargement- for small factors but computationally prohibitive at extreme scales. The proposed CoZ instead autoregresses over scale-states: we factorize p( 2x))) into a tractable sequence of intermediate zoom distributions, enabling arbitrarily high magnifications without retraining at every factor.

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Diffusion-Based Super-Resolution. Diffusion models have become the de-facto approach for high-fidelity SISR. SR3 [32] first denoised noisy HR guesses into realistic outputs with diffusion models. St 43 reuses a diffusion prior for faster convergence, and prompt-aware variants (e.g.,
SeeSR [49]. SUPIR (55] add textual conditioning to bolster semantic faithfulness. OSEDiff [48] distills the multi-step chain into one-step denoising. Because of its accuracy and efficiency, we adopt OSEDiff as the backbone SR module in our CoZ demonstrations. However, CoZ is model-agnostic: the same scaling strategy can wrap any existing text-guided diffusion (or non-diffusion) SR network.
RL for Vision-Language Guidance. Reinforcement learning with human feedback (RLHF) is now widely used to align VLM behaviour with user preference, Early vision-grounded efforts such as LLaVA-RLHE [36] and LLaVACritic [50] employ reward models or critic networks refine image-conditioned dialogue. Generalized Reward Policy Optimization (GRPO) was introduced by Shao et 34 policy-space alternative to PPO
33 GRPO has since been adopted in vision tasks outside SR: Seg-Zero [26) uses GRPO to train VLMs for open-set semantic segmentation, while MetaSpatial [30] applies to 3-D spatial reasoning virtual environments. Building on these precedents, we are the first to bring GRPO to prompt-extraction in super-resolution. Our pipeline fine-tunes prompt-extraction VLM with composite reward objective unexplored in prior SR work.
3 Chain-of-Zoom
3.1 Intermediate Scale-State Modeling
In the CoZ framework, we propose to bridge the gap between a target HR image zH E Rd and an input LR image æy E Ed by introducing intermediate scale-states æ, E R Suppose that an image generative process is modeled and consecutive states have dimension ratio s (i.c. d, = sd;-1) larger than Under the Markov assumption, the joint distribution could be modeled as p(z:, I1 .... I. p( [T==1 p(æ; (x,1)) However, the model follows a Markov chain structure, relying solely on the transition probability p(xx//x;-1) leads to loss of righ-frequency details increases (see Fig. 3). Inspired by recent work in inverse problems [8 [1] that demonstrate the effectiveness of text embeddings in reducing the solution space and improving super-resolution between consecutive scales, we therefore introduce latent variables e; through text embeddings. The text prompt extraction supplements information of the overall zoom process.
sequence (æo, I1, .... Zn,) where Vo æL. 2n zH.
Importantly, to reduce hallucinations caused by incorrect text guidance across scale, we find that multi-scale aware text extraction is necessary by feeding -;-1 and the coarser state T,-2 in prompt generation, leading to the conditional probability for the prompt:
(c (4
(2)
Therefore, instead of using the Markov assumption, we propose AR-2 modeling of the image generative process with multi-scale-aware prompts as latent variables:
plao, 3t. Ir...., ..,) ) = p( x1) II
p( 1, x;-2);
1=2
p 22-2) / //1ppp///// p(00//1:-1, 2, Ci ci)pp(ca(: 2: Bi 2)cc
Then, the joint distribution of the sequence (Io, C1, I1, Ca, F.n) is expressed as follows: Proposition 1. Given a sequence of scale-states x; that follows AR-2 structure and latent variables C₁ that satisfy Eq. (2). the joint distribution is expressed
p( C1, Wi,.... Cn, xn) = p(( 20 C1. x1) II (.gll
(5)
Now, our objective function is maximizing the likelihood of the entire joint distribution of and c;- Taking the logarithm of Eq. (5). we get the objective function to be maximized:
= log p(Do, C1. x1) log pl x. x. 2i c:)
log p x. 1.
(6)
Eun
Ess
where Linit represents the initial super-resolution step.

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Input
16x
258X
Input
258x
(a) Null Frompt
(a) Nuil Prompt
(b) DAPE Prompt
(b) DAPE Prompt
(c) VLM VLM Prompt
VLM Prompe
Figure 3: Significance of proposed multi-scale-awarr prompts: (a) Null prompt: coarse structure is retained, but high-frequency details are smoothed out. (b) DAPE prompt: inserting text from degradation-awar aware prompt extractor (DAPE) helps, yet the images lack intricate detail large magnifications. (c) VLM- TLM--enerated prompts (ours): multi-scale prompts extracted by VLM steer the SR backbone synthesize realistic textures and crisp details.
3.2 Training Objective
The additive structure of the components in Eq. (6) allows for the independent optimization of each term. We achieve this via next æ; prediction and next e; prediction, using parameterized models and o respectively.
Next v, prediction. The training objective LsR represents the likelihood of given previ- ous scale-states I;-1, 2;-2 and description c, for æ4 Under the assumption that the distribu- tion (a,,,|a;-1,, 2; c;) (x;; fo 2;-2, C;), I²I) is Gaussian, where the parameterized model fo predicts the conditional mean of the distribution, the likelihood x; is equivalent to
logp(z;/2 2;-2, = 20 Ø fola,-1, +
(7)
where C= - log (2to²). To reduce the computational complexity of training fo. our key idea is that its dependency n; 2 is only through the multi-scale-aware prompt, i.e. C; c2(00-1, 22;-)) leading to So (2;-1, -:22,,22 Cs) ( (x 22) Maximizing the simplified likelihood thus reduces to minimizing the mean-squared error (MSE) between the predicted HR patch from æ;-1 and the ground seeth-precisely the loss most SR backbones are already trained with. In this work, we perform experiments with a backbone SR model trained via settings in Sec. yet our framework is model-agnostic.
Next c; prediction. Recall that the dependency to the x;-2 in AR-2 model is through the multi- scale aware prompt extraction, which supplements information of the overall zoom process and reduces hallucinations caused by incorrect text guidance. For single zoom step the prompt C; = (G,11 ... Cr,T,) is a token sequence conditioned on the current and previous image, i.e. -; 24 Modern VLMS model this distribution autoregressively:
Tr
(c 2:-1. 2:-2 II Po(Ci,t I;- T. Ci,et)
(8)
where 6, = (c.,1.... 1. Maximizing the log- ligeliill log 2;-1, 2;-2) therefore
amounts to minimizing the negative log-likeliban likelihood (cross-entropy) for each token:
T.
V = log 1; 3i xi-2)
log pa (o 8;-1, D4 G..et)
(9)
5

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Text Decoder
Reward Functions
Phrase Exclusion Reward
0.7
Vision Language Model
< VLMM Output
"Butterfly wing, Monorch butterffly Wing pattern. Black orange.
Repelition Penally
-0.2
Rewards
Crilic Reward
Encoder
"The second image is zoom-in of the first image. Give a
Critic VLM
Figure 4: GRPO Training Framework. At every zoom step, multi-scall selti-scaaelee image crops are fed the base VLM, which generates candidate prompts after perceiving input images. A critic VLM scores the prompt for semantic quality, while phrase-exclusion and repetition penalties enforce conciseness and relevance. The weighted sum of these rewards forms the GRPO signal that iteratively fine-tunes the base VLM, steering towards prompts that best guide extreme-scale super-resolution.
Eq. (9) is exactly the standard next-token cross-entropy loss used to pre-train modern VLMs; hence our framework can employ any off-the-shelf VLM whose weights already maximize this objective.
Inference. Given pre-trained parameterized models A and the sequence (Eo, C1, 21, .... Cu. 2x,) can be generated recursively. Starting from the low-resolution image æ1 = Zb. a description for the next scale, C1 po(c, æo). is first sampled. Then, the next scale state is generated by sampling 2 ~ (2)) Øg. c. For subsequent steps, the description at scale is sampled as c; ~ po(c; 1; 22, followed by sampling the image at that scale æ; ~ po 1, 2;, C:). This sequential sampling process generates specific, plausible high-resolution outputs a. without needing to model the full marginal distribution p(aro, -... 2r,)) explicitly. When using SR backbone models that require input and output dimensions to be identical (e.g.. mubleediifusioon-asee
SR models [43 48 49 55), fixed-size window cropped from the HR image and resized to the required dimension. Thus, super-resolution operates in local regions, and achieving outputs of entire images would require multiple runs of CoZ.
3.3 Training Multi-Scale-Awarr Prompt Extraction using RL
At extreme magnification factors, the visual evidence in the input image becomes extremely sparse, causing the SR backbone model rely more heavily text prompts. To curb the ensuing drift towards implausible high-frequency hallucinations, we fine-tune the prompt-extraction VLM so that its textual guidance aligns with human aesthetic and semantic preferences. Our fine-tuning pipeline (Fig. ( adopts Generalized Reward Policy Optimization (GRPO). For each zoom step i, the VLM receives multi-scale image crops (x;- 1; and produces candidate prompt C.. The prompt is scored by a set of task-specific reward functions, and the weighted sum R(c;) drives the GRPO update align the VLM prompts with human preference. The overall reward R(e;) a weighted sum of three components, each targeting distinct failure mode observed during preliminary experiments:
R(c:) Wentie Rentic + Wphrase Rehrass + Whep Reep
(10)
Critic Preference Reward ( (Reritie). A stronger vasion-language critic VL.M judges the candidate prompt in the context of the input multi-scale image crops and assigns a raw score in [0, 100). We linearly rescale this score to [0, 1] and treat it as proxy for human preference, thereby imbuing the prompt-extraction VLM with the critic VLM's higher-level semantic priors.
Phrase-Exclusion Reward (Rphrase). Multi-image conditioning occasionally leads the prompt- extraction VLM to emit viewpoint markers such as "first image" or "second image," which meaningless to the downstream SR model. We therefore issue a reward if none of a predefined blacklist of such phrases appear, and 0 otherwise.
Repetition Penalty (Rrep). Following Yeo al. (54, we compute the fraction of repeated n-grams in the prompt and give negative reward (down to 1) for higher repetition ratio.

