One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
Authors: Jianze Li
Jiezhang Cao
Yong Guo
Wenbo Li
Yulun Zhang
Venue: ICML 2025 poster (2025)
Keywords: One step Diffusion
FLUX.1-dev
flow matching models
Image Super Resolution
Primary area: applications->computer_vision

Abstract: Diffusion models (DMs) have significantly advanced the development of real-world image super-resolution (Real-ISR), but the computational cost of multi-step diffusion models limits their application. One-step diffusion models generate high-quality images in a one sampling step, greatly reducing computational overhead and inference latency. However, most existing one-step diffusion methods are constrained by the performance of the teacher model, where poor teacher performance results in image artifacts. To address this limitation, we propose FluxSR, a novel one-step diffusion Real-ISR technique based on flow matching models. We use the state-of-the-art diffusion model FLUX.1-dev as both the teacher model and the base model. First, we introduce Flow Trajectory Distillation (FTD) to distill a multi-step flow matching model into a one-step Real-ISR. Second, to improve image realism and address high-frequency artifact issues in generated images, we propose TV-LPIPS as a perceptual loss and introduce Attention Diversification Loss (ADL) as a regularization term to reduce token similarity in transformer, thereby eliminating high-frequency artifacts. Comprehensive experiments demonstrate that our method outperforms existing one-step diffusion-based Real-ISR methods. The code and model will be released at \url{https://github.com/JianzeLi-114/FluxSR}.

One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
Jianze Li
Jiezhang Cao *2
Yong Guo
Wenbo Li Yulun Zhang?
I.R
SinSR.s1
OSEDif s1
AdaSR
TSD-SR-sl
FluxSR-s (ours)
N
I.R
DiABIR -50
SeeSR-s50
ResShift-s15
AddSR s4
FluxSR-sl (ours)
Comparison between FluxSR and state-of-the-an one-step diffusion methods. Bottom: Comparison between FluxSR and state-of-the-art multi-step diffusion methods. Our proposed FluxSR generates more realistic images with high-frequency details.
Figure 1: Visual comparisons of different Real-ISR methods. Top:
Abstract
facts. To address this limitation, we propose FluxSR, a novel one-step diffusion Real-ISR tech- nique based on flow matching models. We use the state-of-the-art diffusion model FLUXX. 1-dev both the teacher model and the base model. First, we introduce Flow Trajectory Distillation (FTD) to distill a multi-step flow matching model into one-step Real-ISR. Second, to improve image realism and address high-frequency arti- fact issues in generated images, we propose TV- LPIPS perceptual loss and introduce At- tention Diversification Loss (ADL) regular- ization term to reduce token similarity in trans- former, thereby eliminating high-frequency arti- facts. Comprehensive experiments demonstrate that our method outperforms existing one-step diffusion- based Real-ISR methods. The code and model will be released at https: / /github. com/ homm/iinzeeii-ie 4/FluxSR.
Diffusion models (DMs) have significantly ad- vanced the development of real-world image super-resolution (Real-ISR), but the computa- tional cost of multi-step diffusion models lim- its their application. One-step diffusion mod- els generate high-quality images in a one sam- pling step, greatly reducing computational over- head and inference latency. However, most isting one-step diffusion methods are constrained by the performance of the teacher model, where poor teacher performance results in image arti-
'Equal contribution 'Shanghai Jiao Tong University "Harvard University Huawei Consumer Business Group "Huawei Noah's Ark Lab. Correspondence Yulun Zhang <yu- lun100@ gomi..com>.
Proceedings of the nnnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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One Difflusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
1. Introduction
extremely high or even infeasible when distilling a large student model from additional teacher of at least the same model size. For example, we find that even a server with 8 A800-80GB GPUs cannot satisfy the memory requirement of this distillation if we directly apply the popular one-step distillation method OSEDiff (Wu et al., 2024a) on top of Flux.I-der (Labs, 2023).
Real-world Image Super-Resolution (Real-ISR) (Wang et al., 2020; 2021) aims to recover high-quality images from low-quality ones captured in real-world settings. Tra- ditional image super-resolution (Kim et al., 2016; Zhang et al., 2015; Dong et al., b0;b;; Chen et al., 2023) assumes a known degradation process. However, this assumption does not account for the complex and unknown degrada- tions present in real-world low low-quality images (Wang et al., 2021). Consequently, real-world super-resolutin tasks are more challenging and practical. In recent years, they have attracted increasing attention from researchers.
