One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs
Authors: Yuanzhi Zhu
Ruiqing Wang
Shilin Lu
Hanshu Yan
Junnan Li
Kai Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion distillation; rectified flow; one-step diffusion
Primary area: generative models

Abstract: Recent advances in diffusion and flow-based generative models have demonstrated remarkable success in image restoration tasks, achieving superior perceptual quality compared to traditional deep learning approaches. However, these methods either require numerous sampling steps to generate high-quality images, resulting in significant computational overhead, or rely on common model distillation, which usually imposes a fixed fidelity-realism trade-off and thus lacks flexibility. In this paper, we introduce OFTSR, a novel flow-based framework for one-step image super-resolution that can produce outputs with tunable levels of fidelity and realism. Our approach first trains a conditional flow-based super-resolution model to serve as a teacher model. We then distill this teacher model by applying a specialized constraint. Specifically, we force the predictions from our one-step student model for same input to lie on the same sampling ODE trajectory of the teacher model. This alignment ensures that the student model's single-step predictions from initial states match the teacher's predictions from a closer intermediate state. Through extensive experiments on datasets including FFHQ (256$\times$256), DIV2K, and ImageNet (256$\times$256), we demonstrate that OFTSR achieves state-of-the-art performance for one-step image super-resolution, while having the ability to flexibly tune the fidelity-realism trade-off. 
Code and pre-trained models are available at \href{https://github.com/yuanzhi-zhu/OFTSR}{https://github.com/yuanzhi-zhu/OFTSR} and \href{https://huggingface.co/Yuanzhi/OFTSR}{https://huggingface.co/Yuanzhi/OFTSR}, respectively.

Published as a conference paper at ICLR 2026
ONE-STEP FLOW FOR IMAGE SUPER-RESOLUTION WITH TUNABLE FIDELITY-REALISH TRADE-OFFS
Yuanzhi Zhu
Ruiqing Wang"
Shilin Lu
Junnan Li
Hanshu Yan Kai Zhang?!
Nanjing University *Rhymes.Al
Nanyang Technological University
ABSTRACT
Recent advances in diffusion and flow-based generative models have demon- strated remarkable success image restoration tasks, achieving superior percep- tual quality compared to traditional deep learning approaches. However, these methods either require numerous sampling steps to generate high-quality images, resulting in significant computational overhead, rely common model dis- tillation, which usually imposes a fixed fidelity-realism trade-off and thus lacks flexibilityy. In this paper, introduce OFTSR, novel flow-based framework for one-step image super-resolution that can produce outputs with tunable levels of fidelity and realism. Our approach first trains conditional flow-based super- resolution model to serve as a teacher model. We then distill this teacher model by applying a specialized constraint. Specifically, we force the predictions from our one-step student model for same input to lie on the same sampling ODE trajectory of the teacher model. This alignment ensures that the student model's single- step predictions from initial states match the teacher's predictions from closer intermediate state. Through extensive experiments on datasets including FFHQ 256), DIV2K, ImageNet (256 x 256) and real world SR datasets, we demon- strate that OFTSR achieves sftt--ff-the-at performance for one-step image super- resolution, while having the ability to flexibly tune the fidelity-reall realism trade- off. Code and pre-trained models are available at htt.ggiihuu..o//uuuuuuaa zhu/OFISS and https:/hhuggingfaceccom http:/gginnnnan.ecooaaaa/////100 respectively.
INTRODUCTION
Recently, diffusion and flow-based generative models have demonstrated the ability to generate im- ages with higher quality (Ramesh al. 2022 Nichol & Dhariwal, 202T Dhariwal & Nichol, 202T)
than earlier generative models such as Generative Adversarial Networks (GANs) (Goodfellow et al.,
2020 Karras al., 2019). Normalizing Flows (NFs) (Dinh et al.) 2016) and Variational Autoen- coders (VAEs) (Kingma & Welling 2013 Razavi al. 2019). Beyond visual generation, diffusion models have shown remarkable success across a variety of tasks, including image editing (Hertz 3D content generation (Poole et al., 2022
et al., 2022 Brooks et al.) [023 Kawar et al. 2023).
Wang et al.
2023a| Liu et [0.3b] Wu et al. 2022
Wang et al.) 2024a), and image restoration 2022b Zhu et al. 2023 Delbracio & Mi-
(Kawar al.
2022 Chung al. 2022 Wang et al.
lanfar: 2023
Lin et al.) 2023), with particularly notable advancements in image super-resolution
(SR) (Saharia et al. 2021 Chen al. 2023 Yue al.
2024b
Wang al. 2024b).
Existing diffusion and flow-based SR methods can be broadly divided into two approaches: training- free methods (Zhu et al. 2023 Kawar et al. [2022] Wang et al. 2022b) Chung et al., 2022 Alkhouri
et al.
2024| [Mardani et al. 2023] [Song et 2023a). and training-based methods (Saharia et al.
2021
Luo et al. [023b] Liu al. 2023ai Yue al. 2023 Wang al. 2024c] Yue al. 2024b Liu et al. 2024: Delbracio & Milanfar 2023). Training free methods decompose the conditional
probability into a prior term and a likelihood term, with each term associating directly to a specific
subproblem (Zhu et al. 2023). During iterative sampling, the prior subproblem is naturally han- dled by pre-trained unconditional diffusion models, which serve as powerful regularizers to guide
"qqual contribution.
Work done while interned at Rhymes. Al (zyzeroer@ @gmal..cmmilmm Coorrsponning author (kaizhang @ @jj.eedu.c))

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Published as a conference paper at ICLR 2026
(a)
LR
Augmented LR
(b)
Ours
DDRM
DifEPER
SITCOM
One-step model
70
CDDD
0
DDC
RosShift
SinS8
22.5
23.0
23.5
24.0
24.5
25.0
25.5
26.0
PSNR (dB)
PSNR: 25, dB LPIPS: 0.318
PSNR: 23.9 LPIPS: 0.133
Figure 1: Our final model takes the concatenation of low-rcsolution image with its noise-augmented version as input, and is able to generate high-resolution outputs with either high realism or high fidelity by adjusting the interpolation parameter t. We indicate the PSNR and LPIPS value on the output images. (b) Comparison of different diffuusin and flow based image super-resolution methods on the ImageNet 256 x 256 dataset. Bubble radius indicates the NFEs used by the methods.
the solution toward realistic High Resolution (HR) images. Meanwhile, the likelihood subprob- lem is addressed through specialized optimization techniques or analytical approximations to ensure fidelity to the observed Low Resolution (LR) image. On the other hand, training-based methods directly model the conditional probability using paired data, either by training from scratch (Sa- haria et al., 2021 Delbracio & Milanfar (2023) or by incorporating additional control modules into existing generative priors (Wang et al., 2024b; Yu et al. 2024 Lin et al., [023 Rombach et al.,
2022]. Several other bridge-based methods (Luo et al. 2023b et al. 2023a Yue et al. 2023
Chung et al.] (024) have also been proposed for general image-to-image translation tasks, sharing similarities with direct learning approaches.
Despite the promising results of the above methods, they require many iterative sampling steps to achieve high perceptual quality, and reducing the number of iterations often results in higher fidelity but lower perceptual quality. In this sense, their fidelity-realism realism trade-offs are achieved at the cost of more sampling steps. In order to achieve high perceptual quality with fewer sampling steps, some attempts (Wang al., 2024c [Le al. 2024 Wu et al.
2024 Xie et al. 2024 Li al. 2024)
have been made to distill the diffusion sampling process into a single step with diffusion disti lation
approaches (Luhman & Luhman 2021 Salimans Ho
2022: Liu et 2022 Song al. 2023b
Yan et al.) [024]] Yin et al.) 2024b a Sauer et al. 20255) However, while these methods improve efficiency, they sacrifice flexibility by limiting control over the iideity-reallise trade-off, reducing
their applicability in domains where different tasks require varying levels of fidelity and realism,
such as medical imaging. remote sensing and film upscaling (Greenspan 2009 Li al. 2023a
Wang et al. 2022a Mentzer et al. 2220 Joshi et al. 2025).
In this paper, we propose OFTSR that achieves one-step image SR and preserves the capability to produce outputs with tunable fidelity-realish trade-offs. Specifically, OFTSR uses two-stage pipeline. In stage one we train a noise-augmented conditional rectified flow to expand the support of the initial distribution: poise-perturbed LR images form the initial distribution while the LR images are used as conditions, enabling diverse HR reconstructions from single LR. the second stage, distillation strategy is proposed to restrict the student model's predictions match the same Ordinary Differential Equation (ODE) induced by the teacher model from the first stage.
Our main contributions be summarized follows:
Noise-augmented Conditional Rectified Flow for Image Restoration: We introduce an enhanced conditional rectified flow model for image restoration. By leveraging a noise- augmented LR conditioning strategy, approach enables more effective L-R-ooditioneed diffusion restoration, serving as both a general restoration framework and the foundational stage for our proposed distillation algorithm.