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Input
(a) NN Interpolation
(b) Direct
(c) CoZ (Null)
(d) CoZ (DAPE)
(e) (VLM)
B4x Magnification
256x Megnification
Figure 5: Qualitative Results. For each input image, super-resolution is performed on different magnifications with various methods: (a) Nearest neighbor interpolation; (b) One-step direct SR with the backbone SR model; (c-e) Variants of CoZ with different text prompts. The CoZ framework shows significantly better performance large magnifications. Furthermore, using CoZ with VLM prompts assists the SR model in generating realistic details without hallucinations.
Experiments
4.1 Experimental Settings
We adopt the setup of prior work 49
48 and train OSEDiff [88 as the backbone SR model with
the LSDIR [24] dataset and 10K images from FFHQ 18 We use Stable Diffusion 3.0 [1] as the backhone diffusion model and adopt a coarse-to-fire training strategy: first training on random degradation, and then training specifically for 4x magnifications. Text guidance is provided by Degradation-Awee Aware Prompt Extractor (DAPE) 49] as the naive prompt extractor, while Qwen2.5-VL. 3B-Instruct 37 is used the prompt-extraction VLM. RLHF training with GRPO is performed with Intern VL2.5-8B (5) as the critic VLM. The same dataset used for training the backbone SR model is also used for GRPO training, and weights are given as: Weritic
1.0, Wp
0.5. Wrep 0.5.
Evaluation performed on the training datasets of DIV2K I and DIVSK [5 consisting of 800 images and 1500 images, respectively. Each image is resized and center-cropped resolution of 512 x 512 to be input to the SR model. For four recursions, the HR image of the previous zoom is center-cropped and resized by scale of back the resolution of 512 x 512.
4.2 Comparison Results
We perform comparison across four recursions for various methods. Specifically, we compare between nearest neighbor interpolation, direct magnification via one-step SR, and three versions of the proposed CoZ leveraging different prompts (i.e., Null, DAPE, VLM).
Qualitative Comparison. Qualitative results in Fig. show that nearest neighbor interpolation and one-step direct SR fall off at higher scales, while CoZ. variants produce images better quality. Thus, incorporating VLM prompts helps overcome the sparsity of the original input signal.
7

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Table 1: Quantitative comparison on no-reference metrics. Bold: best, Underline: second-best.
DIV2K
DIVSK
Scale
Method
NIQEL
MUSIQI
MANIQAT
CLIPIQAT
NIQEL
MUSIQT
MANIQAT
CLIPIQAT
4x
Interpolation
12.1252
39,96
0.3396
0.2630
13,1984
40.26
0,3472
0.2672
Direct SR
4.7320
67.00
0.6344
0.7005
4,8631
66.29
0.6359
0.6946
CoZ (Null)
4,7706
66.99
0.6309
0.6977
4,9011
66.23
0.6325
0.6897
CoZ. (DAPE)
4.7312
67.01
0.6344
4.8607
0.6946
CoZ (VLM)
4.6572
67.10
0.6360
0.7017
4,8099
66.37
0.6370
0.6953
16x
NN Interpolation
22.1215
24.01
0.3378
0.2346
22.2744
24.94
0.3465
0.2585
Direct SR
7.2183
51.25
0.5406
7.5855
50.17
0.6035
CoZ (Null)
6.5016
59.19
0.5859
0.6686
6. 7,7898
58.04
0,5881
0.6618
CoZ (DAPE)
6,5456
58.83
0.5946
0. 0.6609
6.8607
57.79
0,5964
0.6628
CoZ (VLM)
6.3957
58.81
0,5970
0,6574
6.6500
57,99
0.6006
0.6615
64 x
NN Interpolation
27.4051
37.69
0.3803
0,3690
27.7533
37.13
0,3861
0.3837
Direct SR
16.5915
22.54
0.3995
0.4309
16.5874
22.97
0.4066
0.4451
CoZ (Null)
8,3500
51.82
0.5627
0.6305
8 8.5694
50.96
0.5638
0.6240
CoZ (DAPE)
8.6598
$1.77
0.5726
0.6262
8.7669
50,40
0,5714
0.6274
CoZ (VLM)
8,2335
52.13
0.5788
0.6282
256x
Interpolation
34,8461
27.01
0.4179
0,5259
37.2612
26.98
0,4184
0.5299
Direct SR
16.1749
28. 89
0.4470
0,5196
15.8667
28, 90
0,4464
0.5256
CoZ (Null)
10.0456
46.28
0,5510
0.5857
10.0630
46.56
0,5479
0.5899
Co7. (DAPE)
10.4569
46.22
0.5564
0.5889
10.2788
45.81
0.5535
0.5984
CoZ (VLM)
9.8260
47.83
0,5692
0.5986
9.6405
47.25
0.5646
0.6041
Quantitative Comparison. Quantitative results are given in Tab. Due to the non-availability of ground-truth images for 256x magnifications, we follow 27 and evaluate performance on no-reference perceptual metrics. Specifically, we use metrics NIQE (57] MUSIQ [19], MANIQA- pipal [52]. CLIPIQA [22 for thorough evaluation. At low scales (i.e., Scale 4x), difference between methods is minimal, but at high scales (i.e., Scales 64x, 256x) the proposed framework shows consistently better performance. Furthermore, prompts by DAPE show comparable performance at low scales but fall off at higher scales, while VLM-generated prompts exhibit significantly better performance, supporting our claim that prompt-extraction by VLMs make up for the deficient visual conditioning provided by the initial image.
Quantitative Comparison with Baseline Methods. In Tab. 2 we further provide quantitative
comparison with baseline methods: arbitrary-scale SR methods (LIIF 4 and direct super-resolution of diffusion-based SR methods (SeeSR [49]. S3Diff (56) ). All three baselines show greatly degraded performance at high magnifications. For the case of S3Diff, we additionally show quantitative results for applying CoZ to the pretrained, frrely accessible S3Diff, leveraging our multi-scale aware GRPO fine-tuned VLM. All results clearly confirm the significant performance improvement by CoZ. Additional results for performing CoZ with OSEDiff leveraging the Stable Diffusion v2.1 backbone is provided in Appendix
4.3 GRPO for VLM
GRPO Training, Reward graphs for training the prompt-extraction VLM with different critic VLMs are shown in Fig. 6 and Fig. When using Intern VL2.5-8B (5) as the critic VLM, phrase exclusion reward and repetition penalty converge to 1.00 and 0.00 (respectively) in the early stages of training, while the critic reward increases gradually throughout the training process. Similar trends are observed when using --L-2..5- our method. Additional guantitative results for this case is provided in Appendix
(37 as the critic VLM, proving the robustness of
Preference Alignment. Using an off-the-sheef VLM for prompt-extraction can cause unwanted hallucinations to occur in the zoom process. An example case shown in Fig. X (Top), where the off-the-shelf VLM generates improper prompts due to insufficient knowledge of the initial image at high magnifications. By inducing the VLM to generate multi-scale-aweer prompts by conditioning on (x;-1. x;-2), we can produce more suitable prompts Fig. 8 (Middle). Finally, using the VLM fine-tuned with GRPO we can produce high-quality samples while reducing unwanted hallucinations as in Fig. 8 (Bottom). We further prove that the VLM after undergoing GRPO training is better aligned with human preference through user study. For this, we follow prior work 27), and perform a MOS (mean-opinion-scorg) test various samples. Results and details are included in Appendix D
8

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Repetition Panalty
0.925
1.00
3.00
1.40
-001
1.35
0.850
-2.02
0.823
-0.03
1.30
0.000
0.775
0.96
-3.04
1.25
3.95
1.20
2500
5000
7500
10000
2500
5000
7500
10000
2500
5000
7300
10000
2500
5000
7500
10000
$0.00
Stop
Step
Figure 6: Reward graphs of using Intern InternVL2.5-888 VL2.5-8B as the critic VLM, evaluated on validation set. Values for Critic Reward, Phrase Exclusion Reward, Repetition Penalty, and Total Reward increase throughout the training process.
Reward
Exclusion
Repatition Penalty
Reward
0.90
1.00
-2.02
1.40
0.99
0.04
0.80
0.95
00.05
1.25
0.75
0.57
-0.05
1.20
0.95
0.10
0.95
-0.32
0.60
1.05
2500
7500
2500
7500
10000
2.50
10000
2500
Figure 7: Reward graphs of using Qwen2.5-vL--7B-Insrrus as the critic VLM, evaluated on validation Values for Critic Reward, Phrase Exclusion Reward, Repetition Penalty, and Total Reward increase throughout the training process.
Po 21-1)
'feathers, glitter,.."
"night sky, stors...
"wooden planks,
texture, dork brown....
Polcc 8:-1, 2-22)
*dog, stick, water, ".. spider's web with water
"spider web
"fecther texture
GRPO Training
"Fur""
"fur"
"hair"
"hair"
Figure 8: RLHF training with GRPO assists the prompt-extraction VLM creating meaningful prompts for accurate guidance. (Top) Base VLM: generating prompts only from the LR input causes unwanted hallucinations as shown by the incorrect prompts; (Middle) Multi-scale image prompts helpful at low scales (e.g. accurate prompt of "dog. stick, water, ...") but fail at high scales; (Bottom) VLM aligned with human preference guides samples with improved text guidance.

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Table 2: Quantitative comparison with baseline methods. Best, Second-Best Beand.
DIV2K
DIVSK
Scale
Method
NIQEL
MUSIQU
MANIQAT
CLIPIQAT
NIQE,
MUSIQ
MANIQAT
CLIPIQAT
4x
LIFF
6,6210
57.47
0,5456
0,4587
6,8050
55.52
0,5386
0.4545
SeeSR
5.5208
57.56
0.5387
0.5535
5.5940
$5.50
0.5346
0.5385
S3Diff
4,9803
65.82
0.6361
0.6596
5,0305
64.08
0.6328
0.648
S3Dif CoZ
4.8637
67.18
0.6459
0.6835
4,9414
65.31
0.6405
0.6680
16x
LIFF
11.4815
25.94
0,2860
0.3024
11.8734
26.73
0.2896
0.3178
SceSR
9.6798
41.68
0,4310
0,4669
9,8865
40.02
0.4210
0.4601
S3Diff
6.7383
51.14
0,5305
0,5886
7.2399
50.32
0,5370
0.5841
S3DHT + CoZ
6.7310
56.82
0,5752
0.6168
6.8698
$6.46
0,5836
0,6218
64%
LIFF
16.9215
20.02
0,3451
0.4131
17.4912
20.43
0,3522
0.4250
SceSR
16.9095
21.83
0.4106
0.4193
16.9275
22.68
0.4167
0.4309
S3DiIT
16.1421
21.54
0,3893
0.4917
16.5506
22.00
0,3968
0,5089
S3DHT + Coz.
8.7770
48.90
0.5490
0,5801
8,9794
48.07
0,5560
0,5909
256x
LIF
23.8949
26.05
0,4108
0.5380
24.3300
26.01
0.4116
0.5423
SceSR
20.9635
25.81
0.4438
0.5193
19.9628
25.94
0.4429
0.5203
S3DifT
16,7809
25.92
0,4324
0,5359
16,9952
25.91
0,4312
0,5415
S3DHT + Coz.
10.7668
43.59
0.5438
0,5570
10.5001
43.31
0.5417
0,5673
Table 3: Runtime analysis for different methods (seconds).
Scale
Phasc
Direct SR (DAPE)
CoZ (Null)
CoZ (DAPE)
CoZ (VLM)
4x
SR
0.1467
0.1443
0.1460
0.1445
PE
0.0136
0,0000
0.0130
0.3777
16x
SR
0.1462
0.2886
0.2912
0.2896
0.0130
0.0000
0.0254
0.7068
64x
SR
0.1462
0.4329
0.4363
0.4349
0.0131
0.0000
0.0382
1.0301
256x
0.1462
0.5774
0.5816
0,5802
PE
0.0132
0,0000
0.0509
1.3505
4.4 Runtime Analysis
Runtime analysis for direct super-resolution and CoZ. variants across varying scales (4x to 256x) given in Tab. 3 The average inference time required (in seconds) to apply CoZ single image is evaluated on 500 images the DIV2K dataset. We divide inference into two phases: super-resolution (SR) and prompt extraction (PE); computational time required for each phase is analyzed accordingly.
5 Conclusion
This paper tackles the long-standing scalability gap in single-image super-resolution: state-of-the-art models excel at their trained scale factors yet fail when asked to enlarge images far beyond that range. Specifically, we introduced Chain-of-Zoom (CoZ), a scale-level autoregressive framework that transforms any existing SR backbone into an extreme- magniiiiaatiiiiioonn engine by decomposing the LR to HR mapping into sequence of intermediate scale-states and multi-scale-aware prompts. CoZ is model-agnostic,, requires no retraining the base network, and thus offers a -oft-effective path up to extreme resolutions. In particular, to maintain semantic coherence as visual evidence thins out, we leverage a multi-scale-aware prompt extractor driven by a VLM fine-tuned through a GRPO- based RLHF pipeline. Overall, CoZ yields sharp, realistic results at extreme scales while keeping inference efficient. By decoupling super-resolution performance from fixed training magnifications and demonstrating the value of aligned textual guidance, work opens new avenues for resource- frugal image enhancement and lays a foundation for future exploration of learned zoom policies, domain--pecific reward functions, and adaptive backbone selection.
Limitation and Potential Negative Impacts. While CoZ enables extreme super-resolution with high visual fidelity, it requires repeated application for extreme magnification, which may cause error accumulation over iterations. Moreover, high-fidelity generation from low-resolution inputs may raise concern regarding misinformation unauthorized reconstruction of sensitive visual data.
10