In this paper, we propose a novel one-step diffusion model for Real-ISR, called FluxSR, with FLUX.1-dev as the base model. Specifically, our design comprises three main com- ponents: 1) We propose a Flow Trajectory Distillation (FTD) to address the generative distribution issue. The key idea is to build the relationship between the noise-to-imane flow in T2I and L--to- flow in SR based on the flow matching theory. Unlike existing methods, we explicitly keep the original T2I flow unchanged while learning the SR flow trajectory conditioned on it. This approach maximizes the preservation of the teacher model's s generative capabilities, thereby enhancing the realism of the generated images. 2) We develop a large model friendly training strategy that does not rely on an extra teacher model to compute the dis- tillation loss. Instead, we cast the knowledge of the teacher model into the noise-to-image flow in the T2I task. In this sense, we able to generate a bunch of flow data in the offine mode and exclude the teacher model from training to save memory consumption. 3) We propose TV-LPIPS as a perceptual loss. By incorporating the idea of total variation (TV), this loss emphasizes the restoration of higu-fryy components and reduces artifacts in the generated images. Moreover, we introduce the Attention Diversification Loss (ADL) (Guo et al., 2023) that improves the diversity of different tokens in attention modules. We use it as regu- larization term to address the repetitive patterns observed in the images. Extensive experiments show that our FluxSR achieves remarkable performance and requires only one sampling step. Figure 1 presents the visual results of our method. In summary, our contributions are as follows:
Diffusion models (Ho et al., 2020; Song et al., 2020) are a type of generative model and initially designed for text- to-image (T2I) tasks. They have shown overwhelming ad- vantages in many computer vision tasks (Rombach et al., 2022a). In recent years, numerous researchers have applied diffusion models to Real-ISR (Wang al., 2024a; Lin et al., 2024; Yang et al., 2024; Yu et al., 2024). These applica- tions have achieved unprecedented quality. These methods leverage the strong priors of pre-trained diffusion models, making the generated images exhibit more realistic details. Very recently, a lot of efforts have been made to investi- gate the scaling law of diffusion models (Henighan et al., 2020; Yu et al., 2024; Tian al., 2024) for image generation. Interestingly, large model, e.g., Flux (Labs, 2023) with 12B parameters, is able to significantly improve the visual quality and reatosse compared to those small diffu- sion models (Rombach et al., 2022b; Podell et al., 2023; Esser et al., 2024) with 1B~3B parameters. Nevertheless, such a large model still requires multiple steps for inference and becomes very computationally expensive, hindering its practical applications. Thus, how to reduce the number of steps to achieve efficient inference based on large diffusion models becomes an important problem.
To address this issue, many one-step distillation meth- ods (Wang et al., 2024b; Wu et al., 2024a; Xie et al., 2024; He et al., 2024; Dong et al., 2024; Zeng et al., 2024) could be useful. But they still suffer from several critical issues, particularly raised by the generative distribution shift is- sue and the training difficulty of very large model. First, fine-tuning well-trained T2I model on SR data may eas- ily destroy the original noise-to-image mapping and thus innur a distribution shift, as shown in Figure 2. Note that recent large diffusion models often follow the flow match- ing strategy (Esser et al., 2024; Labs, 2023) that explicitly learns the flow along the diffusion path. In other words, existing one-step methods may completely ignore the orig- inally well-learned T2I flow when learning the target SR flow. As a result, existing one-step models tend to produce images with unexpected artifacts and degraded visual qual- ity. Second, the memory footprint and training cost become
We develop FluxSR, a one-step diffusion Real-ISR model based on FLUX.I-dev. To the best of our knowl- edge, this is the first one-step diffusion for Real-ISR based on a large model with over 12B parameters.
We propose a Flow Trajectory Distillation (FTD) method that explicitly builds the relationship between the noise-to-image flow and LR-to-HR flow. With the noise-to-ine noise-to-image flow unchanged, we are able to preserve the high choto-realism in the T2I model and effectively transfer it to the -o-to-HHR flow for SR.
To make the training feasible, we propose a large model friendly training strategy that excludes the extra teacher model from the training phase. Instead, we cast the knowledge from teacher into the noise-to-image flow and generate a bunch of them in the offline mode, to reduce both memory consumption and training cost.
2

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One Difflusion Step to Real-Worle Super-Resolution via Flow Trajectory Distillation
2. Related Work
3. Bacckrroudd
2.1. Acceleration of Flow Matching Models
3.1. Flow Matching Models
Liu et al. (2022) proposed the Rectified Flow method, which straightens the flow trajectory to achieve high-quality results within a one sampling step, laying solid theoretical foun- dation for subsequent research. InstaFlow (Liu et al., 2023) applies the Reflow method to straighten the curved ODE solving path, allowing latents to transition more quickly from the noise distribution to the image distribution. The straightened ODE path also reduces the learning difficulty for the student model, improving the distillation effective- ness. This enables one-step generation for large-scale text- to-image tasks, PeRFlow (Yan et al., 2024) further improves Reflow correction by segmenting the flow trajectory, achiev- ing exceptional performance.
Given two data distributions Po and p1. there exists a vector field in that generates a probabilistic path pt transitioning from Po to Pr. In generative models, Po represents the data distribution, while pl is an easily accessible simple distribu- tion, such as the standard normal distribution (0, 1).
Following Esser et al. (2024), we define the forward process as:
Zx = 0, + bie, where E ~ (N 1). (1)
The coefficients a and b, satisfy do 1, bu 0, a1 0, and bj = 1. This defines probabilistic path Pt. from po to PI. The transformed variable is given by:
o ax at
dg
2.2. Diffusion-based Real-ISR
x (xxlee
ebel
(2)
at
b
Multi-step biffusiionbased Real-ISR. In recent years, dif- fusion models have achieved remarkable success in the field of image super-resolution (Wang al., 2024a; Lin al., 2024; Yang et al., 2024; Yue et al., 2024; Wu et al., 2024b; Yu et al., 2024). DifBBIR (Lin et al., 2024) reconstructs low-resolution (LR) images using a small network and then employs ControlNet (Zhang et al., 2023) to control the gen- eration of the diffusion model. SeeSR (Wu et al., 2024b) introduces a module for extracting semantic information from images. This module effectively guides the diffusion model's 's generation through semantic cues, preventing errors caused by image degradation. SUPIR (Yu et al., 2024) uses Restoration-Guidee Sampling to ensure both generative ca- pability and fidelity. It also leverages a large dataset and a large pre-trained diffusion model, SDXL (Podell et al., 2023), to enhance the model's performance.