One-Step Diffusion Distillation with Flexible -idelity-Realism Trade-off: We introduce distillation strategy applicable to empirical probability flow ODEs of any pre- rre-tranned conditional diffusion or flow model. Unlike prior methods that limit flexibility, ours enables one-step sampling while preserving control over fidelity and perceptual realism for SR.
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generative Published as a conference paper at ICLR 2026
State-of-the-Art (SOTA) Performance Benchmark Datasets: Extensive experiments on DIV2K (Agustsson & Timofte 2017). FFHQ (Karras et al. 2019). ImageNet (Deng ct al., 2009) and several real world SR dataset including RealSR (Cai et al. 2019), Re- alSet80 (Yue et al. 2024b) and Reall.Q250 (Ai et al. (025) show that OFTSR achieves competitive one-step reconstruction, surpassing recent SOTA methods in both perceptual quality and fidelity.
2 BACKGROUND
2.1 DIFFUSION AND FLOW-BASED BASED GENERATIVE MODELS
Drawing inspiration from non-equilibrium thermodynamics, diffusion models operate through two core processes: a forward diffusion process that gradually adds Gaussian noise to data until it be- comes pure noise, and reverse denoising process that systematically reconstructs the original data by removing noise (Sohl-Dickstein et al. 2015; Ho et al.) [020] SSong et al. 2020b). Let X repre- sent the data x at timestep I. The forward process can be formally described by the Itō Stochastic Differential Equation (SDE) (Song et al. 2020b):
dx, fxx,tt + gedw,
where w is the standard Wiener process, ft R - R is the drift coefficient, and ga scalar function called the diffusion coefficient.
R
R
is
For every diffusion process described by Eq. (1), there exists corresponding deterministic Proba- bility Flow Ordinary Differential Equation (PF-ODE) that maintains the same marginal probability density:
dx: dt
fix:
2 Vi log (x.))
where pe(-) represents the marginal probability density at time The term Vx, log pe pc(),) (xx) is known the score function, which can be approximated by neural network sæ (x, t) with parameters 0.
This network is typically trained using score matching techniques (Hyvärinen & Dayan 2005 Song
& Ermon 2019
Song et al. 2020a)
To generate data samples, the process begins with Gaussian noise drawn from an initial Gaussian distribution Po and solves Eq. (2) numerically from t = 0 to t 1. By utilizing the learned score function (x,,, t), the empirical PF-ODE can be obtained as: dx fix;
1/2 5 t).
Rectified flow (Liu et al. 2022
Liu 2022
Lipman et al. 2022 Esser et al. 2024)
modeling framework based on ODEs. Given an initial distribution po and target data distribution
P1, rectified flow trains a neural network to parameterize a velocity field using the following loss function:
[ [vloooxx (xi -x0
C=(0)= = Ex, Mppxxaa
dt where -1 f)xo + tx1.
(3)
Once trained, sample generation is achieved by solving the empirical ODE dx. vo( () from f = 0 to = 1. In practical implementations, this empirical ODE is solved numerically using standard ODE solvers, ranging from the simple forward Euler method to higher-order methods such as RK2 and RK45.
de
2.2 PERCEPTION- CENEEEPTIOO-ISTTTTTTUE DISTORTION TRADE-OFF
The perception-distee -iitttotonn (rdelitm-idellit) trade-off (Blau Michaeli 2018 is fundamental con- cept in image restoration. It describes the inherent trade-off between perceptual realism and fidelity to the ground truth, and mathematically proves that it is generally not possible to achieve both good perceptual realism and high fidelity simultaneously.
To address this challenge, researchers have explored various approaches to enable tunable trade-offs between these two desirable qualities. One common technique involves interpolating between the weights of two models with the same architecture, trained with GAN loss and mean squared error loss (Wang al. 2018). Recently, diffusion models have emerged promising approach for
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this task. The iterative sampling nature of diffusion models provides flexible means of controlling the desired trade-offs. By adjusting the Number of Function Evaluations (NFEs), users can generate reconstructions that better match their specific requirement requirements (Chung et al. 2024). Specifically, lower NFEs tend to result in reconstructions with reduced distortion, as the output regresses towards the mean (Delbracto & Milanfar 2023). Conversely, higher NFEs prioritize perceptual quality, if it comes at the expense of some distortion from the ground truth (similar to Fig. 66.
METHOD
In this section, we introduce the OFTSR frame- work for one-step SR that can restore HR im- ages with either high realism or high fidelity. We achieve this goal through two-stage pro- cess: first, we train direct flow-based model for SR, and then we distill this model into a sim- plified one-step variant. In Sec. 3.1 present a simple puise-augmented conditional flow that expands the support of the initial distribution, enabling diverse reconstruction. In Sec. [3.2 propose to distill the student model by stricting its predictions on the same ODE using teacher model from Sec. 3.1
Teacher Va
X
XO
One-step model
Figure 2:
Illustration of the proposed distillation loss. Rather than directly distilling from the teacher, we leverage the teacher to align the one-step intermediate outputs, x; and xs, along teacher's PF-ODE trajectory. For simplicity, LR conditioning is omitted in this figure.
3.1 NOISE AUGMENTED CONDITIONAL FLOW
Unlike diffusion models, flow-based models have the advantage that their initial distribution is not limited to Gaussian distributions. This flexibility suggests a natural approach for image restoration directly learning a flow that maps the distribution of LR images (PLR) to that of HR images (PHR)- However, our initial experiments (see Tab. 7 showed poor performance with this direct approach, aligning with findings from several recent works (Delbracio & Milanfar, 2023 Kim et al. 2024 Lee et al. 2024). This training procedure tends to collapse the LR-HHR mapping: during inference each LR IS driven toward a single HR.
To overcome this limitation, propose noise- auggeeed approach to process LR images. For any input image XLR, we construct our initial distribution (x( = Ty by adding Gaussian noise with standard deviation dp. Specifically, adopt Variance-Presserving (VP) noising operation
(Ho et al. 2020 Song et al. 2020b):
X9 V 1100--3 xL + opE,
where is a standard Gaussian noise. While this noise perturbation facilitates better generalization, inevitably causes information loss in the LR image. To address this, we incorporate XLR as a conditional input to our model in Fig. [ This VP formulation, together with the condition XLR- makes our method particularly versatile, encompassing previous approaches as special cases. When
Tp O, our method reduces to the minimal augmentation case in InDI (Delbracio & Milanfar 2023). and when op = 1. it matches the training strategy of SR3 (Saharia et al. 2022).
Given this noise-augmented formulation, we can now define our training objective as:
Laow (0) Exirpa [/"" ID (OVv(XXARR) 0), xo) di
)ar)
(5)
where D is a discrepancy loss that measures the difference between two images (e.g.. lz loss or the & loss), Va our velocity model, X,,LR concat(x,, xLR) ) the concatenation of x, and XLR in channel dimension (see Fig. [}. The LR input of the algorithm given by X H²(((x))) where H. is the downsampling operator, H is its transpose and n is i.i.d. Gaussian noise with variance of The perturbed version of XLR. denoted as Xg. is obtained using the noise augmentation strategy described in Eq. (4). Additionally, X, = (1 t)xg + tx, denotes the intermediate state as
n),
rectified flow (Liu et al. 2022 Liu 2022).
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3.2 DISTILLATION LOsS
We introduce distillation loss to train a one-step student that preserves the pre-trained SR flow's Mheelit-reealisn trade-off, allowing control inference via single hyperparameter As shown in Fig. [ and observed in prior work (Delbracio & Milanfar 2023] Liu et al. 2023a), single-step estimates of the final state x obtained from an intermediate state x lie on a fidelity-realism curve: along the ODE sampling trajectory. estimates for larger (closer to 1) exhibit richer detail and lower LPIPS (better realism), whereas estimates for smaller (closer to 0) are blurrier but achieve lower MMSE and higher PSNR (better fidelity).
To preserve the ridelity-realism trade-off, given the same imput XD,LR, for two different timesteps t and s where states x and x, that lie on the same ODE trajectory defined by the teacher (see Fig.
require the student model Va to produce two corresponding intermediate
x, = xx + (s - t)
(6)
where Xo.LR concat(Xo- XLR) ) is the concatenation of the input image X and the LR condition XLR along the channel dimension. The intermediate states and x, can be computed using our one-step student model Vg:
X tv (XO.LR, t).
Substituting the expression for the intermediate image x, and x, from Eq. 7 into Eq. (6). we have the following constraint on the student model:
s(Va s) (x t)) = (s (x t) vo (x(,L.x,
(8)
Similar to BOOT, we can set = 8 - and derive the final distillation loss:
Vo((Xo.LR s) SG vo +)+ dt (vo(X),LR, t) -V- (XO,UR t))
Camiu (6) Exi M00.1
where [G is the stop-gradient operator for training stability (Gu et al. 2023
Tee et al. 2024
2024). Similarly to (Song et al. 2023b Gu et al., (023), we can use the Euler or general RK2 solver to calculate ve in Eq. (9). In our main experiments,
Since = df and f > 0, we do not have the 'dividing by O' issue in Tee et al.
employ the midpoint method, while also evaluating two other RK2 solver variants, i.e., Heun's 's method and Ralston's 's method, for comparison in our ablations (see show that distillation loss is the discrete-time counterpart of the forward distillation loss (Boffi et at. (2025) [Lu
Tab. 8. In Sec. B.2]
2025) by fixing the start timestep at O, which is highly related
recent work MeanFlow (Geng al. 2025 and AlignYourFlov (Sabour et al.) 2025).