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Acknowledgments and Disclosure of Funding
This work was supported by the National Research Foundation of Korea under Grant RS-2024- 00336454, and by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. RS-2024-00457882, Al Research Hub Project). This work was also supported by the IITP (Institute of Information & Communications Technology Planning & Evaluation)-ITRR (Information Technology Research Center) grant funded by the Korea government (Ministry of Science and ICT) 2---2202----22-00-
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14

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A Proofs
Proposition 1. Given sequence of rcale-states æ; that follows aAR-2 structure and latent variables C₁ that satisfy Eq. (2). the joint distribution is expressed as
pl c1, 01, ... Ca. Rn) = p( C1. 21
Il
(5)
Proof. By substituting Eq. (4
to Eq. (3) and expanding the base term p( 21). we obtain:
V pl CI, c1,
/ pa:;
II
i=2
= =f / plao, cn x1)
II 2. dei den
p(zo, C1, L1, Cn. de)de1 ... den.
P(Fo: ..., Fn.)
This confirms the joint distribution in Eq. 5 is consistent with the marginal AR-2 process.
B Experimental Details
B.1 Model Checkpoints
We use the pretrained VLM models Owwen2.5-VL-33B-Insse and Intern available
htttp::/hhugiingface httpeaaccc../vee//eeee2222 5-VL- Instruct and https: //hggiingfae
co/
8B88 respectively. We also use the pretrained Stable Diffusion 3.0 model available at hatps://uuggiigffae co/ htt/sstaatttty/ssaalleee /tt.stabllafffiiinnn--- -3-medium diffusion-S Evaluation is performed using the script for testing IQA (Image Quality Assessment) in https
B.2
User Prompts
The user prompt used for the base VLM is as follows:
The second image is zoom-in of the first image. Based on this knowledge, what is in the second image? Give me a of words.
The user prompt used for the critic VLM is as follows:
First Image: <image>
Second Image: <image>
The second image is zoom-in of the first image. Please rate the quality of the following description how well it describes the second image. Output only single score between 0 and 100.
Description: <Output of Base VLM> Rating (0-100):
B.3 Other Settings
The backbone SR model is trained based on the training scheme of OSEDiff 48), with Stable Diffusion 3.0 as the backbone diffusion model. We train using four NVIDIA GeForce RTX 3090 GPUs with the LSDIR [4 dataset and 10K images from FFHQ [8] Coarse-to-fine training is used: random degradation (same setting as OSEDiff) for 25K iterations, then specific upscaling 20K iterations. Other settings (e.g., batch size, learning rate, etc.) follow the default settings of OSEDiff.
15

---

The VLM model is GRPO fine-tuned using four NVIDIA GeForce RTX 3090 GPUs with the LSDIR dataset, with train/validation split ratio of 0.01 (i.e., 849 images for validation). Specifically, the -V..2..-
model is LoRA fine-tuned (Rank: 8, Alpha: 32, Dropout: 0.05), with two generations per prompt for 10K global steps. The SWIFT [59] infrastructure was used for this process. Reward graphs during training for the validation set are given in Fig. c of the main paper.
Evaluation is performed with the code provided 488, modified for no-reference metric evaluation. For occasional failure cases, worst values are given for each metric (100.0 for NIQE, 0.0 for others).
C Algorithms
The following algorithms are provided:
Algorithm T
the main algorithm for Chain-of-Zoom inference.
Algorithm 2
the algorithm for GRPO- based human preference alignment training of VLMs.
Algorithm 1 -Cain--o- ooom Inference
Input: Low resolution image æt, Super-resolution model po. VLM Pø. Number of recursions Output: High resolution image Fn
I: 2g <- æt
2: for : + n do
3:
if 1 then
4:
C; - Po (2;-1)
5:
else
6:
C; + (polec/1,,i (1,1,,
7:
end if
8:
end for po(2,,10;-11
9:
Algorithm 2 GRPO-based RL Training of Prompt-Extraction VLM
Input: Base (prompt-extraction VLM pa with parameters Critic VLM Veritie Phrase blacklist Bptaae for Rehnsse Number of training iterations Ni Number of generations per prompt Ngen
Training dataset D = {xx I (1) M of multi-scale image crop pairs
Output: Fine-tuned prompt-extraction VLM ps
1: for iteration t 1 - Niter do for generation g Sample a multi-scale image pair (I+-2, 2;-1) from D
2:
N egen do
3:
Generate candidate prompt c.9) pol- (1(12111,,
4:
5:
Seritic + (c. 2i-)) Reriic Rescalel (Seritic; 0)
D Critic VLM scores prompt, range [o, 100)
6:
Rescale score to [0,
7:
Rphrase +-1
8:
for all E Bphrase do
9:
if phrase b is in C; then
(9)
Rphrase break
11:
12:
end end for Reep f- -FaccionOrRppeeeaNggaaaeeeeee c.9))
13:
14:
Repetition Penalty, range [-1,
15:
R(c(.))
Weritic Reritic + W Wprrase Rphrase Wrep Reep
b Total weighted reward
16:
end for A(9) R(c(.)) 1 EN' (()
Group-based advantage estimation
Nun
Calculate (O) with estimated advantages A
Detailed procedure in 34
19:
Update parameters o of using GRPO policy update with (
end for
16

---

D User Study
We further prove that GRPO fine-tuning the VLM enhances human preference alignment by per- forming a MOS (mean-opinion--coorf test various samples for 25 human participants. Specifically, we compare between three different VLM prompts: (i) prompts generated from only the LR input (i.e., (a Ti )); (ii) prompts generated from multi-scale image prompts (i.e., (o(c; 1,-1,,-;-21 and (iii) prompts generated after GRPO fine-tuning (i.e., (c I.-1, 2t-2) with RL-trained Φ).
Example questions are provided in Fig. After being given set of instructions, each user was asked evaluate five different of randomly mixed zoom sequences and five different mixed text generations. Users expressed their preference from "Very Bad' to 'Very Good', and the preferences were converted to score of further conduct pair- wise t-test to confirm the statistical significance of the scores.
of randomly
Resulting preference scores are shown Fig. TO We
sequence
What is the white box?
(e) Orange, jellyfish, bockground
(8) Red, blue, asstroct lines, background. vieront, modemn, artistic, dynemir, controst
(c) Red tentocles
Very
10
0
0
Figure 9: Example questions used for the MOS test. (Left) Human-Preferree Image Generation. Users were first given the instruction: 'In this survey, several samples will be given where we zoom into the center of the image. For each sequence of zoom, please rate how preferable the zoom is. (i.e.. If we zoom into this input image, will the images look like this sequence?) (Right) Human- Preferred Text Generation. Users were first given the instruction: 'In this section, several samples will be given where we try to explain the center of the image. For each image, please rate how preferable the explanation is. (i.e., Does the text explanation well explain what is in the white box?)'
(a)
(b)
660
Average Score
Average Score
2.96
3.56
3.86
2.30
2.70
4.24
Multiple
GRPO
Multiple
Figure 10: (a) Mean opinion scores for image generation. (b) Mean opinion scores for text generation. The scores on each bar denote the means and the error bars represent standard deviation. Significance of scores denoted as, *, p 0.05, **; 0.01, * #**, 0.001.