Subsequently, the marginal vector field ur (xx) is obtained using the conditional vector field (x(rlcc) as follows:
ue(2,,) = - ue (2x(2)))) plaza e ()(()) de.
(3)
(x(
Here, the marginal probability density pr(2+)) is defined by:
Po(#,) = p((xe)))p(e)dd
(4)
Flow matching aims to train a vector field co t), pa- rameterized by a deep neural network, to approximate the marginal vector field uz Specifically, flow matching minimizes the following objective (Lipman et al., 2022):
(o) == E., (x:)) (l (1o () uy (1.))
(5)
One-step Diffusion-based Real-ISR. Recently, one-step diffusion ISR models have become a popular research direc- tion, showing great potential and application value (Wang et al., 2024b; Wu et al., 2024a; Xie et al., 2024; He et al., 2024; Dong et al., 2024). SinSR (Wang et al., 2024b) intro- duces a deterministic sampling method. It fixes the noise- image pair using sessiistencc--rseerviie distillation. OSED- iff (Wu al., 2024a) employs Variational Score Distillation (VSD) (Wang et al., 2024c; Nguyen & Tran, 2024) and directly uses the low-resolution (LR) image as the start- ing point for diffusion inversion. In addition. OSEDiff uses DAPE (Wu et al., 2024b) to extract semantic infor- mation from the LR image as the generation condition. ADDSR (Xie et al., 2024) combines adversarial training by introducing Adversarial Diffusion Distillation (ADD) and ControlNet to achieve both 4-step and one-step models. TSD-SR (Dong et al., 2024) proposes Target Score Distil- lation (TSD) and Distribution Aware Sampling Module (DASM), effectively addressing the issue of artifacts caused by VSD in the early stages of training.
However, the expression for de cannot be explicitly com- puted, making the direct optimization of the aforementioned loss challenging. Lipman et al. (2022) proposed conditional flow matching, demonstrating that we can optimize the fol- lowing equivalent yet more tractable objective by using de (z ()::
LCFM (f) == E,, p.(ed)) [1 t) - u. (2, (6)
3.2. Flow Trajectories
In this paper, we consider the flow trajectory used in I-dv,, namely rectified flow (ReFlow) (Liu et al., 2022). This is simple diffusion trajectory that defines the forward process as a straight path between the data distribu- tion and the noise distribution (Liu al., 2022; Esser et al., 2024), specifically:
St = (1 - -1)00 + te,
(7)
where No ~ Po. e ~ P1 = (0.
3

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One Diffusson Step to Real-World Super- Resolution via Flow Trajectory Distillation
T L
I L
SR Flow
3
SR
U1
T2I Flow
E
20
Shift E H
Utt
0 (
Figure 2: Difference of exiting methods and our Flow Trajectory Distillation. (Left) Based on the pre-trained models from noise to images To, existing one-step diffusion models fine-tune the model from LR images to HR images TH. It may lead to distribution shift between the real data distribution (blue) and the generated distribution (orange). (Right) To bridge the mapping from LR image distribution (green) to real data distribution, we propose Flow Trajectory Distillation. We constrain us R using the other two trajectories in the triangle, ensuring that the real data distribution (blue) does not shift.
By substituting into Equation 2, we obtain the conditional vector field of ReFlow:
the distribution of the vector field u. SR at zy while mod- ifying the distribution of the diffusion starting point (i.e., transitioning from the noise distribution to the LR image distribution as shown in Figure 2) by fine-tuning the T2I model. Therefore, we propose Flow Trajectory Distillation, which indirectly obtains urR by fitting u/', avoiding the shift in the real data distribution.
(e(crele) C
Tzg.
(8)
1
Therefore, following (Lipman al., 2022; Esser et al., 2024), the training objective of ReFlow is
CReFIow (0) [L)= ps ((.le)).
(1
(9)
Approximating the LR Image Distribution. Inspired by DMD (Yin et al., 2024b;a), we can learn the underlying distribution of the training data by training a diffusion model. For flow matching models, training on LR data allows us to obtain parameters vg. which fit the vector field uf that maps the noise distribution to the LR image distribution. The corresponding conditional flow trajectory is given by:
Intuitively, the goal of ReFlow is to train the neural network
(g(re,t) t) to predict the velocity from noise to data samples.
4. Method
4.1. Flow Trajectory Distillation (FTD)
Fe = -1- + le,
(10)
Our goal is to distill one-step diffusion super-resolution model from pre-trained text-to-image (T2I) flow model. Most current one-step diffusion ISR methods directly fine- tune the pre-trained T21 model and incorporate modules such as VSD or GANs to improve performance (Wu et al., 2024a; Xie et al., 2024; Dong et al., 2024). Although these methods have achieved good results, they still face some challenges. As shown the left side of Figure 2, the flow trajectory of the pre-trained T2I model is not aligned with that of the SR model. During fine-tuning, these methods have no mechanism to keep the diffusion endpoint distribu- tion unchanged. In other words, the real data distribution (blue) in the figure shifts, converting to the generated dis- tribution (orange), For large-scale T2I models, which have already fit the real data distribution well, fine-tuning them using the above methods could lead to negative outcomes.
where 31. ~ PL, 6 ~ NO0 1) and & E [0, 1]. The velocity of a sample x, at time is given by
Computing the LR-to-HR Flow from Noise-to-Image Flow. At this point, we have obtained the flow model vs that maps from noise low-resolution (LR) images and the flow model Ureal that maps from noise to real-world high- resolution (HR) images (the pre-trained T2I model). Given the linearity of the ReFlow trajectory, we can easily derive the flow model vo for mapping LR images to HR images. We have:
To = -- Ug, IL c uf.