3.3
ALIGNMENT AND BOUNDARY Loss
In BOOT (Gu et al. 2023). boundary condition is applied to enforce that the one-step student model and teacher model perform the same at the boundary t = 0. We aim to align the teacher and student outputs in model. The student produces X + Va (XQ,LR. f), while the teacher generates
X, +1 - ( LR, LR, t) based on the student's output x, using Eq. (7) By minimizing the difference between these outputs, we get the following alignment loss align the teacher and student:
[|
Lalign (0) Bxxpep
(vo VO(
(10)
If consider this alignment loss only = 0, it becomes equivalent the boundary loss used in BOOT:
VeoXXOLR 0) vo (Xo,LR, 0)
Loc(o) Exyopy
(11)
Since it is difficult to sample = 0 for most training iterations, we add addition the boundary loss Eq. [11 in our final training objective.
The overall training objective. The student network va is trained to minimize the combination of the aforementioned three losses terms:
(()) Lasan()) + Aatign (0) ABcLac()).
(12)
where Aatign and Anc are the weights for alignment loss and boundary condition loss, respectively. The distillation stage of the proposed method is summarized in Algorithm
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Realism
Fidelity
GT
t=
f 0.8
0.6
f 0.4
== 0.2
t= 0
LR 0..38/27.48
LPIPS /
PSNR
2.056/27.60 .6
0.090/28.92
0.120/29.56
0.142/29.88
0.157 /0157/30.02 0.160/30.03
Figure 3: OFTSR is capable to generate continuous transitions between image realism and fidelity.
(1000)
(20)
(100)
(100)
Figure 4: Qualitative comparison with training-free methods. The first row shows noiseless SR on the FFHQ dataset, the second row presents noisy SR (o,, 0.05) FFHQ, and the bottom row demonstrates noiseless SR the ImageNet dataset. Numbers
the method names represent the required NFEs.
Inference. After training, the one-step student Vo produces the final high-resolution output x in single forward pass, conditioned on the initial state Xo, the low-resolutio imput XLR, and the trade-off parameter f. Concretely,
x = xo
va XLR.
(13)
where (-(-) predicts residual that refines x toward the desired point on the fidelity-realism curve specified by
3.4 COMPARISON TO RELATED WORKS
In this section, we distinguish the proposed OFTSR from several closely related methods.
BOOT (Gu et al. 2023). Gu al. proposed make the prediction the student model fulfill the Signal-ODF. In contrast, OFTSR directly constrains the student's implicit prediction using the PF-ODE of the teacher model, leading to more concise and intuitive derivation and distillation ob- jective. Morcover, while BOOT was originally designed for text-to-image generation using diffusion models, our method is built on rectified flow and demonstrates smaller distillation gap compared to BOOT loss for SR task, and empirically achieves markedly better fidelity-realism -realism trade-offs.
DAVI (Lee et al. 2024). Lee et al. introduced DAVI, which combines Variational Score Distillation (VSD) loss (Wang al. 2024d Luo et al. 2023a Yin et al., 2024b) with data consistency loss to train a one-step SR model and utilizes the perturbation trick to present robust restoration ability. However, DAVI needs to train fake score to track the denoising score of the one-step generator, resulting in reduced training efficiency.
SinSR (Wagg al. 2024c). Wang al. proposed SinSR, which achieves near-teacher performance by distilling ResShift (Yue et al.) 2024b) without adversarial training. However, SinSR requires simulation of the teacher model's s ODE trajectory, leading computational overhead during training.
Our OFTSR stands out from other diffusion and flow-based SR methods due to its unique ability to restore images with either high perceptual quality low distortion. This capability is novel among diffusion and flow-base based approaches.
EXPERIMENTS
In this section, we provide experimental details and empirical evaluation of OFTSR and compare with prior works.

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Published as a conference paper at ICLR 2026
Table 1:
Noiseless quantitative results on DIV2K.
Table 2:
Noiseless (top) and noisy (bottom) quantita-
We compute the average PSNR (dB). LPIPS and FID
tive results on FFHQ 256 256.. We compute the av- erage PSNR (dB), LPIPS and FID of different methods
of different methods on 4x SR. The best and sec- ond best results are highlighted in bold and underline.
on 4x SR. The best and second best results are high- lighted in bold and underline.
The distilled model produces superior performance in terms of orade-off metrics through adjustment of the hyperparameter L.
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1100
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150
28.18
0.774
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33.35
0.120
95.75
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100
160
36.00
25.35
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RF5 Chess 1330
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4.1 EXPERIMENTAL SETUP
Datasets. We perform extensive SR experiments on the FFHQ 256 (Karras al. 2019), DIV2K (Agustsson & Timofte| 2017) and ImageNet x 256 (Russakovsky et 2013) datasets to assess the bicubic SR performance of OFTSR on faces and natural images. For each dataset, we eval- uate on 100 hold-out validation images without cherry-picking For evaluating real SR, we use both synthetic set and real world set. Synthetic set includes 100 images from Imagenet for 64-2256 SR and 100 images from DIV2K for 128 -52 SR, both degraded using RealESRGAN pipeline. Real world test set includes RealSR (Cai et al. 2019), RealSet80 (Yue et al. 2024b) and RealL (Ai et al. 2025). For distilling DiT4SR, we construct the training set using combination of images DIV2K (Agustsson & Timofte 2017). DIV8K (Gu et al., 2019). Flickr2K (Timofte et al..
from
2017
LSDIR (Li al. 2023b) and the first 10K images from FFHQ (Karras et al. 2019).
Teacher Models. We employ three types of teacher models in our experiments: (1) Self-trained
teachers (2 types of backbones: Guided Diffusion (Dhariwal & Nichol 2021) for bicubic SR (Tabs. [ to 3 and ResShift (Yue et al. (2024b) for real SR (Tabs. 4 and (5) using the noise-augmented con- ditional flow strategy in Sec. [.1] which showcases the effectiveness of our training scheme; (2) An off-the-shelf DiT4SR teacher (built on Stable Diffusion (SD) 3.5). Since SD-based models possess significantly stronger generative priors and are prohibitively expensive for us pre-train, we distill DiT4SR with our method to enable fair comparison with the latest SOTA approaches for realSR (Tab. (): (3) An off-the-shee ResShift teacher, allowing direct comparison with SinSR (which distilled from ResShift) and fair computational cost comparison (Tabs. 9 and TOP.
Evaluation Metrics. The metrics we use for comparison are Peak Signal-to-Non Noise Ratio (PSNR), Fréchet Inception Distance (FID), and Learned Perceptual Image Patch Similarity (LPIPS) ZZang lt al. 2018) distance. The FID evaluates the visual quality by calculating the feature distance be- tween two image distributions. In our experiments, we calculate the FID using the HR images and the restored images from the 100 hold-out validation set with Clean-FID (Parmar et al. 2022). LPIPS measures the average perceptual similarity between the restored images and their correspond- ing HR images. PSNR measures the restoration faithfulness between two images. And LPIPS and PSNR the two main metrics we use measure the perceptual-fidee task, we also use no-reference Image Quality Assessment (IQA) including NIQR[Zhang et [ (2015). CLIPIQA Wang al. (2023b), MUSIQ Ke et al. (2021), and MANIQA Yang et al. (2022).
trade-offs. For real SR
Compared Methods. We conduct comprehensive comparisons against state-of-the-art diffusion- based image super-resolution methods, which can be categorized into two groups: (1) Training-free methods, including DPS (Chung et al.) 2022), DDRM (Kawar et al.) 2022). DDNM (Wang et al.,
20226), DifiPIR (Zhu et al. 2023). CDDB (Chung et al. [224)), and SITCOM (Alkhouri et al.
2024]: (2) Training-basse based methods: GOUB (Yue et al. 2023), ECDB (Yue et al. 2024a), InDI (Del-
bracio & Milanfar 2023 DAVI (Lee et al. 2024). 12SB (Liu et al.) (023a). DDC (Chen et al.
2024), ResShift Yue et al. 2024b), SinSR (Wang et al. 2024c) and CTMSR Youu et al. 2025). And
large scale Stable Diffusion based methods such as OSEDif (Wu et al. 2024), AddSR (Xie et al.)
2024) and TSDSR (Dong et al. 2025). It is noteworthy that SITCOM requires K inner-iterations to evaluate and differentiate the score function at each sampling step. To further validate the effective- real-world image super-resolution. Following (Yue)
ness of our method, we conduct experiment jet al., 20244b; Wang et al., 2024c), we use Imagenet 256 x 256 as HR training data and synthesize I.R images using degradation pipeline of RealESRGAN (Wang et al. 2021).