---

E
Additional Results for Performing CoZ with Open-Source OSEDiff
We further prove the applicability of our CoZ framework with the open-source OSEDiff 48 model (leveraging Stable Diffusion v2. backbone) available at htttps://giihiei g/tt:///tthubb... com/ cswry OSEDiff Quantitative comparison on DIV2K, DIVSK training datasets are provided in Tab. 4 and example qual- itative results are provided in Fig. IT Results show that CoZ is robust and shows good performance when utillizigg OSEDiff that leverages the Stable Diffusion v2.1 model its backbone.
Table 4: Quantitative comparison using the open-source OSEDiff. Best, Second-Bes -estt.
DIV2K
DIVSK MUSIQT
Scale
Method
NIQEL
MUSIQU
MANIQAT
CLIPIQAT
NIQE]
MANIQAT
CLIPIQAT
4x
Interpolation
12.1252
39.96
0.3396
0.2630
13.1984
40,26
0.3472
0.2672
Direct SR
4.7572
69.26
0.6366
0.7266
4,8659
68. 16
0.6349
0.7198
CoZ
4.7295
0.7184
CoZ (DAPE)
4.7577
69.26
0.6366
0.7265
4,8662
68.16
0.6350
0.7199
CoZ (VLM)
4.7241
69.42
0.6368
0.7279
4.8437
68.31
0.6346
0.7224
NN Interpolation
22.1215
24.01
0.3378
0,2346
22.2744
24.94
0.3465
0.2585
Direct SR
6.9951
51.88
0.5361
0.6206
7.4394
51.65
0.5472
0.6300
CoZ. (Null)
6.5369
61.86
0.5776
0.6988
6.7363
60.76
0.5842
0.6919
CoZ (DAPE)
6.5628
61.47
0.5799
0.6899
6.7985
60.58
0,5888
0.6926
CoZ (VLM)
6,5254
62.05
0.5801
0.6958
6.7348
61.11
0,59IM
0.6978
64x
NN Interpolation
27.4051
37.69
0,3803
0,3690
27.7533
37.13
0.3861
0,3837
15.6269
21.56
0.4255
0.5059
CoZ (Null)
8.9369
54,46
0.5598
0.6672
8 8.9645
53.48
0,5643
0.6655
CoZ (DAPE)
8.8681
53.50
0.5622
9. 9.0221
52.76
0,5687
0.6616
Co7 (VLM)
8.8259
54,84
0.5645
0.66115
8,8553
53.84
0,5716
0.6677
256x
NN Interpolation
34.8461
27.01
0.4179
0,5259
37,2612
26.98
0.4184
0.5299
Direct SR
15.6688
26.37
0,4593
0.5203
15.9510
26.17
0.4574
0.5231
CoZ (Null)
11,0907
47.14
0.5441
0.6223
11.0661
47.09
0.5439
0.6297
CoZ (DAPE)
11.0014
45.81
0.5440
0.6162
10.9251
46.50
0.5475
0.634S
CoZ (VLM)
10.8156
48.22
0.5495
0.6257
48.25
0.6384
Input
4X
16x
64x
256x
BUSB
BUS
Figure 11: Qualitative results for performing CoZ with the open-source OSEDifI (leveraging Stable
Diffusion v2.1 I as the diffusion backbone). The GRPO fine-tuned VLM is used as the prompt extractor.
18

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F
Using Qwen2.5-VL-77-IIstruue as the Critic VLM
Quantitative results using Sween.5-V1.-7B--nnstte as the critic VLM is provided in Tab. settings are identical to Tab. except for choice of critic VLM.
All
Table 5: Quantitative results using Qwen2.5-VL- Instruct as the critic VLM.
DIV2K
DIVSK
Scale
Method
NIQEL
MUSIQI
MANIQA
CLIPIQAT
NIQEJ
MUSIQI
MANIQAT
CLIPIQAT
4x
Direct SR
4.7320
67.00
0.6344
0.7005
4.8631
66,29
0.6359
0.6946
CoZ (VLM)
4.6546
66.89
0.6336
0.6993
4.7947
66.19
0.6349
0.6939
16x
Direct SR
7.2183
51.25
0.5406
0.6080
7.5855
50.17
0,5473
0.6035
CoZ (VLM)
6.2600
58.44
0.5955
0.6520
6.5513
$7.62
0.5996
0.6572
Direct
16.5915
22.54
0.3995
0.4309
16.5874
22.97
0,4069
0.4451
(VLM)
7,8554
52.12
0.5824
0.6229
8,0089
51.07
0.5826
0.6235
256x
Direct SR
16.1749
28.89
0.4470
0.5196
15.8667
28.90
0.4464
0.5256
CoZ (VLM)
8.9237
48.60
0.5786
0.5948
8,8982
48.06
0.5767
0.6040
G Zooming into Overlapping Regions
Cosine similarity measurements of overlapping patches across scales are provided in Tab. 6 Specifi- cally, for the first 500 images of the DIV2K dataset, we create overlapping patch pairs with overlapping amounts of 50% (i.e., the right half of one patch and the left half of the other patch represent the same region in the original image). The pixels of overlapping patches are flattened into a I-dimensional vector and their cosine similarity are calculated. We evaluate for patch pairs across different magnifi- cations (multiple recursions), and observe error accumulation. Cosine similarity for non-overlapping patches are also reported as reference. Results show that cosine similarity of overlapping patches are high, even for the highest magnification of 256x.
Table 6: Cosine similarity for overlappinghoonnoverlaaaingg regions.
Overlapping Regions
Non-overlapping Regions
Scale
Mean
Median
Minimum
Mean
Median
Minimum
4x
0.9977
0.9982
0.9864
0.7951
0.8196
0.3077
16x
5.5208
6.6210
0.5387
0.5535
5.5940
0.5346
64x
4.9803
6.6210
0.6361
0.6596
5.0305
0.6328
256x
4.8637
6.6210
0.6459
0.6835
4.9414
0.6405
H Full Image Super-Resolution
Chain-of-Zoome can be applied to super-resolution of full images by using the same tiling method used in prior works (e.g.. SeeSR, OSEDiff). Specifically, we use tiling of VAE and tiling of latents as described below produce full-image super-resolution results without boundary artifacts.
1. A given LR image is resized to the target resolution and VAE encoding is done in tiles, allowing encoding to be performed even in settings of limited GPU memory.
2. The encoded (low-resolution) latent is tiled into overlapping patches. For latent sizes of 64 x 64, we find overlaps of 16 to work sufficiently well.
3. Each low-resolution patch of 64 x 64 passes through the super-resolution network to become high-resolution patches, each guided by patch-specific prompts generated by the prompt- extractor VLM. Note that this step requires multiple passes of the VLM, computational bottleneck to be solved by future work.
4. The output high-resolution patches are multiplied by Gaussian weights in overlapping regions for smooth transposition between patches, and then combined to create the final high-resolution image.
5. The whole process is repeated as scale autoregression to achieve higher resolutions.
19

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I
Additional Qualitative Results
Additional qualitative results of extreme super-resolution by CoZ are provided below.
Input
16x
64x
1911
Figure 12: Extreme super-resolution of photorealistic images by CoZ up to 64x magnification.
20

---

Input 4x 16x
64x
258x
C
Figure 13: Extreme super-resolution of photorealistic images by CoZ up to 256x magnification.
21

---

Input
4x
16x
64x
258x
ILLIA
B BROS
SROS
LIONS
ns
Figure 14: Extreme super-resolution of photorealistic images by CoZ up to 256x magnification.
22

---

Input 4x
16x
64x
258x
Figure 15: Extreme super-resolution of photorealistic images by CoZ up to 256x magnification.
23

---

Input
4x
16x
64x
258x
CLY Y
en
upe
Figure 16: Extreme super-resolution of photorealistic images by CoZ up to 256x magnification.
24

---

Example Failure Modes of VLM before Fine- Tuning
J.1
Repetition
The second image is zoom-in of the first image. Based this knowledge, what is in the second image? Give me a set of words.
RESPONSES
Qwen2.5-VI.-3B-Instte
fur texture orange background animal fur close-up pattern texture orange fur texture orange fur background orange fur texture orange fur background orange fur texture orange fur texture orange fur texture orange fur texture orange fur texture orange fur texture orange fur texture orange fur texture orange fur texture orange fur texture orange fur texture orange fur
GRPO Training
fur
J.2
Unwanted Phrases
The second image is zoom-in of the first image. Based on this knowledge, what is in the second image? Give a of words.
RESPONSES
Owen2255-VL-3B-Instte
The second image shows close-up view of surface with textured pattern. The texture appears to be a combination of smooth and slightly raised areas, giving it somewhat wavy ripple-like appearance. The color gradient ranges from a lighter shade the top darker shade at the bottom, creating sense depth and dimension.
GRPO Training
feathers
25

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J.3 Suboptimal Results
The second image is zoom-in of the first image. Based on this knowledge, what is in the second image? Give me a set of words.
RESPONSES
Qwen2.5-VL-3B - ant leg
GRPO Training
crab claw
26