(11)
Here, Ureal and Us parameterize u, and uf respectively. By combining the above equations, obtain the trajectory from J. to zo:
Ideally, the resulting model serves as a mapping from the low-resolution (LR) image distribution Pr. (green distrubi- tion in Figure 2) to the high-resolution (HR) image distri- bution Po (blue distrubition in Figure 2). We aim to fix
Zo Ft. (14 ut/).
(12)

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One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
Muti-step Generation
T steps iteration
FLUX.1-dev
Random
DVT Block
DIT Block
DIT Block
Decoder
Sample
Book
Zt-1
20
Distill (FTD)
C ADL
Super-Resolution
Encoder
Decoder
FluxSR
L Rec
Noise-Image
pair
L
5 H
C
share weights
generated
offline
1
U4 Vo
LFTD
Tr.
t 21.
Tz 71
Flow Trajectory Distillation
T2
Figure 3: Training framework of FluxSR. (Top) Multi-step inference process of the pre-trained FLUX model. (Middle) Training strategy of FluxSR. (Bottom) Computation process FTD. We distill a one-step super- apper-resolution model from the multi-step FLUX model, without the need the teacher model to involved online during training.
4.2. Large Model Friendly Training Strategy
model. As shown Figure 3, similar to Eq. (11), we have:
Although we have derived the theoretical formulation of FTD, its practical application faces the following challenges: Inference Efficiency: During inference, we need both the vector field u, calculated by the pre-trained T2I model and the vector field uf calculated by the model fine-tuned on LR data. This requires two different flow models with separate parameters, leading to significant computational overhead during inference. iî) Estimation Error: Running the flow model a one step makes it difficult to accurately estimate the velocity at time Without using reconstruction loss to optimize the generator, the model performance may degrade. In this section, propose optimized training strategy to ensure that only one flow model is required during inference. Additionally, we incorporate reconstruction loss to enhance model performance.
30 Utf:
L ---11 (1-Tr)up
(13)
[t ui SRTI.
By combining the above equations, we obtain:
SSRT.
Ut Me
u1
where [Tz, 1].
(14)
1 Tτ
The model parameterization can be expressed as:
Ug. (x,, t) = u, E) - Us, (Ir, t)T2 1 Tí.
(15)
where
t Tr. Ft 1- Tt Fr. I-T, Tí. e, [Tr.).)
1-t
Direct Parameterization of u SR, As shown on the left side of Figure 3, since we can derive u from i, and u/, we also obtain ui from ,, and w,S. This avoids the issue caused by the inability to directly parameterize uR We parameterize uSA using te. To represent both uf and uSR with one model. we define the time step corresponding to the LR image as Tr. instead of O. This ensures that the model represents only uf in the time range [Tr., 1] and only už s at Tr. Additionally, the LR image distribution is more similar the intermediate states He of the pre-trained diffusion
(16)
Generating noise-to-image flow for distillation. We pre- compute noise-sample pairs generated by FLUX and use them as training data, without relying on any real images. This approach offers two crucial benefits for large model training. 1) By using data pairs generated by the teacher model, we can directly compute (e(zzle) = 2o, thus avoiding the estimation error during single-step inference. 2) The teacher model is not required for online inference during training, which significantly reduces GPU usage and
5

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One Diffusion Step to Real-Worke Super-Resolutiin via Flow Trajectory Distillation
Attention Diversification Loss. To address periodic arti- facts at the feature level, we introduce the Attention Diversi- fication Loss (ADL) proposed by Guo et al. (2023), ADL aims to reduce similarity between tokens and enhance atten- tion diversity. We incorporate this loss to prevent different tokens from generating identical feature components.
To reduce computational complexity, ADL first approxi- mates the overall cosine similarity by computing the cosine similarity between each token feature vector A! and the mean of all token feature vectors, defined as:
Figure 4: Examples of Pronounced Periodic Artifacts Dur- ing Training. Left: 256-pixel image with noticeable periodic high-frequency artifacts. Right: 64-pixel zoomed-in region, showing artifacts with four cycles in both width and height.
N
A
A
(22)
N
1=1
training time, especially for large T2I models like FLUX. Using e-prediction, the loss function of FTD is given by:
Here, A! represents the i-th feature vector in the output of the 1-th transformer layer. For model with L layers, ADL computes the mean ADL loss across all layers:
(0) Er, p(e) (I(1 Tu. (e- x2) -Ie,, pe(.)e)) [I(u; - (2 t)) (E (E-xx)) 11) (17)
where u, = c- xn. t [Tz, 1]. The generator Gy can be expressed as:
N
1 A
1 CADL
Σ L
(() ADL
ADL
||4
(1) ||||
A
Go(XL) = zL - vo(IL,
(18)
(23)
In summary, the overall training procedure of FluxSR is presented in Algorithm 1.