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Table 3: Noiseless quantitative results on Im- ageNet 256x256. We compute the average PSNR (dB), LPIPS and FID of different meth- ods on 4x SR. The best and second best results are highlighted in bold and underline.
Table 4: Quantitative results of real-world image super- resolution on ImageNet 256x256 and RealSR (Cai all 2019). The best and second best results are highlighted in bold and underline. The number of inference steps is indicated by 's', which is the same as NFE when not use CFG.
Minhal
PSNK-1)
MANRgA
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33355
104.5
22.34
0.333
207.35
6,0034
0.533
0.554
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Figure 5: Qualitative comparison with training-basee methods. The first two columns demonstrate 4x SR results on the FFHQ dataset with noise level 0.05. The remaining columns show noiseless 4x SR results on the ImageNet dataset. Numbers next the method names represent the required NFEs.
Training Details. We do experiments for both noisy and noiseless SR. For noiseless SR, bicu- bic downsampling performed on all three datasets. For noisy SR, we conduct experiment only on FFHQ 256 dataset with average-pooling downsampling and Gaussian noise with stan- dard deviation Fy = 0.05. All images are normalized to the range of [-1, 1]. For experiments on FFHQ 256 x 256 and DIV2K,
adopt the same model architecture used for FFHQ in (Chung al.) 2022]; and for experiment on ImageNet 256x256 we use the same model architecture as the pre- trained unconditional model used in (Dhariwal & Nichol 2021). We modify the input convolution layer to accept concatenated image input. The first stage models are trained from scratch and are sampled with RK45 sampler by default. The one-step model is initialized from the teacher model for distillation. We use the Adam optimizer with a linear warmup schedule over 1 training steps, followed by learning rate of le for both stages.
4.2 RESULTS
Quantitative Results. We present comprehensive quantitative evalua- tions on several benchmark datasets: DIV2K, FFHQ ImageNet and real world test set and different tasks (including noiseless SR, noisy SR and real world SR) (Tabs. [ to 6). Our analysis reveals several findings: (i) The first-stage OFTSR achieves supe- rior performance in perceptual metrics (FID and LPIPS) while requiring fewer than 32 NFEs. (ii) Our distillation algorithm is versatile, when applied to ResShift (uue et al., 2024b) teacher, distilled model
Table Quantitative comparison on real world sets. The best and second best results are in bold and underline.
NIQE.
MUSIQ
MANIQA
CLIPIQA 1
SwiniR
63.72
0.5919
3,5479
NAP
8.7794
0.3975
0.528
1,3699
ReulSet00
ResShal- 15a
Sin5R-1s
6.1955
56182
61.35
6396
0.5318
05376
0.6722
67242
3.4733
1
Flino Sg
4317
66:00
8.617
08787
1998
Ouss disilled Is 1)
4.1926
67.46
0,5770
0,9904
40366
161628
MealLAq250
ResShar:
SunS Is
5.8200
63.25
0.6990
0.6239
2.9540
3,2578
Dimoe Sp.
4.28.0
65
6.5481
0.830
3,837
Ones detilled 1s 19
4.0731
67.32
0.257
06532
37211
achieved better one-step performance than SinSR (Wang et al., (024c) (see Tab, 99. (ii) Our
distilled version of OFTSR demonstrates remarkable versatility, achieving either the highest PSNR
8

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Published as a conference paper at ICLR 2026
Table Quantitative comparison of state-of-the-ar one-step SR methods on synthetic (DIV2K- Val) and real-world (RealLQ2250) benchmarks. Best results are in bold, second best are underlined. Our ResShift' means we train our noise-augmented conditional flow in Sec. 3.1 jusing the ResShift model architecture then distillation; and DiT4SR means we use the pre- trained DiT4SR model as the teacher model for distillation.
method is tested under
OSEDiff-
(DiT4SR)-Is
PSNR
24.50
24.87
22.39
23.86
22.17
23.91
22.80
SSIM
0.6136
0.6349
0.5682
0.6213
0.5680
0.6073
0.5774
LPIPS
0.3164
0.3011
0.3728
0.2896
0.2679
0.3226
0.2716
DISTS
0.2110
0.2100
0.2357
0.1999
0.1901
0,208
0,1889
DIVIK-Val
FID
131.96
126.49
133.78
100.53
103.49
133.30
98.27
NIQE
6.1721
5.3036
5,9929
4,9741
4.6621
4,9061
4,399
MUSIQ 1
64.26
66.59
63.39
68.53
71.19
68.71
20.25
MANIQA
0.5442
0.5146
0,6010
0.5464
0.6145
CLIPIQA
0.6687
0.6602
0.5734
0.6692
0.7221
0.6545
0.7233
NIQE
5,8200
4.5835
4. 9,9238
3,9656
3.4868
4.0731
3 1,7802
MUSIQ
67.32
RealI.Q250
MANIQA
0.5161
0.5078
0,3304
0.5782
0.5829
0.5287
0.5904
CLIPIQA
0.6990
0.6706
0.6437
0.6725
0.7221
0.6532
0,7252
LIQE
3.3373
3.7211
4.1122
ranking among the top two methods for FID and LPIPS metrics in step. This indicates minimal performance degradation between the teacher and student models. (iv) Our experiments suggest that FID) serves performance gap between teacher and student models during distillation. (v) When applied to
reliable indicator of perceptual quality and better captures the
powerful SD-based SR model (DiT4SR), our distillation algorithm produces one-step generator whose performance is competitive with other leading SOTA distillation methods. This also validates the versatility of our distillation algorithm.
Visual Results. Our experimental results demonstrate that OFTSR achieves high-quality image reconstructions. We evaluate OFTSR against leading training-free methods for 4x SR, as shown in Fig. 4 While DPS can produce sharp reconstructions, it requires 1000 NFEs and often introduces significant distortions. resolution inputs while reconstructing fine details. Notably, distilled version of OFTSR requires only one NFE, as other training-foe free methods suffer from severe error accumulation when using less than 10 NFEs. As illustrated in Fig. 5 we also compare OFTSR against state-of-the-art SR methods that require training. The results show that our approach generates patterns with rich, natural details. Furthermore, distilled model enables flexible control over the fidelity-realism trade-offs in the generated high-resolution images. Fig. demonsttates this capability through examples of noisy 4x SR with varying degrees of realism and fidelity. More qualitative comparison and visual examples can be found in Sec.
contrast, OFTSR successfully preserves structural information from low-
4.3 ABLATIONS
Perturbation Strength oy. In Tab. ? we evaluate the design choices the simple conditional flow training stage. All experiments in this ablation study are conducted under identical training conditions, with performance metrics measured using the RK45 solver. The most critical hyper- parameter in this ablation is the strength of the perturbation op. Consistent with previous works, we confirm that perturbation is essential for generating perceptually compelling images from LR inputs. Notably, we discover that increasing perturbation strength does not necessarily improve perceptual quality but instead leads to more curved PF-ODE, requiring additional NFEs to solve (see Tab. 77. Furthermore, experiments demonstrate that conditioning information loss during perturbation. We also find that & loss outperforms & loss for our specific task. While (Kim et al. 2024) previously highlighted the significance of Gaussian perturbation, our work is the first to systematically analyze the relationship between noise perturbation and the trade-off between generation quality and efficiency in flow-based models.
XLR is crucial to compensate for
Distillation Design Space. In Tab. we evaluate several crucial design choices for the distillation stage, including the distillation loss type, solver type, dt value, and the weighting of alignment and boundary losses. Since learning v (X 0) is considerably easier than learning V (Xo,,. 1), we utilize metrics from the latter decide our distillation hyperparameters. Our analysis of the step size

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Published as a conference paper at ICLR 2026
Table 7: Ablation noiscless FFHQ 256x256 first stage. The default training setting is bs 32; 0.0001; loss type = f1; with condition; all experiments are trained for 100k steps. The
Table Ablation on noiseless FFHQ x56x256 dis- tillation stage. The default training setting is bs 8; op 0.1, 0.0001: loss type lz: with LR dition; all experiments are trained for 20k steps; And the one-step metrics are calculated with = 1. Ablations in subgroups can be ordered as +
final choice is highlighted to balance the perfor- mance and efficiency.
ABC
Aalign
Solver and dt Distillation Loss
Strength Perturbation
NFEs (L)
PSNR (1)
LPIPS (1)
FID (1)
Auge
(1)
LPMPS
110.29
Ous
0.160
0.001
29.56
0.115
48.39
0.076
0.91
20
29.56
0.066
34.70
0.065
28.83
0.053
30.54
Ours
0.063
0.073
30.77
BOoT
2003
0.05
0.483
0.3
32
28.88
0.033
31.02
PINA
Tee
Bnker
0.05
27.92
0:250
0.5
28.86
0053
30.22
Fnler
0.05
29.75
0.064
0.54
31.02
Our:
Fuler
0.05
001
0.1
79.69
0/61
1.