---

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Reviews:
[{"review_id": "mn2cYjkkmG", "reviewer": "Reviewer_3H4j", "summary": "This paper presents **Chain-of-Zoom (CoZ)**, a model-agnostic framework for extreme super-resolution (up to 256×) by autoregressively chaining multiple intermediate-scale inferences using a fixed 4× SR model. The method integrates multi-scale-aware prompts extracted by a vision-language model (VLM), which is further fine-tuned via GRPO-based reinforcement learning to align with human preference. Experimental results on DIV2K and DIV8K datasets show perceptual quality improvements over baselines, especially at high magnification factors.", "questions": "1. My theoretical analysis suggests that CoZ’s **SR model compute cost is bounded** by ~4/3× that of a one-step baseline (e.g., OSEDiff), due to decreasing image sizes at each zoom step (approximate cost: 1 + 1/4 + 1/16 + ...). However, the **VLM cost grows linearly** with zoom steps (e.g., 4× VLM calls for 256× upscaling using 4× SR). Is this analysis correct? Could the authors provide empirical runtime numbers to support this?\n\n2. How does CoZ handle **full-image inference**? The paper only mentions cropping and resizing local patches. Is there a recommended strategy (e.g., overlapping sliding windows, global prompt sharing) for coherent full-resolution image generation?\n\n3. Are there any failure cases where VLM prompts lead to semantic drift or hallucination over zoom steps? Could qualitative examples be added?", "limitations": "yes", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "### Strengths\n- **Well-motivated**: Addresses a real limitation of fixed-scale SR models at extreme upscaling factors.\n- **Modular and model-agnostic**: CoZ can wrap around existing SR models without retraining them.\n- **Innovative use of VLM**: Multi-scale prompt generation and GRPO-based alignment are novel and well-integrated.\n- **Strong qualitative results**: Visual comparisons are convincing at 64× and 256×.\n- **Comprehensive experiments**: Uses multiple no-reference metrics and ablations.\n\n### Weaknesses \n\n(**These are all practical concerns for deployment, but they do not detract from the fact that CoZ is an excellent and innovative contribution.**)\n\n- **Incomplete discussion of full-image inference**: The method focuses on local patches, but lacks a clear pipeline for applying CoZ to full-resolution images.\n- **VLM becomes a compute bottleneck**: Each zoom step requires a separate VLM inference, which grows linearly with the number of steps.\n- **No timing or runtime analysis**: The paper presents no quantitative evaluation of inference cost compared to baseline methods.", "was_revised": true, "final_justification": "The authors have provided clear and detailed responses that address my main concerns. I appreciate their efforts and find the clarifications satisfactory. I will keep my original score.", "rebuttal": "**W1, Q2. [3H4j] comments on a lack of discussion regarding full-image inference.**\n\nThough our original explanation of Chain-of-Zoom focuses on local patches, we would like to assure the reviewer that Chain-of-Zoom can be easily applied to super-resolution of full-resolution images. Such full-image super-resolution can be  easily done using the same tiling method as used in prior works (*e.g.*, SeeSR and OSEDiff).  Specifically, we use tiling of VAE and tiling of latents as describe in the following. More specifically,\n\n1. Given an LR image, it is resized to the target resolution and VAE encoding is done in tiles, which can be easily performed even in settings with limited GPU memory.\n2. The encoded (low-resolution) latent is tiled into overlapping patches. For latent sizes of $64\\times64$, as is usual for input image resolutions of $512\\times512$, we find that overlaps of 16 to sufficiently work well.\n3. Each low-resolution patch of $64\\times64$ passes through the super-resolution network to become high-resolution patches, each guided by patch-specific prompts generated by the prompt-extractor VLM.\n4. The high-resolution patches are combined, multiplied by Gaussian weights in overlapping regions for smooth transposition between patches.\n\nFollowing the procedure above, we successfully produced full-image super-resolution results without any boundary artifacts.  We will provide detailed  procedure as well as qualitative results regarding full-image super-resolution in our final manuscript.\n\n---\n**W3, Q1. [3H4j] suggests on giving additional runtime analysis compared to baseline methods.**\n\n Below, we give detailed runtime analysis for each method across varying scales (4x ~ 256x). The average inference time required (in seconds) to apply CoZ on a single image is evaluated on 500 images of the DIV2K dataset. We divide inference into two phases: super-resolution (SR) and prompt extraction (PE); accurate analysis on the computational time required for each phase is given accordingly.\n\n| Scale | Phase | OneStep (DAPE) | CoZ (Null) | CoZ (DAPE) | CoZ (VLM) |\n|-------|-------|----------------|------------|------------|-----------|\n| 4x    | SR    |     0.1467     |   0.1443   |   0.1460   |   0.1445  |\n|       | PE    |     0.0136     |   0.0000   |   0.0130   |   0.3777  |\n| 16x   | SR    |     0.1462     |   0.2886   |   0.2912   |   0.2896  |\n|       | PE    |     0.0130     |   0.0000   |   0.0254   |   0.7068  |\n| 64x   | SR    |     0.1462     |   0.4329   |   0.4363   |   0.4349  |\n|       | PE    |     0.0131     |   0.0000   |   0.0382   |   1.0301  |\n| 256x  | SR    |     0.1462     |   0.5774   |   0.5816   |   0.5802  |\n|       | PE    |     0.0132     |   0.0000   |   0.0509   |   1.3505  |\n\n---\n**W2. [3H4j] comments on the VLM becoming a computational bottleneck.**\n\nWe agree that the VLM can become a computational bottleneck, especially when approaching extreme scales (*e.g.*, 64x, 256x). Computational cost for the SR model increases linearly, since the actual 4x super-resolution we achieve is fixed as (128x128 &rarr; 512x512) for each zoom step. Meanwhile, computational cost for prompt extraction also increases linearly, regardless of the prompt extractor used.  While the use of  VLM as the prompt extractor is a current computational bottleneck, we envision that further work could be done to decrease this computational bottleneck, for example by generating multiple prompts across zoom steps while using the VLM only once.\n\n---\n**Q3. [3H4j] questions if any failure cases were observed.**\n\nWhen extracting text prompts, using the AR-2 modeling allows for the VLM to consider a larger receptive field compared to when using the Markov assumption. However, the VLM is still unable to consider the *full* image as recursions continue, thus out-of-context prompts can be created in cases where the global context can not be sufficiently inferred by the VLM.\n\nA specific example can be observed when zooming into an image of a jellyfish with red tentacles (the 37th image of the DIV2K dataset). As we zoom into the center of the image by $4\\times$ magnification four times recursively, we get the prompts \"jellyfish\", \"red lines\", \"red cables\", \"red wires\" in their respective orders. As the VLM loses any hint of the jellyfish in its receptive field, it loses the ability to infer that the red lines are actually tentacles of a jellyfish. Thus, there is a minor *semantic drift* from what would be red tentacles of a jellyfish into red wires. To solve such issues of semantic drift, we believe that subsequent research could be done on further widening the receptive field of the VLM. \n\nWe will add qualitative examples of failure cases with semantic drift (including the jellyfish example just described)  in our final manuscript."}, {"review_id": "ocFQ5BZ0aa", "reviewer": "Reviewer_wycu", "summary": "The paper addresses extreme image super-resolution (SR), with magnification levels reaching up to 256x. It introduces Chain-of-Zoom (CoZ), which decomposes extreme SR into sequential 4x SR steps using the same backbone SR model. To address the diminishing visual cues at high SR ratios, the method incorporates multi-scale-aware text prompts, where a vision-language model (VLM) generates captions for the current low-resolution (LR) image based also on its previous output. Additionally, GRPO is employed to enhance the captioning ability of the VLM in this extreme SR setting. Experiments on DIV2K and DIV8K demonstrate that the proposed CoZ method outperforms the direct SR strategy and other captioning baselines.", "questions": "- The paper should include time efficiency measurements for the proposed method to provide insights into its computational performance.\n- The distinction between *random degradation (same settings as OSEDiff)* and *4x specific upscaling* needs to be clarified, where OSEDiff should also use 4x upscaling for training.\n- The rationale for using InternVL2.5-8B as the critic VLM, which differs from the prompt-extraction VLM, should be explained. It is better the paper also explores the effects of using different critic VLMs. For example, what would the results be if Qwen2.5-VL-7B-Instruct were used as the critic VLM?\n- In Figure 7, the captions for the first row and the middle row differ. This difference should be clarified.\n- The paper could benefit from a discussion of related works that explore the use of text prompts to enhance SR performance.\n- For Figure 5, it would be helpful to explicitly indicate the SR ratio.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n- The paper focuses on an interesting topic: extreme super-resolution, with magnification levels reaching up to 256x.\n- It effectively decomposes extreme SR into sequential lower-ratio SR steps by utilizing the same SR backbone model. To tackle the issue of diminishing visual cues, the method introduces multi-scale-aware text prompts to provide additional semantic guidance.\n- The VLM generates captions based on the current image and its previous-stage SR result, which has a larger receptive field. This process is further enhanced by GRPO, improving the SR results.\n- The paper is well-structured and includes visually appealing illustrations that enhance clarity and presentation.\n\nWeaknesses\n- The validation is limited to comparisons among baselines (i.e., nearest neighbor interpolation, Direct SR, and CoZ variants: Null, DAPE, and VLM). It remains unclear how existing methods perform under similar conditions. Quantitative comparisons with existing approaches, such as diffusion-based SR methods or arbitrary-scale image super-resolution methods (and more visual comparisons), are recommended to strengthen the evaluation.\n- Model training details are insufficiently explained. Specifically, it is unclear whether the SR model is jointly trained with the VLM. Additionally, the source of the caption $c$ for SR model during training (e.g., from DAPE, VLM, or fine-tuned VLM) needs to be clarified.\n- For the ablation study on VLM training, the paper should provide quantitative results of different VLMs beyond the visual comparisons shown in Figure 7. Furthermore, results using a critic VLM for captions would provide a useful reference.\n- The quantitative improvement (Table 1) over the baseline CoZ (Null) is relatively small.", "was_revised": true, "final_justification": "The paper proposes a novel Chain-of-Zoom (CoZ) formulation, which decomposes extreme super-resolution (SR) into sequential 4x SR steps using the same backbone SR model. The overall quality and presentation of the paper are good. Initially, I had some questions about the experiments, but the authors' response has addressed most of my concerns. Therefore, I tend to increase my rating to Accept.", "rebuttal": "**W1. [wycu] recommends additional comparison with existing methods.