4.3. Anti-artifacts Loss Functions.
During training, we observe that the generator's predictions exhibit periodic high-frequency artifacts the pixel space. As shown Figure the artifact period 16 pixels, exactly the product of the VAE scaling factor (8) and the transformer patch size (2). This indicates that each token has similar components in certain dimensions.
Algorithm 1 FluxSR Training Procedure
1: Input: Pre-computed noise-image dataset DD [c, 20}. Pre-trained diffusion model ve and VAE encoder E,,, decoder D Training iterations N.
2: Output: one-step generator Gg.
Init: Up < Us. Eg < E.g, Do + Des Initialize:
Improvement of Perceptual Loss. We aim to reduce varia- tions between adjacent pixels in flat regions to suppress high- frequency artifacts while preserving sharp edges. Inspired by the total variation (TV) loss, we propose TV-LPIPS as the perceptual loss for training. Specifically, TV-LPIPS is computed as follows:
Trainable LoRA mounted on vg.
4: for i == to N do
5: Sample (E, 20) ~ D.
6: U1 < 20
7: FTD Loss:
8: Sample E [Th, 1].
LIVEPINg (I, = CIPIPS (I)
1-t
1. Tí.
(19)
EL + 1- Ti
+ + (TV),
10: Us, (zx, 1) + u - U0 (Ee, 1)TL
where
TI 11: Compute LITD using Eq. (17).
(14-)) -t.l 1 + |1,5+1 - Ta.s
(20)
12:
// Reconstruction Loss:
TV-LPIPS measures the degree of pixel variation and com- putes the LPIPS distance with the ground-trui -ttthh This not only prevents excessive variations between adjacent pixels in smooth regions but also enhances the LPIPS loss's sen- sitivity to high-frequency components. In summary, the reconstruction loss for training is given by:
13:
En + 21. - (v) Tr.)
14:
20 + Do(éo).
15:
Compute LIVAEIes using Eq. (19)
16:
LRSE LuSE + XLTV
17:
11 ADL
18:
Compute LADL using Eq. (23).
19:
L(0) = LFID + Cree + &LADL
CRee(Go(Zz)) aH) LMsE EsseeGo(zLL),. HH)
20:
Update vo using L()).
(21)
+ XLTV (G)))))HH)
21: end for
6

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One Diffusion Step to Real-World Super-Resolutee via Flow Trajectory Distillation
Table 1: Quantitative results (x4) on the Real-ISR testset with ground truth. The best and second-best results are colored red and blue. the one-step diffusion models, the best metric is bolded.
Model
Method
PSNRT
SSIMt
LPIPS1
DISTSŞ
MUSIQT
MANIQAt
TOPIQT
Q-Alignt
StableSR-s200
26.28
0.7733
0.2622
0.1583
60.53
0,3706
0.5036
3,8789
DiftBIR-s00
24.87
0.6486
0.3834
0,2015
68.02
0.5287
0.6618
4.1244
SceSR-s50
26.20
0.7555
0,2806
0.1784
66.37
0,5089
0,6565
3,9862
ResShift-s15
25.45
0.7246
0.3727
0,2344
56. 18
0.3477
0.4420
3.8936
ADDSR-s4
23.15
0.6662
0.3769
0,2353
66.54
0.6094
0,7241
4,1635
RealSR
SinSR-s1
25.83
0.7183
0.3641
0.2193
61.62
0.4255
0.5362
3,9237
OSEDiff-s1
24.57
0.7202
0.3036
0,1808
67.31
0.4775
0.6382
4.0646
ADDSR-s1
25.23
0.7295
0.2990
0,1852
63.08
0.4093
0,5685
3,9806
TSD-SR-s1
23,80
0.6987
0,2874
0,1843
68.31
0,4899
0.6568
4,0926
FluxSR-sI
24.83
0.7175
0.3200
0.1910
68.95
0,5335
0.6699
4,3781
StableSR-s200
23.68
0.6270
0,4167
0,2023
49,51
0.2696
0,3765
3.7427
DifiBR-=s00
22.33
0,5133
0,4681
0,1889
70.07
0,5471
0.6958
4,2666
SccSR-x50
23.21
0.6114
0.3477
0.1706
67.99
0.4687
0.6592
4,4594
ResShift-s15
23.55
0.6023
0.4088
0.2228
56.07
0,3409
0.4580
3.9961
ADDSR-s4
22.08
0.5578
0.4169
0.2145
68.26
0.5496
0.7168
4.3910
DIVlK-val
SinSR-sI
22.35
0,5405
0,4390
0,2033
62.25
0.4241
0,5787
4,1712
OSEDifi-s
23.10
0.6127
0.3447
0.1750
66.62
0.4115
0.5971
4.1366
ADDSR-s1
22.74
0.6007
0.3961
0.1974
62.08
0.3867
0.5817
4.2971
TSD-SR-s1
21.65
0.5546
0.3456
0.1530
68.65
0.4393
0.6415
4.1539
FluxSR-s1
22.30
0.6177
0.3397
0.1634
68.72
0.4615
0.6426
4.6128
Table 2: Quantitative results (x4) on RealSet65 testset. The best and second-best results are colored red and blue. In the one-step diffusion models, the best metric is bolded.