28.82
0.053
30.75
Ours
Haus
0.05
0.01
0.1
29.21
0.087
Our
Raistun
0.05
0.01
0.1
29.15
0.056
0.1 (no cond)
20
28.09
0.073
42.47
Our
Malpoiui
0.05
0.01
011
29.14
0.056
(fs)
Oun (bc:322
Midpeini
0.05
0.01
01
29087
0085
di reveals that smaller values do necessarily yield better results, leading select di 0.05 for subsequent experiments. Our proposed loss function demonstrates substantial improvement over both the original BOOT (Gu al. 2023 loss and PINN (Tee et al., (2024) distillation loss, achieving a significant LPIPS score improvement of more than 0.1. Further experimentation shows that both the alignment loss (Eq. (TO)) and boundary loss (Eq. (TT) contribute to enhanced performance. By combining these losses with Midpoint 2-order solver, we achieve additional improvements in our one-step model's performance t 1.
4.4 COMPUTATIONAL OVERIEAD
Training Cost Comparison. Our distillation algorithm is highly flexible and can be easily applied to any pre-trained diffusionn/foowbassee conditional model. As shown in Tab. 9 we applied our distillation algorithm the ResShift Yue al. 2024b) pre-trained model and achieved teacher- level performance in one step. surpassing SinSR Wang et al. (2024c) in FID with much less training compute. Even taking the training stage into account with a larger model, our method remains more
efficient than ResShift. We use / = 1 for OFTSR.
Table 9: Comparison training cost on single NVIDIA A100.
Method
[NFE(L)]
Iterations
</lter
Training
Time
PSNR (f)
LPIPS (L)
FID (L)
DDC (Chen et al
2024
(base model frozen) [5]
160k
0.89
1.66 days
24.67
0.156
62.06
ResShilf Yoc
2024b
(teacher)
500k
1.32
77664 days
23.68
0.207
60.75
SinSR Wang et al.
2024c
(ResShift teacher) [1]
30k
7.41
-255 days
22.25
0,207
94,90
OFTSR (ResShill Teacher]
[]]
days
OFTSR (prestrain+distill)
[I]
100k
50k
3.9/4.4
44,5142.54 days
24.20
0.135
$2.69
Inference Cost Comparison. We have included detailed comparison of the inference cost in Tab. 10
using FLOPS and MAC to measure model complexity. We use t = 1 for OFTSR.
Table 10:
Comparison of inference cost single NVIDIA A100.
Method
[NFE(U)
Params (+VAF)
(1)
MACS
(+VAE) (L)
Runtime
DDNM Wang et 2022b
[100]
552.8M
84,98
L.ITT
2.24T
7.00s
DDC Chen et al. 2024
[5]
552.8+ 11.7M
62.06
L.140.24T
2.24+0.49T
0.74s
ResShift Yue et nl.
2024b
[4]
118.6455, 3M
60.75
50.1+473.5G
100 5G
0.27s
SinSR WWng
2124e
118.6+55.3M
50.1+473.5G
100.4+948.5E
0.09s
OFTSK (KesShitt teacher)
[1]
118. 645.6455.3M
60.64
50.1+473.5G
100.4.98..50 5G
0.09%
OFTSR (pre-triee trati-iiiiii
552.8M [1]
52.69
1.ITT
2.24T
0.21s
CONCLUSION
In this paper, we introduced OFTSR, novel approach to developing efficient one-step image super- resolution models. Our extensive experiments on FFHQ, DIV2K, ImageNet and real world SR datasets demonstrate that method significantly improves computational efficiency while main- taining high-quality image restoration capabilities. The proposed framework represents a promising direction in efficient image SR, effectively addressing the perception-diisortion trade-off.
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Published as a conference paper at ICLR 2026
ACKNOWLEDGEMENTS
Acknowledgments: This work was supported by the National Natural Science Foundation of China (Grant No. 62572234), the Gusu Innovation and Entrepreneurship Leading Talent Program (Grant No. ZXL20254324), and the Suzhou Key Technologies Project (Grant No. SGY2023136).
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Appendix for OFTSR
A USE OF LARGE LANGUAGE MODELS
We used large language models solely for text polishing and grammar correction during manuscript preparation. No LLMs were involved in the conception design of the method, experiments. or analysis. All technical content, results, and conclusions have been independently verified and validated by the authors.
B RELEVANT DERIVATIONS TO OUR DISTILLATION LOss
B.1 CONTINUOUS VERSION OF THE FINAL Loss
We provide detailed derivation distillation loss used in the paper. By substitute intermediate results X, and X, from student model Eq. 7 into the ODE induced by teacher model Eq. (8), we have:
X ( s)
=x + tv tV t) + (s t) t)
S 5) vo (XO,RRR)) t))
=(t s- t) + t)vo(XX,RR,t) (14) -dr(vo(x..RR.)) t) Val (XO,RR f)).
Start from this constraint that applies the student model, we can construct distillation loss in different forms. (i) In the same spirit as BOOT (Gu et al., 2003). we make only Vo (xo,,, s) and this will lead to loss Eq. (12). (ii) only detach the teacher output, we will end up with loss similar to PINN based distillation PID proposed in (Tee et al. 2024):
CyIN(() = Ex--pp
vo((X.LR, 8) - -v((x018.1) + vo(XO,1R;
0) - SG (o[vo(xx,R.. t)]
(15)
Both Eqs. and 15 are loss variants from Eq. and we did not try other variant given the already-goom good performance of Eq. (12).
In addition, by considering the intermediate interpolation x, = (1 t)x; ix, special case of X; 0: + (xXi in BOOT (Gu et al., 2023), we can derive the following distillation loss:
(6) = Exiepi.000000
X (XU.LR, 8) SG [x. + + Xxxo(xxLR. 1) X (X0,LR, (0)
X
(16)
where À = 1 ((1-8)
s(1-t) X (X0.LRR = xo + (X0.LR. t), X (Xo,LRR 8) Xo + V (Xo.LR s). and
Xo (X,,,RRR, t) = + (1 - (Xr,,R.. with Xo + iv (VV.XX..RR t). We compared our proposed
loss Eq. (1) with its variant Eq. (15) and Eq. (16) in Tab. and our ablation shows that Eq. (2)
works best for SR task.
B.2 OFTSR AS FORWARD DISTILLATION
The general form of our OFTSR loss or BOOT (uu et al.
2023) loss can be seen as a special case of forward distillation (Boffi al. 2025 Sabour al. 2025 Liu 2025). Start from general relation:
X, (s l) (x,, 1,8)
X₂-
(17)
where V. (x,, Vx t, s) is the mean velocity defined on the time interval [t. s] as defined in MeanFlow (Geng al. 2025).
The MeanFlow loss can be derived directly by taking derivative w.r.t. of Eq. (7]. which is also named backward distillation loss. Similarly, when taking derivative w.r.t. 8, the end timestep of the interval, of Eq. (17). we get the forward distillation loss.
For OFTSR loss, if we consider mapping from arbitrary start timestep i two close end timestep 81 and 82, and connecting the corresponding two state xs, and Xay, we have:
x + (32 t) tx,
=X + (s1 (X $1) + (s2 $1 (1)vo(X,1
-52 - t)(v (xe, t, 82) - vo (x t, $1))
= =(s2 - (1) (Xe, 81) tx),
(18)
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For 82 51 ds and limds- +0. have 81 82 and:
V Xx. L, 8) ve(x,, 8) vo (x,,
(19)
ds
which recovers the forward distillation loss as the time derivative w.r.t. 8 of Eq. (17). Thus we can view the OFTSR loss and BOOT (Gu et al., 2023) loss as discretization of the forward distillation loss. And is easy to verify that the signal ODE in BOOT is equivalent to our distillation loss the flow schedule.
C LIMITATIONS
While our method advances one-step image super-resolution, limitations include performance con- strained by teacher model capabilities. Future work will incorporate ground-truth supervision through regression loss or adversarial training.
D DIFFUSION AND
TRADE-OFF
In practice, we found that our distilled model is slightly off the perception-distortion frontier of the teacher model, displayed in Fig- 7 To be specific, the corresponding timestep shifts bit but for same MMSE value the first-stage model and distilled model have very close LPIPS value. This might be caused by the error from large step size dt used in practice and leave this for future investigation.
0.0012
0,125
0.0012
MMSE
MMSE
0.0010
LPIPS
0.100
MMSE
MMSE
LPIPS
0.075
0.0010
LPGS
0.10
0.0008
0.0008
0.0
0.2
0.4
0.6
0.8
1.0
0.05
(1)
0.0
0.2
0.4
0.6
0.8
1.0
Figure 6: Metrics evaluation of estimated x across different timesteps f. During sampling, at each timestep f, estimate the final image using the current model prediction Ve (X1,1R, t) and state x, via xf = X + (1 2)v t). Both MMSE and LPIPS metrics are averaged over 100 sampling pro- cesses. We present MMSE instead of PSNR for better visual effect.