**\n\nWe would first like to remind the reviewer that quantitative and qualitative results using the official implementation of **OSEDiff** (with Stable Diffusion v2.1 as backbone) are already provided in **Section E of Supplementary Material**. Applying CoZ greatly improves performance of OSEDiff, a SOTA diffusion-based SR method.\n\nWe further provide **quantitative results for other baseline methods**: arbitrary-scale SR methods (**LIIF [1]**) and diffusion-based SR methods (**SeeSR, S3Diff [2]**). All baselines show greatly degraded performance at high magnifications. We additionally show quantitative results for **applying CoZ to the pretrained S3Diff**, leveraging our multi-scale aware GRPO fine-tuned VLM. We will also include visual comparisons against baselines in the final manuscript as recommended.\n\nTable of Quantitative results for **LIIF**, **SeeSR**, and **S3Diff**:\n|||DIV2K||||DIV8K||||\n|---|---|---|---|---|---|---|---|---|---|\n|Scale|Method|NIQE ↓|MUSIQ ↑|MANIQA ↑|CLIPIQA ↑|NIQE ↓|MUSIQ ↑|MANIQA ↑|CLIPIQA ↑|\n|4x|LIIF|6.6210 |57.47|0.5456|0.4587|6.8050 |55.52|0.5386|0.4545|\n|4x|SeeSR|5.5208 |57.56|0.5387|0.5535|5.5940 |55.50|0.5346|0.5385|\n|4x|S3Diff|4.9803 |65.82|0.6361|0.6596|5.0305 |64.08|0.6328|0.6481|\n|4x|S3Diff + Ours|**4.8637**|**67.18**|**0.6459**|**0.6835**|**4.9414**|**65.31**|**0.6405**|**0.6680**|\n|16x|LIIF|11.4815|25.94|0.2860|0.3024|11.8734|26.73|0.2896|0.3178|\n|16x|SeeSR|9.6798 |41.68|0.4310|0.4669|9.8865 |40.02|0.4210|0.4601|\n|16x|S3Diff|6.7383 |51.14|0.5305|0.5886|7.2399 |50.32|0.5370|0.5841|\n|16x|S3Diff + Ours|**6.7310**|**56.82**|**0.5752**|**0.6168**|**6.8698**|**56.46**|**0.5836**|**0.6218**|\n|64x|LIIF|16.9215|20.02|0.3451|0.4131|17.4912|20.43|0.3522|0.4250|\n|64x|SeeSR|16.9095|21.83|0.4106|0.4193|16.9275|22.68|0.4167|0.4309|\n|64x|S3Diff|16.1421|21.54|0.3893|0.4917|16.5506|22.00|0.3968|0.5089|\n|64x|S3Diff + Ours|**8.7770**|**48.90**|**0.5490**|**0.5801**|**8.9794**|**48.07**|**0.5560**|**0.5909**|\n|256x|LIIF|23.8949|26.05|0.4108|0.5380|24.3300|26.01|0.4116|0.5423|\n|256x|SeeSR|20.9635|25.81|0.4438|0.5193|19.9628|25.94|0.4429|0.5203|\n|256x|S3Diff|16.7809|25.92|0.4324|0.5359|16.9952|25.91|0.4312|0.5415|\n|256x|S3Diff + Ours|**10.7668**|**43.59**|**0.5438**|**0.5570**|**10.5001**|**43.31**|**0.5417**|**0.5673**|\n\n\n---\n**W2, Q2. [wycu] requests additional explanation of training details.**\n\nWe appreciate the opportunity to further clarify the training details. Importantly, the SR model is *frozen*, and is *NOT* jointly trained with the VLM. The two are trained in a completely disjoint manner, and when applying CoZ on a pretrained off-the-shelf SR model we can bypass SR model training completely.  Additionally, when training our custom OSEDiff model (with Stable Diffusion 3.0 as backbone), DAPE is the source of caption $c$, as in the original OSEDiff paper.\n\nIn a question below, the reviewer also requests clarification between random degradation and 4x specific upscaling. By *random degradation (same settings as OSEDiff)* we refer to the degradation pipeline of Real-ESRGAN, which includes 4x upscaling as inquired by the reviewer. By *4x specific upscaling* we refer to strictly creating LR images by only performing 4x downsampling of HR images with no sorts of randomness. We would like to assure the reviewer that we follow the standard procedure in the paper to ensure overall robustness of our SR model for real-world images.\n\n---\n**W3. [wycu] requests quantitative results of different VLMs, including the critic VLM.**\n\nWe would first like to remind the reviewer that a **MOS (mean-opinion-score) test** among different VLMs is available in **Section D of Supplementary Material**.  MOS testing is a widely accepted quantitative metric in perceptual super-resolution literature (e.g., FAN [3], SRGAN [4], PULSE), and it is particularly relevant in our case, as our method explicitly aims to align the VLM with **human preferences**.\n\nFurthermore, we include **additional quantitative results** to assess the quality of prompts created by different VLMs while accounting for human preference. Specifically, we include quantitative results for the metrics **ImageReward** [5] and **HPSv2** [6] to assess the quality of VLM prompts used when zooming into images at extreme magnifications. We compare the alignment between generated prompt and original input image, to accurately assess preservation of semantic consistency. The table below includes quantitative results for using the **InternVL2.5-8B** critic VLM. Interestingly, our GRPO fine-tuned VLM even outperforms the critic VLM on the ImageReward metric, highlighting the effectiveness of GRPO fine-tuning.\n\nFor each row: **Single** refers to using prompts generated from only the LR input, i.e. $p_\\phi(c_i|x_{i-1})$; **Multi** refers to using prompts generated from multi-scale image prompts, i.e. $p_\\phi(c_i|x_{i-1},x_{i-2})$; **GRPO** refers to using $p_\\phi(c_i|x_{i-1},x_{i-2})$ with GRPO fine-tuned $\\phi$; **Critic** refers to using multi-scale image prompts generated by the critic VLM.\n\nResults for 64x magnification:\n\n||DIV2K||DIV8K||\n|---|---|---|---|---|\n||ImageReward ↑|HPSv2 ↑|ImageReward ↑|HPSv2 ↑|\n|Single|-1.9009|0.1784|-1.7699|0.1811|\n|Multi|-1.5222|0.1978|-1.3238|0.2033|\n|GRPO|-1.3558|**0.2087**|-1.2825|**0.2099**|\n|Critic|**-1.2480**|0.1900|**-1.1219**|0.1931|\n\nResults for 256x magnification:\n\n||DIV2K||DIV8K||\n|---|---|---|---|---|\n||ImageReward ↑|HPSv2 ↑|ImageReward ↑|HPSv2 ↑|\n|Single|-1.9705|0.1753|-1.8706|0.1777|\n|Multi|-1.7624|0.1850|-1.6635|0.1874|\n|GRPO|-1.6336|**0.2045**|-1.5634|**0.2041**|\n|Critic|**-1.4764**|0.1781|**-1.3353**|0.1816|\n\n---\n**W4. [wycu] comments about relatively small quantitative improvement over baseline CoZ in Table 1.**\n\nAs we approach larger magnifications, the quantitative improvement of using VLM prompts also increases, supporting our claim that the incorporation of VLM prompts helps *fill in* for insufficient conditioning at high magnifications. Specifically, at 256x magnification, **10.030 &rarr; 9.6405 decrease in NIQE** can be observed for DIV8K, representing a **non-negligible decrease of 4.20%**.\n\nMoreover, we would like to emphasize that incorporating VLM prompts proves to be better than null prompts **regardless of dataset or metric** at high magnifications of 64x or 256x. \nThis **consistent** improvement provides strong evidence for the effectiveness and reliability of our approach.\n\n---\n**Q1. [wycu] suggests including computational performance.**\n\nWe give a detailed runtime analysis below. The average inference time required (in seconds) to apply CoZ on a single image is evaluated on 500 images of the DIV2K dataset. We divide inference into super-resolution (SR) and prompt extraction (PE), and analyze computational time required for each phase.\n\n|Scale|Phase|OneStep (DAPE)|CoZ (Null)|CoZ (DAPE)|CoZ (VLM)|\n|---|---|---|---|---|---|\n|4x|SR|0.1467|0.1443|0.1460|0.1445|\n|4x|PE|0.0136|0.0000|0.0130|0.3777|\n|16x|SR|0.1462|0.2886|0.2912|0.2896|\n|16x|PE|0.0130|0.0000|0.0254|0.7068|\n|64x|SR|0.1462|0.4329|0.4363|0.4349|\n|64x|PE|0.0131|0.0000|0.0382|1.0301|\n|256x|SR|0.1462|0.5774|0.5816|0.5802|\n|256x|PE|0.0132|0.0000|0.0509|1.3505|\n\n---\n**Q3. [wycu] questions the rationale for using InternVL2.5-8B, and inquires of using different critic VLMs.**\n\nInternVL2.5-8B was selected as the critic VLM as a demonstration that different families of VLMs can be leveraged in our framework. To prove the robustness of our method, we additionally explore using Qwen2.5-VL-7B-Instruct as the critic VLM and present below the results for up to 5K steps. No other setting was changed except for selection of critic VLM. We present the Critic Reward and Total Reward for up to 5K steps, which are shown to consistently and significantly increase in a steep curve. Thus, we expect actual performance of the final model to likewise produce high quality results after rewards finally converge. We will include quantitative, qualitative results in our final manuscript.\n\n||Critic Reward|Total Reward|\n|---|---|---|\n| 500|0.6391|1.0949|\n|1000|0.6704|1.1311|\n|1500|0.6958|1.1581|\n|2000|0.7186|1.1532|\n|2500|0.7324|1.1675|\n|3000|0.7666|1.2316|\n|3500|0.7849|1.2511|\n|4000|0.7944|1.2660|\n|4500|0.8221|1.2828|\n|5000|0.8483|1.3003|\n\n---\n**Q4. [wycu] questions the captions for the first and middle row of Figure 7.**\n\nThe captions for the first row are generated by a VLM from only the LR input, i.e. $p_\\phi(c_i|x_{i-1})$ due to the AR-1 modeling. Captions for the middle row are generated by a VLM using multi-scale image prompts, i.e. $p_\\phi(c_i|x_{i-1},x_{i-2})$ from the AR-2 modeling.\n\n---\n**Q5. [wycu] recommends discussing works that explore the use of text in SR.**\n\nThank you for the suggestion. We will elaborate more on related works that explore text prompt usage in SR.\n\n---\n**Q6. [wycu] suggests explicitly indicating SR ratio in Figure 5.**\n\nWe will further explain that the top two rows are of SR ratio 64x, and that the bottom two rows are of SR ratio 256x.  We will clarify this at the final version.\n\n---\n**References**\n\n[1] Learning Continuous Image Representation with Local Implicit Image Function, CVPR 2021\n\n[2] Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors\n\n[3] Progressive Face Super-Resolution via Attention to Facial Landmark, BMVC 2019\n\n[4] Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, CVPR 2017\n\n[5] ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation, NeurIPS 2023\n\n[6] Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis"}, {"review_id": "x9iQ9BRDIP", "reviewer": "Reviewer_yTPV", "summary": "This work proposed a new autoregressive framework, CoZ, for extreme-scale SISR tasks. CoZ is motivated by the observation that most SR models are scale-specific and struggle to generalize to unseen, extreme magnification levels. To address that, CoZ decomposes the LR to HR transformation into a sequence of intermediate scale-states and multi-scale-aware prompts. These prompts are generated by a VLM, which is fine-tuned using a GRPO-based RL pipeline to align with human preferences. Experiment results show that CoZ can effectively reconstruct sharp and realistic HR images from LR inputs, even at extreme scales.", "questions": "1. Since there is no ground truth for high-scale results (e.g., 64×, 256×), how do you know the model is not hallucinating unrealistic details？The results in Figures 3 and 5 look good, but it's hard to know how far they are from the real high-res images.\n\n2. Have you measured the computational overhead of the full CoZ pipeline (especially at high scales like 256×)? How does inference time or memory consumption compare with a standard one-step SR method?\n\n3. Why is the AR-2 modeling clearly better than a simpler Markov setup?\nIn lines 128–130, you state that \"we **find** that multi-scale-aware text extraction is necessary by feeding \\(x_{i-1}\\) and the coarser state \\(x_{i-2}\\) into prompt generation.\"  Do you have any theoretical justification or quantitative evidence to support this design choice?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1.Motivation is clear and practical: they try to tackle the limitation of most SR models: work only at fixed scales and perform poorly when asked to upscale far beyond what they were trained on. Super useful and interesting. \n\n2.Well-written and organized: the paper is easy to follow, with clear explanations and well-structured sections.