models, including multi-step diffusion ISR models: Sta- bleSR (Wang al., 2024a), DiffBIR (Lin et al., 2024), SecSR (Wu et al., 2024b), ResShift (Yue et al., 2024), and AddSR (Xie et al., 2024); and one-step diffusion ISR mod- els: SinSR (Wang et al., 2024b), OSEDiff (Wu et al., 2024a), and TSD-SR (Dong et al., 2024). We evaluate our model and the aforementioned methods using 4 full-reference metrics: PSNR, SSIM, LIPIS (Zhang et al., 2018), and DISTS (Ding et al., 2020), well 4 no-reference metrics: MUSIQ (Ke et al., 202 1). MANIQA (Yang et al., 2022), TOPIQ (Chen et al., 2024), and Q-Align (Wu et al., 2023). PSNR and SSIM are computed on the channel in the YCbCr space.
Method
MUSIQI
MANIQAT
TOPIQT
Q-Alignt
StableSR-s200
58.89
0.3535
0.4974
3,8093
DistBIR-s50
71.23
0.5682
0.7015
4.1599
SecSR-s50
69.79
0.5030
0.6774
4.1172
ResShift-s15
59.36
0.3622
0.4953
3,8942
ADDSR-s4
68.97
0.5613
0.6971
4.1672
SinSR-sI
64.22
0.4462
0,5947
4.0390
OSEDifi-s!
69.04
0.4625
0.5969
4.1065
ADDSR-
64,22
0.3947
0,5616
4.0806
TSD-SR-s1
69,34
0.4893
0.6392
3,9936
FluxSR-s1
70.75
0.5495
0.6670
4.2134
5.2. Comparison with State-of-the--Arl Methods
5. Experiments
Quantitative Comparisons. Tables and 2 presents quan- titative comparison between FluxSR and other diffusion- based Real-ISR methods. Among one-step methods, our ap- proach achieves the best performance across all no-reference (NR) metries on all test datasets. For FR metrics like PSNR and SSIM, recent studies have demonstrated that image fidelity and perceptual quality involve a trade-off. In the context of diffusion- based super-resolution methods, PSNR and SSIM have limited reference value. Compared to multi-step methods, FluxSR outperforms StableSR across all datasets. Against DiffBIR, SeeSR, and AddSR, FluxSR shows slightly lower performance in TOPIQ. Additionally, we provide further comparisons with non-diffusion-bart methods in the supplementary material.
5.1. Experimental Settings
Training Datasets. Our method does not require any real datasets. We generate 2400 noise-image pairs of size 1024x1022 using FLUUX.1-4 -ddv (Labs, 2023) as training data. To obtain the corresponding low-resolution (LR) images, we use the degradation pipeline proposed by RealESR- GAN (Wang et al., 2021).
We evaluate our model on the synthetic dataset DIV2K-val (Agustsson & Timofte, 2017) and two real datasets: RealSR (Cai et al., 2019) and RealSet65 (Yue et al., 2024). From DIV2K-val, we use the RealESRGAN degradation pipeline to generate corresponding LR images. On the these datasets, we evaluate using full-size images to assess the model's performance in real-world scenarios.
Test Datasets.
Qualitative Comparisons. Figure 5 presents visual compar- ison between FluxSR and other methods. FluxSR is capable of generating realistic details under severe degradation.
Compared Methods and Metrics. We compare the per- formance of our model with other diffusion-basee ISR

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One Diffusion Step to Real-Worke via Flow Trajectory Distillation
LR
StableSR
DiMBIR
SecSRt
ResShift
SinSR
OSFDif
AddSR s
TSD-SR
FluxSR (ours)
better
for hep
years Sac
LR
DimBIR
SocSR
ResShift
AddSR-s4
Collage
Kepp
aammie
AO....
SinSR
OSEDif
AddSR
TSD-SR
FluxSR (ours)
Figure 5: Visual comparisons (x4) on Real-ISR task.
Table 3: Ablation study on FTD.
poor performance, failing to generate high-frequency details and exhibiting significant tigh-frequency artifacts. Using the proposed FTD loss does not disrupt the data distribution learned by the teacher model. It effectively restores high- frequency details and achieves higher degree of realism.
Method
PSNRT
MUSIQI
MANIQAT
Q-Alignt
FTD FTD (ours)
26.33
56.02
0.3775
3,5170
24.67
67.84
0.5203
4.1473
Table Ablation study on different loss functions.
Effectiveness ADL and TV-LPIPS. To verify the ef- fectiveness of ADL and the proposed TV-LPIPS loss, we conducted relevant ablation experiments investigate the impact of each loss function component. We also included the use of EA-DISTS, proposed by DFOSD, as perceptual loss. Table 4 presents the experimental results, showing that using TV-LPIPS as perceptual loss and ADL as a regularization term achieves the best performance.
CLOPS
Crvares CENDISTS CADE
PSNR
MUSIQr MANTQA Q-Alight
23.10
64.55
0.4937
4.0515
22.09
65.04
0.5113
4.0927
/
23.67
64,83
0,5036
4.0003
24.72
24.67
67.84
0,5203
4.1473
For example, the first row of Figure 5, which depicts the restoration of a coat image, DiffBIR, ResShift, and SinSR are affected by noise, resulting in artificial textures. Although AddSR and TSD-SR generate relatively sharp images, they fail to accurately restore the collar's design. In contrast, FluxSR reconstructs the collar in way that closely resembles the real-world appearance. The second row of Figure 5 demonstrates the restoration of numerical digits. FluxSR produces the most realistic result. While TSD-SR also approximately restores the digits, it suffers from Sinc noise, generating bright edges around the numbers.