Time
Figure 7: Metrics evaluation of estimated x across different timesteps t for both teacher model and dis- tilled step model. The teacher model the same as the one in Fig. 6 We present MMSE instead of PSNR for better visual effect.
E MORE EXPERIMENTAL DETAILS
The training of all networks across both stages smoothed using Exponential Moving Average (EMA) with a ratio of 0.9999. For FFHQ and ImageNet datasets, images are resized to 256 pixels with center cropping, while DIV2K training employs random crops of 256x256 patches. Data augmentation consists of horizontal flips with 50% probability and vertical flips with 6% probability throughout all experiments. For FFHQ noiseless experiment, we use default perturbation std op 0.1; for FFHQ noisy experiment, we use a higher perturbation std σ, 0.5 to cover the resized noise from LR images, as suggested in Tab. [1 for both DIV2K and ImageNet we use op = 0.2. For training, we employed three widely-used datasets: the standard ImageNet training set (1.28M images), the DIV2K training set (800 2K resolution images), and a subset of FFHQ consisting of the first 60,000 images from the dataset. All models are trained until convergence or up to 300k training iterations and select the model based on best metries. We train the model with uniform loss weight on t. In the distillation stage, we sample the timestep using ~ [tmin fmax] with Emin 0.01 and fmax 0.99 in practice.
For DIV2K evaluation, we first segment the large 2K resolution images into 256 x 256 patches for model inference, then reconstruct the final image by combining the restored patches. To ensure fair
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Algorithm OFTSR Distillation
Require: teacher flow ve, dataset DuR. On, df, w(t) Initialize the step student Vo with the weights of Va
1:
repeat
Randomly sample x1 DIR
4: Randomly sample n ~ (0, I); ~ N(0, Tp
Compute XLK 71" (7((x:) + n) LR condition
6:
Compute
1 0 T111p
7:
Sample E 24(0. 1] and =
Generate velocities V. (Xo,RR, and Vo
9:
Calculate XL.L Xg + tV (Xo.LR, t) and generate velocity va (X t) by teacher model
10:
Compute Laim with Eq. (9) and Latign with Eq. (0)]
11:
Generate velocities (R,,LR, 0) and vo (x0,LRR UJ and compute Lae with Eq.
12:
Compute L(Φ) Luisail (+) + Calign (0) CBC
13:
Optimize & with gradient-hasee optimizer using Vo
14:
until (()) converges
15:
Return one-step flow V
Table 11: Ablation on FFHQ 256x256 first stage
with noisy SR: default: bs 32; Ir 0.0001; & loss; with LR condition.
Streightmess
NFEs (1)
PSNR (1)
LPIPS (1)
0.
20
25.23
0.319
0.0
0.2
0.4
0.6
0.8
1.0
0.1
20
24.09
0.158
Perturbation dp
0.3
24.14
0.154
0.5
32
24.10
0.154
Figure Straightness of conditional with dif- ferent perturbation strength dp..
1. 44
24.22
0.153
comparison, all generated SR images are stored a dedicated separated folder with consistent file names across all evaluated methods, followed by metrie calculation against the HR folder using evaluation script. LPIPS scores are computed using the 'alex' model architecture. All experiments conducted using 4 NVIDIA H800 GPUs.
The straightness of the learned flow v can be calculated with:
S(v) = S. [liv(x,,t - (x1 - -x,)]l x=') dt,
(20)
We also measured the FID among 50k imagenet validation set and the result FID is 2.458 comparing to 2.8 from 12SB.
F ADDITIONAL EXPERIMENTS
We evaluated our first-stage training on the FFHQ 256x256 dataset using σp = 1 without condition- ing, effectively training unconditional generative model for human faces. For this experiment, we do not use any data augmentation. Our evaluation consists of generating 1 images from random noise using the RK45 sampler (with ODE tolerance of Ie-3) and comparing them against the full training dataset of 70k images (we train our unconditional generative flow with the whole dataset). Initial experiments with loss yielded FID score of 41.042 with an average of 56 NFEs. which falls short of the previous state-of-ther P2 model's 's score of 28. 139 (Choi et al. (002). However, switching to la loss for standard rectified flow training significantly improved performance, achiev- ing a FID of 24.577 with only 44 NFEs on average. The model architecture used in our experiment the same as the one used in P2. We leave further investigation to this discrepancy between & and & for image generation and restoration as future works. To facilitate a direct comparison with P2's best reported results (FID scores of 6.92 and 6.97 with 1,000 and 500 NFEs respectively (Choi generated 50k samples using Eg loss-trained model. Our approach achieved
et al. 2022)), superior FID score of 5,871 with substantially fewer NFEs (44), demonstrating the effectiveness of rectified flow. Representative non-cherry-picte samples from model are presented in Fig.
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LR
Teacher
Student
LR
Teacher
Student
Figure 9: Visual results for 8x (left) and 4x (right) SR from resolution 64 to 512 and 128 to 512 respectively.
As our distillation technique is designed for image restoration tasks, we skip the distillation of this unconditional generation flow.
G ADDITIONAL RESULTS
G.1 STRAIGHTNESS VS PERTURBATION STRENGTH
In Fig. 8 we validate the observation in Sec. 4.3 by also measuring the straightness of conditional flows. We observe that for SR task, the straightness is related the noise perturbation added the initial distribution, and straighter flow does not lead to better performance.
G.2 TRAINING DATASETS
In both stages of our approach, we utilize the same dataset. The following table shows comparable performance across different datasets for distillation with FFHQ teacher.
Table 12:
Comparison distilling FFHQ OFTSR teacher on FFHQ Celeba HQ dataset.
Distillation Dataset
Hyper parameter
PSNR (t)
I.PIPS (L)
FTD (.)
FFHQ
28.98
0.055
36.02
FFHQ OFTSR Teacher
Celeba-HQ
29.75
0.056
41.25
G.3 DIFFERENT RESOLUTION AND SCALE FACTOR (SF)
In this work, by default we follow previous works to use the setup of 4x SR at 256 x 256. We also test SF 256 256 and SF 4&c8 512-resolution FFHQ, the results shown in Tab. 13
All models are trained for 30k iterations (bs 32) and distilled for 10k iterations (bs 16). We visualize 8x and 4x reconstruction of teacher and student Fig.
Table 13: A comparison the models trained across different resolution and scale factor.
Metood
Target Resolation
Scale Factor
NFE (
PSNR
LPIPS (U)
FI)
DDNM Wang of 20226
256
100
25.65
0.178
104.47
OFTSR
256
(1)
(25.8))
0.2110.166)
(2.83
SR3
OFTSR (distilles)
512
(2)
27.31
(28.12)
0.151 (0.153)
42.20
OFTSR (isiilled)
512
(2)
(31.30)
0.73 (0.072)
13.21 (1395)
H FAILURE CASE
We show visualization extreme (boundary t and out of distribution t) in Fig. 10
Results of t
ranges from [0 do not show failure case, while (ill-defined) OOD t, especially < 0 fails.
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LR
GT
1=
0.5
0
1=1
2
Figure 10: Visual results of boundary (0 and 1) and out of distribution / (-0.5 and 2)
I RECONSTRUCTION DIVERSITY
The noise- augmented initialization (Sec. 3.1) introduces stochasticity that enables multiple diverse HR reconstructions for the same LRR input. Both the teacher and student model can give different restorations of LR image under different random seeds, and the visualization is shown in Fig. 11
J THE CHOICE OF t
The parameter controls the fidelity-realism trade-off and is inherently guided by user prefer- ence: favors maximal fidelity, while & 1 emphasizes realism. In our experiments, the ridelity-realism parameter t is not highly sensitive to the dataset or degradation type: its effective range stays consistent. When target domain or evaluation metric specified, automatically by optimizing it small validation set to best satisfy the desired objective or target. This enables both user-driven and metric-driven control of the fidelity-realism balance.
also be chosen
K ADDITIONAL VISUAL SAMPLES AND COMPARISONS
In this section, we present additional visual results that demonstrate our method's capabilities. Fig. [3 showcases multiple examples illustrating the tunable fidelity-realimm trade-offs achieved on the FFHQ dataset. Figs. and provide comprehensive comparisons between our method and ex- isting approaches on FFHQ and ImageNet images, respectively. In Fig. [6] we compare real-world (without synthetic degradation) SR results, under the 128 + 512 SR setting. Fig. [7] we shows OFTSR can perform arbitrary scale SR. Here, the model is trained solely on ImageNet for 64 - 256 SR, demonstrating strong resolution and scale generalization without any retraining. Additionally, in Fig. 18 demonstrate method's performance both real- world SR tasks and Al-generated content enhancement. In Figs. 19 and 20 we compare visually our OFTSR (DiT4SR) with other generated with
SOTA method for one-step large resolution SR. Results from Figs. 14 our distilled one-step model unless otherwise specified.
15 and 18
L DISCUSSION OF ACCELERATED I2SB METHODS
We provide here detailed discussion of recent accelerated variants of I2SB and clarify their rela- tionship to our approach.