\n\n3.Promising results: the results show that the CoZ can produce sharp and natural-looking images, even when zooming in by 64× or more\n\nWeaknesses:\n\n1.The pipeline is complex and expensive to train: although the authors claim the method doesn’t require retraining the SR model, the full pipeline includes: a base SR model (like OSEDiff), a VLM for generating prompts, a separate critic VLM for training the prompt model with reinforcement learning. Plus, the model needs to run multiple inference steps for high magnifications. All of this adds up to high training cost and system complexity.\n\n2.The experimental section feels somewhat limited. In particular, it's difficult to assess whether the model suffers from hallucination issues based on the qualitative results alone, since there is no ground-truth reference available for comparison at extreme scales(e.g. figure 5 and 3.).", "was_revised": true, "final_justification": "The authors addressed my main concerns clearly. The system is less complex than they expected, hallucination is mitigated via prompt alignment with human preferences, and the computational overhead is justified. AR-2 modeling is supported by metrics and user studies. The novelty and results are sufficient to acceptance.", "rebuttal": "**W1. [yTPV] comments about the pipeline requiring high training cost and being complex.**\n\nWe clarify that our Chain-of-Zoom is a method to boost the performance of pretrained SR models when they are asked to solve for resolutions beyond their training regime. Accordingly, it is a framework designed to **wrap around existing SR models** without need for retraining. Thus, the only training target is a prompt-extraction VLM, chosen to be Qwen2.5-VL-3B-Instruct in this work. We point out that the chosen Qwen2.5-VL-3B-Instruct model is relatively quite small in the domain of VLMs, and we further reduce training costs by performing LoRA fine-tuning instead of full fine-tuning. We further mention in **Section B of Supplementary Material** that we use four NVIDIA GeForce RTX 3090 GPUs for the GRPO fine-tuning, and we believe this to be relatively affordable compared to similar baselines (*e.g.*, DAPE is trained with eight NVIDIA Tesla 32G-V100 GPUs).\n\nRegarding system complexity, we would like to point out that the SR model is *not* used when fine-tuning the VLM, and that the critic VLM is *not* used during inference time. This simplifies system complexity, ensuring that **no more than two distinct models are used during either training or inference**.\n\nFurthermore, once the VLM is fine-tuned, we can utilize it in the CoZ inference framework for *any* SR backbone model. The final fine-tuned VLM can be used in a **plug-and-play manner** in combination with any type of SR model, and does not require any retraining afterwards. We prove this by showing its effectiveness when combined with various types of SR models. Apart from our main results using a custom-trained SR model, quantitative and qualitative results using the official implementation of **OSEDiff** (with Stable Diffusion v2.1 as the backbone) is provided in **Section E of Supplementary Material**. We further provide quantitative results for applying CoZ to the pretrained, official implementation of **S3Diff [1]**, leveraging our **multi-scale aware GRPO fine-tuned VLM** in the table below.\n\nQuantitative results for **S3Diff**:\n|||DIV2K||||DIV8K||||\n|---|---|---|---|---|---|---|---|---|---|\n|Scale|Method|NIQE↓|MUSIQ↑|MANIQA↑|CLIPIQA↑|NIQE↓|MUSIQ↑|MANIQA↑|CLIPIQA↑|\n|4x|S3Diff|4.9803|65.82|0.6361|0.6596|5.0305 |64.08|0.6328|0.6481|\n|4x|S3Diff + Ours|**4.8637**|**67.18**|**0.6459**|**0.6835**|**4.9414**|**65.31**|**0.6405**|**0.6680**|\n|16x|S3Diff|6.7383|51.14|0.5305|0.5886|7.2399 |50.32|0.5370|0.5841|\n|16x|S3Diff + Ours|**6.7310**|**56.82**|**0.5752**|**0.6168**|**6.8698**|**56.46**|**0.5836**|**0.6218**|\n|64x|S3Diff|16.1421|21.54|0.3893|0.4917|16.5506|22.00|0.3968|0.5089|\n|64x|S3Diff + Ours|**8.7770**|**48.90**|**0.5490**|**0.5801**|**8.9794**|**48.07**|**0.5560**|**0.5909**|\n|256x|S3Diff|16.7809|25.92|0.4324|0.5359|16.9952|25.91|0.4312|0.5415|\n|256x|S3Diff + Ours|**10.7668**|**43.59**|**0.5438**|**0.5570**|**10.5001**|**43.31**|**0.5417**|**0.5673**|\n\n---\n**W2, Q1. [yTPV] inquires about experimental results, especially regarding the lack of ground-truth references available for comparison at extreme scales.**\n\nWe appreciate the reviewer's valuable feedback regarding concerns about potential hallucination issues at extreme super-resolution scales. Indeed, by its nature, super-resolution (SR) involves synthesizing plausible high-frequency details based on learned real-world data priors. Therefore, all SR methods inherently exhibit some degree of \"hallucination.\" However, what distinguishes effective SR methods, including ours, is their ability to generate **realistic and semantically coherent details**.\n\nThe critical factor here is **semantic consistency and controllability**. Our method explicitly addresses this by integrating **multi-scale-aware text prompts**, which guide the synthesis of details at each step of the resolution enhancement process. This design ensures that while the model does hallucinate high-frequency details, these details remain semantically controlled and coherent with the context provided by VLM-generated prompts. This semantic control significantly mitigates arbitrary or inconsistent hallucinations and provides interpretable and consistent SR outcomes even at extreme magnifications.\n\nIn particular, we use **human preference** as a target for alignment and fine-tune the prompt extraction VLM with GRPO, using the scores from a critic VLM as a proxy of human preference. Aside from our qualitative results, the **MOS (mean-opinion-score) test** result available in **Section D of Supplementary Material** is an important quantitative result commonly used in perceptual SR literature (FAN [2], SRGAN [3], PULSE). Results support that our method indeed produces realistic details through human preference alignment.\n\nFurthermore, in the following Tables we include **additional quantitative results** to assess the quality of prompts created by different VLMs while accounting for human preference. Specifically, we include quantitative results for the metrics **ImageReward** [4] and **HPSv2** [5] to assess the quality of VLM prompts used for zooming into images at extreme magnifications. We compare the alignment between the generated prompt and the original input image, to accurately assess the preservation of semantic consistency.\n\nResults for 64x magnification:\n\n||DIV2K||DIV8K||\n|---|---|---|---|---|\n||ImageReward &uarr;|HPSv2 &uarr;|ImageReward &uarr;|HPSv2 &uarr;|\n|Single|-1.9009|0.1784|-1.7699|0.1811|\n|Multi|-1.5222|0.1978|-1.3238|0.2033|\n|GRPO|-1.3558|**0.2087**|-1.2825|**0.2099**|\n|Critic|**-1.2480**|0.1900|**-1.1219**|0.1931|\n\nResults for 256x magnification:\n\n||DIV2K||DIV8K||\n|---|---|---|---|---|\n||ImageReward &uarr;|HPSv2 &uarr;|ImageReward &uarr;|HPSv2 &uarr;|\n|Single|-1.9705|0.1753|-1.8706|0.1777|\n|Multi|-1.7624|0.1850|-1.6635|0.1874|\n|GRPO|-1.6336|**0.2045**|-1.5634|**0.2041**|\n|Critic|**-1.4764**|0.1781|**-1.3353**|0.1816|\n\nFor each row: **Single** refers to using prompts generated from only the LR input, i.e. $p_\\phi(c_i|x_{i-1})$; **Multi** refers to using prompts generated from multi-scale image prompts, i.e. $p_\\phi(c_i|x_{i-1},x_{i-2})$; **GRPO** refers to using $p_\\phi(c_i|x_{i-1},x_{i-2})$ with GRPO fine-tuned $\\phi$; **Critic** refers to using multi-scale image prompts generated by the critic VLM. Results support that GRPO fine-tuning of our prompt-extraction VLM helps human preference alignment, thus reducing any unwanted hallucinations.\n\n---\n**Q2. [yTPV] suggests measuring the computational overhead of the full CoZ pipeline, in comparison with a standard one-step SR method.**\n\nWe are grateful for the constructive feedback, and we give detailed runtime analysis for each method across varying scales (4x ~ 256x) in the table below. The average inference time required (in seconds) to apply CoZ on a single image is evaluated on 500 images of the DIV2K dataset. We divide inference into two phases: super-resolution (SR) and prompt extraction (PE); accurate analysis on the computational time required for each phase is given accordingly. Regarding memory consumption, loading and running the VLM model during inference time requires around 8GB of additional memory.\n\n|Scale|Phase|OneStep (DAPE)|CoZ (Null)|CoZ (DAPE)|CoZ (VLM)|\n|---|---|---|---|---|---|\n|4x|SR|0.1467|0.1443|0.1460|0.1445|\n|4x|PE|0.0136|0.0000|0.0130|0.3777|\n|16x|SR|0.1462|0.2886|0.2912|0.2896|\n|16x|PE|0.0130|0.0000|0.0254|0.7068|\n|64x|SR|0.1462|0.4329|0.4363|0.4349|\n|64x|PE|0.0131|0.0000|0.0382|1.0301|\n|256x|SR|0.1462|0.5774|0.5816|0.5802|\n|256x|PE|0.0132|0.0000|0.0509|1.3505|\n\n---\n**Q3. [yTPV] requests further evidence to support the design choice of AR-2 modeling compared to the Markov setup.**\n\nWe would like to point out that the quantitative results regarding **semantic consistency and controllability** also serve as evidence of the importance of AR-2 modeling. For the tables below, **Single** refers to using prompts generated from only the LR input (*i.e.*, $p_\\phi(c_i|x_{i-1})$), which represents the Markov setup. **Multi** refers to using prompts generated from multi-scale image prompts (*i.e.*, $p_\\phi(c_i|x_{i-1},x_{i-2})$), which represents AR-2 modeling. Using AR-2 modeling improves semantic consistency of the SR process.\n\nResults for 64x magnification:\n\n||DIV2K||DIV8K||\n|---|---|---|---|---|\n||ImageReward &uarr;|HPSv2 &uarr;|ImageReward &uarr;|HPSv2 &uarr;|\n|Single|-1.9009|0.1784|-1.7699|0.1811|\n|Multi|**-1.5222**|**0.1978**|**-1.3238**|**0.2033**|\n\nResults for 256x magnification:\n\n||DIV2K||DIV8K||\n|---|---|---|---|---|\n||ImageReward &uarr;|HPSv2 &uarr;|ImageReward &uarr;|HPSv2 &uarr;|\n|Single|-1.9705|0.1753|-1.8706|0.1777|\n|Multi|**-1.7624**|**0.1850**|**-1.6635**|**0.1874**|\n\nWe further mention that the user study in **Section D of Supplementary Material** also compares between the Markov setup of $p_\\phi(c_i|x_{i-1})$ and AR-2 modeling of $p_\\phi(c_i|x_{i-1},x_{i-2})$. For both human-preferred image generation and human-preferred text generation, the AR-2 modeling proves more effective.\n\n---\n**References**\n\n[1] Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors\n\n[2] Progressive Face Super-Resolution via Attention to Facial Landmark, BMVC 2019\n\n[3] Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, CVPR 2017\n\n[4] ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation, NeurIPS 2023\n\n[5] Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis"}, {"review_id": "tAZm6pBc1d", "reviewer": "Reviewer_yu7b", "summary": "The paper introduces Chain-of-Zoom (CoZ), a framework that applies cascaded image SR on center-cropped SR reconstructions.\nInstead of just taking the LR (previous zoom state), it proposes to use the last two zoom states to generate a text prompt, which makes it technically different from how SeeSR is formulated (despite being applied to another kind of task).\nAlso,  the VLM is fine-tuned via Generalized Reward Policy Optimization (GRPO) against a critic VLM.", "questions": "- How would the proposed VLM applied in SeeSR perform in classical image SR settings?\n- What is the difference between single-step SR and your proposed double-step SR (1 zoom state vs 2 zoom states) quantitatively?\n- If you Zoom in two patches that share overlap, how well/consistent is the overlap in the respective SR patches? How does this error accumulate over multiple Zooms?