6. Conclusion and Limitation
This paper proposes FluxSR, an efficient one-step Real-ISR model based on FLUX, the state-of-the-art T2I diffusion model. FluxSR leverages Flow Trajectory Distillation (FTD) to distill a multi-step flow matching model into a one-step super-resolution model. It is trained using noise-image pairs generated by a fixed multi-step model and does not require any real data. We employ TV-LPIPS and ADL enhance high-frequency components in the generated images and reduce periodic artifacts. Our experiments demonstrate that FluxSR achieves unprecedented realism.
5.3. Ablation Study
Limitations. Although FluxSR achieves strong perfor- mance, it has large number of parameters and high com- putational cost. Moreover, we have not entirely eliminated the periodic artifacts mentioned in Section 4.3. In the future, plan apply model pruning techniques compress the model and develop more effective algorithms to pre- vent periodic artifacts, aiming to achieve lightweight yet high-performance Real- -SR model.
In this section, we use RealSR as the test dataset. The training iterations set 30k. Other settings remain consistent with those mentioned in Sec. 5.1.
Effectiveness of FTD loss. To verify the effectiveness and of FTD, we compare with training using only the reconstruction loss, as shown Table 3. Training the one- step flow model with only the reconstruction loss results in
8

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One Diffusion Step to Real-World Super-Resolutiin via Flow Trajectory Distillation
Impact Statement
He, X., Tang, H., Tu, Z.., Zhang. J., Cheng, K., Chen, H., Guo, Y., Zhu, M., Wang, N., Gao, X., al. One step diffusion-based super-resolution with time-aware distilla- tion. arXiv preprint ar arXiv:2408. 074.077700000777 2024.
This paper presents work whose goal to advance the field of Machine Learning. There are many potential societal consequences of our work, none which feel must be specifically highlighted here.
Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., et al. Scaling laws for autoregressive generative modeling. arXiv preprint arXiv:2010. 14701, 2020.
Acknowledgments
This work was supported by Shanghai Municipal Science and Technology Major Project (2727002)))00022 and the Fundamental Research Funds for the Central Universities.
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10


Reviews:
[{"review_id": "v2TrNMEZHw", "reviewer": "Reviewer_m3JT", "summary": "This paper proposes FluxSR, a one-step diffusion model for real-world image super-resolution (Real-ISR). The author introduces Flow Trajectory Distillation (FTD) to distill multi-step diffusion models into a single step. FluxSR addresses distribution shifts by aligning noise-to-image and low-to-high-resolution flow trajectories. The method also introduces TV-LPIPS and Attention Diversification Loss (ADL) to reduce artifacts.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Response to Reviewer m3JT (denoted as R1)\nWe sincerely thank the reviewer for the constructive comments. We provide the detailed responses to all the concerns below.\n\n**Q1-1:** Incomplete Ablation on FTD.\n\n**A1-1:** We add the relevant experiments. The results are shown in the table below. In practice, training the entire flow trajectory on $[T_L,1]$ yields similar results with training on a single time step. The main reason is that it is extremely hard to enforce a one-step model to directly mimic the entire flow trajectory, without explicitly preserving the generative ability of the teacher model. Instead, our FTD obtains significantly better results in most metrics that are widely used to evaluate visual quality.\n\n| Method               | PSNR  | MUSIQ | MANIQA | Q-Align |\n|----------------------|-------|-------|--------|---------|\n| $L_{rec}$ in $T_L$    | **26.33** | 56.02 | 0.3775 | 3.5170  |\n| $L_{rec}$ in $[T_L,1]$| 25.94 | 55.63 | 0.3900 | 3.6993  |\n| $L_{rec} + FTD$       | 24.67 | **67.84** | **0.5203** | **4.1473**  |\n\n*Ablation study on FTD*\n\n**Q1-2:** Insufficient Explanation for Artifacts.\n\n**A1-2:** To investigate the artifact issue, we visualize the attention/feature maps and found that the similarity between tokens is quite high, and there are indeed repeated features in some dimensions. Additionally, we observe that using the official FLUX model for one-step inference also results in high-frequency artifacts, which we believe is a characteristic of FLUX itself. When using only the reconstruction loss, we observed even more severe periodic artifacts, so these artifacts are not caused by FTD. We hypothesize that this issue is mainly attributed to the limited training data, which are significantly smaller than the ones used to train the multi-step FLUX. To verify this, when using a larger dataset, such as 10k noise-image pairs generated from the 220k-GPT4Vision-captions-from-LIVIS dataset, we observed a clear reduction in periodic artifacts.\n\n**Q1-3:** Limited Evaluation on Real Datasets.\n\n**A1-3:** According to your suggestions, we add the comparison on these two datasets, and the results are shown in the table below. In fact, FluxSR achieves the best results. We will include these into the paper.