13SB (Wang et al. 2025). 13SB introduces an improved sampling algorithm for pretrained I2SB models, analogous to DDIM for DDPM. While it yields faster sampling and moderately better
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sults, it retains the fundamental behavior of the original I2SB: multi-stey sampling is required to achieve high perceptual realism, whereas a single step primarily preserves fidelity. In contrast, our distillation framework produces one-step model that attains much stronger realism while also abling a controllable fidelity-realimm trade-off.
CDBM (He et al. 2024). CDBM proposes consistency bridge training and consistency bridge distillation for diffusion bridge models, mirroring the cunsistency-trainiin paradigm used in con- sistency models. However, translation tasks (e.g., Edges > Handbags, DIODE-Outdoor) and ImageNet inpainting. Since no SR evaluation or open-source implementation provided, direct comparison in our setting is not feasible.
experimental scope is limited to relatively small image-to-image
IBMD Gushhhhn et al., 2025). IBMD introduces a distributional matching algorithm for con- ditional bridge models, conceptually related DMD (Yin al., 2024b) for continuous diffusion models. The method requires learning an additional auxiliary network, which increases compu- tational overhead and can introduce training instability. Morcover, reported results show that the one-step performance of IBMD is comparable to 12SB with 1000 NFEs, suggesting limited advan- tages for efficient one-step SR. Therefore, our distilled model remains competitive or superior the one-step regime.
Overall, while these works explore acceleration distillation within the I2SB family, they dif- fer substantially in objectives, model scope, and applicability to super-resolution. Our approach provides a reproducible and effective one-step SR framework with controllable ridelity-realism be- havior not addressed in prior I2SB variants.
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Ground Truth
Sample 1
Sample 2
Sample 3
LR
Sample 4
Sample 5
Sample 6
Ground Truth
Sample
Sample 2
Sample
LR
Sample
Sample
Sample
Ground Truth
Sample
Sample
Sample 3
LR
Sample 4
Sample 5
Sample 6
Figure 11: Our method maintain diversity in outputs. The resolution of ground truth image is 512 x 512 and the x first group generated by teacher model, while the remaining two groups are produced by the student model.
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Figure 12: Random generated samples from unconditional model trained FFHQ dataset.
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GT
t=1
t= 0.8
t= 0.6
f 0.4
t= 0
LR
Figure 13: Qualitative results of one-step model with different tunable t.
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Ground Truth Measurement
DPS (1000)
DDNM (100)
Di (100)
SITCOM (20)
Ours (1)
Figure 14: Qualitative comparisons on FFHQ dataset with a. (first four rows) and o. 0.05
(last four rows).
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Ground Truth Measurement DPS (1000)
DDRM (100) DifIPIR (100) SITCOM (20) Ours (1)
age
Mcasurement
Ours (26)
Ours (1)
Measurement
Ours (26)
Ours (1)
OTEL
ROTEL
HOTEL
unioe
Jene
pown
Penn
Figure 15: Qualitative comparisons on ImageNet dataset for noiseless 4x SR.
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LR
Zoomed LR
ResShift (15)
SinSR
Ours (15)
Ours (1)
Figure 16: Qualitative comparisons for real-worke 4x SR
GB
G
G
PORD
POOD
heesecake Factory
Cheesecake Factory
YE
TE
EE
LATEE
LENE
ANTHE
ANTHE
PACEE
PACIE
LR
OFTSR (1)
Figure 17: Visual result of restoring arbitrary scale image.
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Zoomed LR
Ours (1)
Zoomed LR
Ours (1)
W WONDER
W HONDER
THE WONDER TIME OF THE YEAR
THE WONDER FUL TIME
OF THE YEAR
Zoomed L.R
Ours (1)
Figure 18: Qualitative results on real data and Al generated content using our 4x SR model trained on DIV2K
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LR
CTMSR
OSEDif
AddSR
TSDSR
OFTSR(DIT4SR)
LR
CTMSR
OSEDiff
AddSR
TSDSR
OFTSR(DiT4SR)
Figure 19: Qualitative comparisons for real-world 4x SR. OFTSR is distilled from DIT4SR. All methods perform step inference.
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LR
CTMSR
OSEDif
AddSR
TSDSR
OFTSR(DIT4SR)
CTMSR
OSEDiff
AddSR
TSDSR
(FTTR(D)
Figure 20: Qualitative comparisons for real-world 4x SR. OFTSR is distilled from DIT4SR. All methods perform step inference.
30


Reviews:
[{"review_id": "ZmKjQ1gTFj", "reviewer": "Reviewer_cMY2", "summary": "The paper introduces OFTSR, a conditional rectified flow framework for one-step image super-resolution that allows continuous control between fidelity and realism. The method consists of two stages: a noise-augmented conditional flow trained with a Variance-Preserving (VP) perturbation of the low-resolution (LR) input to enhance diversity and coverage of the HR distribution, and a distillation stage that aligns the student's single-step predictions with the teacher's probability flow ODE trajectory using a combination of distillation, alignment, and boundary losses. OFTSR demonstrates flexible, single-step inference and achieves strong quantitative and qualitative results on diverse datasets showing competitive or superior performance to baselines with significant gains in efficiency.", "questions": "1. Clarification on Table 7: Could the authors explain the distinction between \"OFTSR (ResShift teacher)\" and \"OFTSR (pre-train + distill)\"?\n\n2. Clarification on Table 8:\nTwo OFTSR rows are listed with identical NFEs but differing parameter counts (118.6M+55.3M vs 552.8M). Do these correspond to different architectures (e.g., ResShift-based vs standalone OFTSR) or to inclusion/exclusion of VAE components?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Tunable one step SR with explicit control.\nThe paper introduces an interpretable t-controlled mechanism that enables explicit adjustment of the fidelity-realism trade-off along the teacher’s ODE trajectory.\n\n\n2. Efficiency and applicability.\nThe method requires only a single forward pass at inference while retaining flexibility typically reserved for multi-step models.\n\n3. Strong empirical performance.\nEvaluations span three datasets (DIV2K, FFHQ, ImageNet) and include noisy, noiseless, and real-world SR scenarios. The ablation studies (Tables 5-6) systematically analyze the effects of perturbation strength, solvers, and loss weights, providing solid empirical justification.\n\n4. Clear Methodological Formulation.\nThe paper's derivation of the distillation and alignment losses (Equations 9-12, Algorithm 1) is mathematically consistent and clearly connected to existing distillation methods such as BOOT and forward distillation, offering both clarity and theoretical grounding.", "weaknesses": "1. Boundary of Novelty Relative to Existing Frameworks.\nWhile the paper combines concepts appropriately, its originality is somewhat incremental. \nThe noise-augmented LR conditioning interpolates between SR3 and InDI as acknowledged in Section 3.1. \nSimilarly, the distillation loss in Eq. (9) is a constrained variant of forward distillation (Appendix B.2), \nand the core idea of aligning student and teacher ODE trajectories resembles BOOT. \nThe contribution would be clearer if the authors better emphasized what properties uniquely arise from this specific combination in SR tasks.\n\n\n2. Teacher dependence is acknowledged but under explored empirically. \nSection C mentions that the student's performance is constrained by the teacher's capability, \nbut the experiments (Table 7) explore only a single ResShift teacher. \nThe lack of systematic testing across multiple teachers limits understanding of the method’s robustness \nwhen teacher quality varies or exhibits bias.\n\n\n3. Comparison with one step SR can be expanded. \nThe experimental comparison omits some recent one-step SR approaches [1, 2], \nwhich could contextualize the claimed state-of-the-art results. Including these would strengthen the empirical claims.\n\n\n4. Editorial issues. Minor editorial redundancies exist, such as repeated inference equations 13-14. \n\n[Reference]\n\n[1] TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution, CVPR 2025\n\n[2] One-Step Effective Diffusion Network for Real-World Image Super-Resolution, NeurIPS 2024", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tSJ3ILxwUL", "reviewer": "Reviewer_835P", "summary": "This paper introduces OFTSR, a one-step image super-resolution method that preserves flexible control over the fidelity-realism trade-off. The approach uses a two-stage pipeline: first, training a noise-augmented conditional rectified flow model (teacher) that expands the initial distribution by adding Gaussian noise to low-resolution images, then distilling it into a one-step model (student) by constraining predictions to lie on the same ODE trajectory as the teacher. At inference, users can adjust a single parameter t to generate outputs ranging from high-fidelity/blurry (t=0) to high-realism/sharp (t=1) in just one forward pass, achieving state-of-the-art performance on FFHQ, DIV2K, and ImageNet datasets while being significantly more efficient than multi-step diffusion methods.", "questions": "LPIPS is a perceptual fidelity metric, while PSNR is a fidelity metric. In Figure 3, LPIPS and PNSR were used to demonstrate the transition between image realism and fidelity. A realism metric is preferred, e.g., FID, NIQE, etc. Again, as such a trade-off is a selling point, it would be better in the tables (1, 2, 3, 4). The metrics could be organized, e.g., realism metrics are put on the left and fidelity metrics on the right. That will help understand the differences against t values. \n\nThe SR baseline is established on diffusion and flow-based methods. Thus, the significance of the proposed method for general SR applications is unclear.