\n- Could you highlight for me why this task was invented in the first place and is not compared to SeeSR, but cascaded?\n\nWhile the authors discuss error accumulation and potential misuse (e.g., reconstructing sensitive imagery), they do not compare to classical prompt-aware SR baselines (e.g., SeeSR) or isolate the benefits of VLM prompts in a non-chain setting. \nI suggest adding these comparisons to fully characterize CoZ’s novelty and impact.\nSo far, it feels like the authors invented a new task in order to justify their proposed method.\nAs a result, I tend towards reject but I might change my mind based on the author's reply.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- Solid probabilistic formulation (AR-2 factorization).\n- comprehensive evaluation using no-reference perceptual metrics (NIQE, MUSIQ, MANIQA-pipal, CLIPIQA)\n\nWeakness:\n- No comparison to the state-of-the-art by reformulating the problem of cascaded image SR on center-cropped SR reconstructions.", "was_revised": true, "final_justification": "All raised concerns were addressed accordingly and the newly added results/tables further improved the quality of the submission (see Q1-Q3)! I especially appreciate the provided experiments regarding cosine similarity!", "rebuttal": "**Q4. [yu7b] questions the motivation of the work, and the reason for its cascaded approach without being compared to SeeSR.**\n\nWe would like to start by answering the last question the reviewer raised, so as to help give the reviewer a better understanding of the motivation of our work before delving into the other concerns raised. The motivation behind our work stems from a fundamental limitation in existing single-image super-resolution (SISR) models, such as SeeSR: **they are inherently constrained by their training configurations, and typically fail or produce severe artifacts when pushed beyond these scales.** To support this claim, we provide the quantitative results of attempting super-resolution over increasing magnifications with SeeSR in the response below (please refer to the response for W1 below).\n\nAlthough SeeSR shows strong performance at low 4x magnification, performance is degraded at magnifications higher than its training regime (*e.g.*, 16x, 64x, 256x). In this work, our motivation is to solve this problem and effectively utilize pretrained SR models to achieve much higher magnifications than they are originally trained for. Thus, we did not originally compare with SeeSR as our method is focused on *going **beyond** the training regime* of SR models, rather than *achieving superior performance **within** the training regime* for super-resolution. It is also worth noting that directly training models for extreme magnification factors (*e.g.*, 64× or 256×) is computationally prohibitive due to immense resource requirements, making such approaches practically infeasible.\n\nThe main objective of our work, therefore, is to overcome this scalability bottleneck by introducing Chain-of-Zoom (CoZ), a framework designed specifically to enable existing, well-trained SR models (like SeeSR) to achieve significantly higher resolutions through a autoregressive process. CoZ accomplishes this by breaking down extreme SR tasks into manageable intermediate stages, each guided by semantically coherent multi-scale prompts. By design, **CoZ complements rather than competes with models like SeeSR**; it effectively broadens their capabilities to achieve magnifications beyond their original operational range without additional training. Hence, CoZ is not intended as a replacement but as a versatile and resource-efficient framework that substantially extends the practical applicability and scalability of existing super-resolution models.\n\nIn summary,  our **multi-scale-aware, GRPO fine-tuned VLM** is introduced as a novel method to *aid* the aforementioned autoregressive process. Based on the observation that text prompts are important for creating high-frequency details, we leverage the novel prompt-extraction VLM as a *replacement* for DAPE (a widely used prompt-extractor that can be found in SeeSR, OSEDiff, etc.) and prove its effectiveness through comprehensive experiments.\n\nBelow, we further provide detailed point-by-point responses to the other concerns raised.\n\n---\n**W1. [yu7b] suggests adding comparisons with state-of-the-art SR baselines.**\n\nWe would like to remind the reviewer that quantitative and qualitative results using the another (official) implementation of **OSEDiff** (with Stable Diffusion v2.1 as the backbone) is provided in **Section E of Supplementary Material**. We show that applying CoZ greatly improves performance of OSEDiff, a state-of-the-art SR method, especially at high magnifications.\n\nBelow, we further provide **quantitative results for other baseline methods**: arbitrary-scale SR methods (**LIIF [1]**) and diffusion-based SR methods (**SeeSR, S3Diff [2]**). The quantitative results for SeeSR was provided in the previous response, and results for other baselines are given below. All three baselines show greatly degraded performance at high magnifications. For the case of S3Diff, we additionally show **quantitative results for applying CoZ to the pretrained, freely accessible S3Diff, leveraging our multi-scale aware GRPO fine-tuned VLM**. All the results clearly confirm the significant performance improvement by CoZ.\n\nQuantitative results for **LIIF**, **SeeSR**, and **S3Diff**:\n|||DIV2K||||DIV8K||||\n|---|---|---|---|---|---|---|---|---|---|\n|Scale|Method|NIQE ↓|MUSIQ ↑|MANIQA ↑|CLIPIQA ↑|NIQE ↓|MUSIQ ↑|MANIQA ↑|CLIPIQA ↑|\n|4x|LIIF|6.6210 |57.47|0.5456|0.4587|6.8050 |55.52|0.5386|0.4545|\n|4x|SeeSR|5.5208 |57.56|0.5387|0.5535|5.5940 |55.50|0.5346|0.5385|\n|4x|S3Diff|4.9803 |65.82|0.6361|0.6596|5.0305 |64.08|0.6328|0.6481|\n|4x|S3Diff + Ours|**4.8637**|**67.18**|**0.6459**|**0.6835**|**4.9414**|**65.31**|**0.6405**|**0.6680**|\n|16x|LIIF|11.4815|25.94|0.2860|0.3024|11.8734|26.73|0.2896|0.3178|\n|16x|SeeSR|9.6798 |41.68|0.4310|0.4669|9.8865 |40.02|0.4210|0.4601|\n|16x|S3Diff|6.7383 |51.14|0.5305|0.5886|7.2399 |50.32|0.5370|0.5841|\n|16x|S3Diff + Ours|**6.7310**|**56.82**|**0.5752**|**0.6168**|**6.8698**|**56.46**|**0.5836**|**0.6218**|\n|64x|LIIF|16.9215|20.02|0.3451|0.4131|17.4912|20.43|0.3522|0.4250|\n|64x|SeeSR|16.9095|21.83|0.4106|0.4193|16.9275|22.68|0.4167|0.4309|\n|64x|S3Diff|16.1421|21.54|0.3893|0.4917|16.5506|22.00|0.3968|0.5089|\n|64x|S3Diff + Ours|**8.7770**|**48.90**|**0.5490**|**0.5801**|**8.9794**|**48.07**|**0.5560**|**0.5909**|\n|256x|LIIF|23.8949|26.05|0.4108|0.5380|24.3300|26.01|0.4116|0.5423|\n|256x|SeeSR|20.9635|25.81|0.4438|0.5193|19.9628|25.94|0.4429|0.5203|\n|256x|S3Diff|16.7809|25.92|0.4324|0.5359|16.9952|25.91|0.4312|0.5415|\n|256x|S3Diff + Ours|**10.7668**|**43.59**|**0.5438**|**0.5570**|**10.5001**|**43.31**|**0.5417**|**0.5673**|\n\n---\n**Q1. [yu7b] questions about applying the proposed VLM to SeeSR.**\n\nThe direct integration of our proposed VLM into SeeSR requires additional complexity due to the architectural requirements of SeeSR, specifically its reliance on ControlNet architecture. This confirms the efficiency of our CoZ scheme.  Specifically, SeeSR necessitates both \"soft\" and \"hard\" embeddings: the \"soft embeddings\" (continuous embeddings) can only be generated by the Degradation-Aware Prompt Extractor (DAPE). Since the proposed VLM generates discrete textual prompts (which correspond only to \"hard embeddings\"), simply replacing DAPE with the proposed VLM within the SeeSR architecture is not feasible without significant architectural modifications.  Furthermore, we also delivered proper comparison between DAPE and our proposed VLM in **Figure 5** and **Table 1** of the main paper, confirming the performance improvements of our proposed VLM.\n\n---\n**Q2. [yu7b] questions the quantitative difference between single-step SR and double-step SR.**\n\nThe reviewer is kindly reminded  that the quantitative results in **Table 1** of the main paper already contained the difference between single-step SR and double-step SR. Specifically, 'Direct SR' represents reaching the target magnification (*i.e.*, one of 4x, 16x, 64x, 256x) in a single step, and quantitative results show how image quality becomes degraded at high magnifications. Meanwhile 'CoZ' represents our proposed method, in which we do not attempt to reach the target magnification in a single pass, and split the SR process into smaller (easier) steps. While difference seems small at lower magnifications, the quantitative difference between 'Direct SR' and 'CoZ' become significant as we reach higher magnifications.\n\nThus, in answer to the question of \"quantitative difference between single-step SR and double-step SR\", we could say that the 16x magnification scenario specifically represents this case as we decompose the target 16x magnification into two steps of 4x magnification. Compared to attempting 16x magnification in one step (as denoted by 'Direct SR'), we show that using the Chain-of-Zoom framework produces superior results. The comparison table of Quantitative results for LIIF, SeeSR, and S3Diff above also confirmed the significant performance gain by CoZ.\n\n---\n**Q3. [yu7b] inquires of results for zooming into two patches that show overlap, especially across multiple zooms.**\n\nWe thank the reviewer for the intriguing suggestion, and provide **cosine similarity results** for overlapping patches of 50% across scales. Specifically, for the first 500 images of the DIV2K dataset, we create overlapping patch pairs with overlapping amounts of 50% (*i.e.*, the right half of one patch and the left half of the other patch represent the same region in the original image). The pixels of overlapping patches are flattened into a 1-dimensional vector before cosine similarity calculation. We evaluate for patch pairs across different magnifications (multiple recursions), and observe error accumulation. Furthermore, we also report cosine similarity for non-overlapping patches as reference. Results show that cosine similarity of overlapping patches are high, even for the highest magnification of 256x. This confirms the semantically consistent guiding capability of CoZ.\n\nCosine similarity results for overlapping regions:\n|Scale|Cosine Similarity (Mean)|Cosine Similarity (Median)|Cosine Similarity (Minimum)|\n|---|---|---|---|\n|4x|0.9977|0.9982|0.9864|\n|16x|0.9962|0.9976|0.9546|\n|64x|0.9925|0.9966|0.9048|\n|256x|0.9484|0.9829|0.2408|\n\nCosine similarity results for non-overlapping regions:\n|Scale|Cosine Similarity (Mean)|Cosine Similarity (Median)|Cosine Similarity (Minimum)|\n|---|---|---|---|\n|4x|0.7951|0.8196|0.3077|\n|16x|0.8279|0.8613|0.3003|\n|64x|0.8722|0.9306|0.1082|\n|256x|0.9121|0.9665|0.2349|\n\n---\n**References**\n\n[1] Learning Continuous Image Representation with Local Implicit Image Function, CVPR 2021\n\n[2] Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors"}]

Author rebuttal:

Meta-review: This paper presents Chain-of-Zoom (CoZ), a novel autoregressive framework for extreme super-resolution (SR) that scales up to 256×. It makes a compelling and timely contribution to the SR community by tackling the longstanding challenge of scaling SR to extreme magnifications. The introduction of multi-scale VLM guidance with preference alignment is both novel and technically sound. While computational overhead remains a consideration, the method is general, effective, and well validated. After the rebuttal, reviewers reached a clear positive consensus (6554). The AC agrees with the reviewers and recommends acceptance.
Decision: Accept (spotlight)