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper's main strength is achieving an unprecedented combination of efficiency and flexibility, delivering tunable fidelity-realism trade-offs in a single forward pass, which no prior one-step method accomplishes. The technical approach is elegant and well-motivated: the noise-augmented conditioning prevents mode collapse while the ODE-trajectory constraint ensures the distilled model inherits the teacher's trade-off properties. The results achieved state-of-the-art performance across multiple benchmarks (FFHQ, DIV2K, ImageNet) while requiring only 1 NFE, making it faster at inference. The method is also applied to different teacher models and various tasks (noiseless SR, noisy SR, real-world SR, and different scale factors). It provides practical value, as a single trained model can serve multiple use cases by simply adjusting the t parameter.", "weaknesses": "The primary weakness is that the distilled model doesn't perfectly preserve the teacher's perception-distortion frontier. As shown in Figure 7, there's a slight shift in the trade-off curve, meaning the same t value doesn't yield identical fidelity-realism balances between teacher and student, likely due to the discrete step size (dt) approximation. The method's performance is fundamentally constrained by the teacher model's capabilities, and the two-stage training adds complexity and computational cost (though it remains more efficient than some alternatives, such as SinSR). The noise augmentation strength is a critical hyperparameter that requires careful tuning per task. The paper lacks a solid theoretical justification for why the proposed distillation loss preserves the trade-off properties or for what guarantees exist. Finally, some design choices (like dt=0.05) appear empirically driven rather than principled, and the ablation studies, while thorough, reveal sensitivity to these hyperparameters.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rxMV0HV167", "reviewer": "Reviewer_U3BC", "summary": "This paper introduces OFTSR, a flow-based one-step super-resolution (SR) framework that builds upon rectified flow models and distillation. The key idea is to train a conditional flow-based SR teacher and distill it into a single-step student whose predictions are constrained to lie on the same probability flow ODE trajectory as the teacher. This constraint allows the one-step model to maintain a tunable fidelity–realism trade-off controlled by a continuous parameter (t). Experiments on FFHQ, DIV2K, and ImageNet show that OFTSR achieves competitive or state-of-the-art performance among one-step methods, while being more efficient than multi-step diffusion-based baselines.\n\nI found this to be a well-executed and empirically strong paper, but the conceptual innovation is somewhat limited. The proposed ODE alignment distillation is a reasonable technical improvement, yet it does not introduce a fundamentally new idea beyond existing consistency and distillation methods. The experimental section is solid, but the lack of theoretical depth and real-world validation makes it fall slightly short of the bar for acceptance. That said, the work is promising and practical, and I could imagine it being accepted after stronger justification of its theoretical grounding and broader validation.", "questions": "1) How sensitive is OFTSR to the specific choice of teacher (e.g., if trained with different flow objectives)?\n2) Can your ODE alignment loss be extended to a few-step regime (e.g., 2–4 NFEs)?\n3) Is the model capable of diverse sampling (multiple HRs per LR) or is it deterministic?\n4) How does performance compare to lightweight CNN/transformer SR models under equal runtime?\n5) Could you provide visual examples showing failure or instability at extreme t values?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper’s formulation is clear, the equations are consistent with prior work in diffusion and flow-based modeling, and the proposed ODE alignment loss is a neat way to adapt teacher–student distillation for conditional SR.\n- The experimental section is impressive in scope. The authors benchmark across three datasets, consider both noisy and noiseless conditions, include training-free and training-based baselines, and conduct detailed ablations on hyperparameters and solvers.\n- The method achieves true one-step inference while preserving control over perceptual vs. fidelity quality, which is a meaningful step forward for efficient SR.\n- The paper is well-written and easy to follow, with intuitive figures that make the underlying intuition accessible.", "weaknesses": "- Conceptually, the work is an incremental extension of existing distillation or consistency-based methods (e.g., BOOT, MeanFlow, Consistency Models). The ODE-alignment constraint is interesting but feels more like a technical refinement than a new paradigm. This limits the paper’s theoretical depth.\n- The ODE alignment loss is mostly empirically motivated. There’s no clear analysis of when or why this constraint ensures better one-step consistency, nor how it relates formally to the underlying PF-ODE or optimal transport formulations.\n- Although the experiments are extensive, most are performed under controlled synthetic conditions. The “real-world” SR experiments use degradations simulated with RealESRGAN, which doesn’t fully validate real-world robustness. I would have liked to see more genuinely real test data or domain-shift scenarios.\n- All baselines are diffusion/flow-based. The paper does not compare against any deterministic SR networks (e.g., SwinIR, NAFNet), which are used as efficiency benchmark in practice.\n- The paper doesn’t discuss whether OFTSR maintains diversity in outputs (e.g., multiple plausible HR reconstructions per LR input) or generalizes across scales/resolutions. These are important in assessing flow-based generative SR methods.\n- While the interpolation parameter t is central to the paper, there’s little analysis of its sensitivity or failure modes. Some visualizations (e.g., at extreme t) would help validate robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RoIFJtHF4j", "reviewer": "Reviewer_dEo3", "summary": "The paper introduces OFTSR, a novel one-step image super-resolution (SR) method using a flow-based framework that incorporates tunable fidelity-realism trade-offs. It leverages a teacher-student distillation approach, with a noise-augmented conditional flow model for SR. The student model is trained through a distillation strategy, aligning its predictions along the probability flow ordinary differential equation (PF-ODE) trajectory of the teacher. The method is demonstrated to achieve state-of-the-art performance for one-step SR on several datasets like FFHQ, DIV2K, and ImageNet. A unique feature of OFTSR is its ability to adjust the fidelity-realism trade-off via a single hyperparameter.", "questions": "How sensitive is the fidelity–realism control parameter $t$ to different datasets or degradation types? Can it be automatically optimized for a given target domain?\n\n---\n\nIf the above weaknesses and questions are addressed, I would consider increasing my rating.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed OFTSR integrates flow modeling with distillation, achieving high-quality super-resolution in a single inference step. It reduces inference cost by over an order of magnitude compared to diffusion-based methods, while maintaining comparable perceptual quality.\n\n2. The approach’s ability to control the fidelity-realism trade-off through a single hyperparameter adds significant flexibility. OFTSR balances the perceptual quality and fidelity of SR results.\n\n3. The training cost is less than previous methods, and the method seems to be a general and extensible solution for fast generative restoration.", "weaknesses": "1. The paper does not compare with recent state-of-the-art methods such as OSEDiff [1], AddSR [2], CTMSR[3], and TSDSR [4]. This omission makes it difficult to fully evaluate the competitiveness of the proposed method in the context of the latest advances in this field.\n\n2. The experiments are mainly conducted at a resolution of 256×256, without investigating the model’s generalization capability at higher resolutions.\n\n3. The paper lacks an ablation study on SFT a one-step model initialized from the teacher model. I suggest the authors include this experiment to better demonstrate the effectiveness of the proposed method.\n\n[1] One-Step Effective Diffusion Network for Real-World Image Super-Resolution. NeurlPS2024\n\n[2] AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation.\n\n[3] Consistency Trajectory Matching for One-Step Generative Super-Resolution. ICCV 2025\n\n[4] TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution. CVPR 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dpJ0SkqfXC", "reviewer": "Reviewer_vEMX", "summary": "The authors consider the super-resolution problem and propose a method to distill a pretrained flow model in a generator, which produces output on the PF-ODE trajectory for a given time “t” in 1 step. The ability to produce output at any “t” on the ODE trajectory provides a way to control the fidelity-realism trade-off, since for a bigger time “t”, samples are better in “realism” while for less time “t”, samples are better in “fidelity”. The authors evaluate their method on noisy and noiseless SR setups, and conduct some evaluations on real SR setups.", "questions": "Is there a way to automatically choose a time for the generator?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed method provides a clear and simple method to control the fidelity-realism tradeoff by choosing the time parameter “t” in the generator.\n- The distilled model works in 1 step without significant degradation of the quality of generation.", "weaknesses": "- The proposed method is not theoretically novel in the sense that it is a special case of the general forward-distillation framework.\n- Most of the evaluations and comparisons are done with simple downsampling or downsampling plus the addition of noise setups. For RealSR, only non-reference metrics are provided. For the ImageNet comparison, only one competitor distillation method (SinSR) is considered, while other competitive methods like OSEdiff are omitted.\n- The authors compare with I2SB, but not with its various accelerated I2SB versions [1, 2, 3], which is strange since the proposed method is also distillation.\n\n[1] Wang Y. et al. Implicit Image-to-Image Schrödinger Bridge for image restoration //Pattern Recognition. – 2025. – Т. 165. – С. 111627.\n\n[2] He G. et al. Consistency diffusion bridge models //Advances in Neural Information Processing Systems. – 2024. – Т. 37. – С. 23516-23548.\n\n[3] Gushchin N. et al. Inverse Bridge Matching Distillation //arXiv preprint arXiv:2502.01362. – 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)