\n\n| Method      | DiffBIR | SeeSR  | SUPIR  | ResShift | SinSR  | AddSR  | OSEDiff | TSD-SR  | **FluxSR** |\n|-------------|---------|--------|--------|----------|--------|--------|---------|---------|------------|\n| MUSIQ       | 71.61   | 70.53  | 65.91  | 59.45    | 65.38  | 64.23  | 69.56   | 72.10   | **72.65**  |\n| MANIQA      | 0.5472  | 0.4971 | 0.3907 | 0.3383   | 0.4264 | 0.3707 | 0.4230  | 0.4596  | **0.5490** |\n| TOPIQ       | 0.6835  | 0.6653 | 0.5631 | 0.4709   | 0.5790 | 0.5470 | 0.6075  | 0.6456  | **0.6848** |\n| QAlign      | 4.2307  | 4.1652 | 4.1442 | 3.6340   | 3.7426 | 3.8884 | 4.2484  | 4.1682  | **4.4077** |\n\n*RealLQ250*\n\n| Method      | DiffBIR | SeeSR  | SUPIR  | ResShift | SinSR  | OSEDiff | AddSR  | TSD-SR  | **FluxSR** |\n|-------------|---------|--------|--------|----------|--------|---------|--------|---------|------------|\n| MUSIQ       | 69.63   | 69.75  | 64.88  | 59.87    | 65.11  | 65.42   | 69.61  | 71.02   | **71.60**  |\n| MANIQA      | 0.5526  | 0.5045 | 0.4677 | 0.3591   | 0.4549 | 0.3945  | 0.4388 | 0.4884  | **0.5588** |\n| TOPIQ       | 0.6772  | 0.6635 | 0.5870 | 0.4990   | 0.5998 | 0.5634  | 0.6083 | 0.6526  | **0.6814** |\n| QAlign      | 4.2529  | 4.2399 | 4.1675 | 3.7959   | 3.9317 | 4.0156  | 4.2895 | 4.2258  | **4.4004** |\n\n*RealLR200*\n\n**Q1-4:** There are some inconsistencies and typos regarding mathematical notations. For example, the formulation presented in Algorithm 1 (line 9) is inconsistent with the corresponding equation shown in Figure 3. Clarifying and correcting these discrepancies would improve readability.\n\n**A1-4:** Thank you for noticing and pointing out this mistake. We did make an error while editing the image. Specifically, the formula in Figure 3 corresponding to line 9 of Algorithm 1 should have a \"$+$\" sign instead of a \"$-$\". We will correct this error.\n\n**Q1-5:** Although Flux is described as a DiT-based diffusion model, Figure 3 visually depicts it as a U-Net structure, potentially misleading readers. Revising the figure to accurately represent Flux’s architecture (DiT-based transformer) would avoid confusion.\n\n**A1-5:** Thank you for pointing this out. We will delete the U-Net structure and replace it with the DiT architecture in Figure 3.", "claims_and_evidence": "Most claims in the paper are clearly supported by convincing experimental results, particularly regarding the effectiveness of Flow Trajectory Distillation (FTD) for aligning flow trajectories and improving realism. However, the claim related to the reason why high-frequency artifacts emerge (\"high-frequency artifacts due to token similarity in transformers\") is not sufficiently explained or supported.", "methods_and_evaluation_criteria": "The proposed method (FluxSR) is reasonable and effective in one-step diffusion methods for Real-ISR tasks. The evaluation metrics chosen by the authors are appropriate and cover both perceptual and fidelity aspects comprehensively.\n\nHowever, the experimental evaluation is somewhat limited due to the use of three datasets, of which DIV2K is synthetic and does not fully reflect real-world complexities. Evaluating on additional, diverse real-world datasets would further validate the generalizability and robustness of the proposed method.", "theoretical_claims": "The paper contains relatively few theoretical claims, primarily focused on clearly defined equations for Flow Trajectory Distillation (FTD). I have checked the main theoretical derivations and formulations provided (e.g., Equations 10–18). These derivations are straightforward and clear. I did not find any significant issues.", "experimental_designs_or_analyses": "I checked the soundness and validity of the experimental designs, particularly the ablation studies, which are comprehensive and effectively demonstrate the contribution of each proposed component (FTD, TV-LPIPS, and ADL). However, one important shortcoming is the lack of visualization results in these ablation studies. Specifically, visual comparisons illustrating how TV-LPIPS and ADL mitigate high-frequency artifacts would significantly strengthen and clarify the analysis.", "relation_to_broader_scientific_literature": "The proposed method, FluxSR, builds upon recent advances in one-step diffusion models (e.g., OSEDiff, TSD-SR), addressing their common limitations related to distribution shifts between teacher and student models through Flow Trajectory Distillation (FTD). Additionally, the proposed loss functions (TV-LPIPS and ADL) closely relate to perceptual loss-based super-resolution approaches (like RealESRGAN).", "essential_references_not_discussed": "I did not find any critical related works missing from the paper. The authors cited and discussed the relevant prior methods and ideas necessary for understanding their key contributions.", "other_strengths_and_weaknesses": "Strengths:\n1. The paper introduces an effective approach (Flow Trajectory Distillation) that addresses the limitations (e.g., distribution shift) of existing one-step diffusion methods.\n2. The paper is well-organized and generally clear, especially in terms of theoretical derivations and method explanations.\n\nWeaknesses:\n1. Incomplete Ablation on FTD: A key contribution of FTD is preserving the prior knowledge from the powerful teacher model (Flux). However, the authors only provide an ablation using the reconstruction loss at the single step ($T_L$). It remains unclear whether training the full trajectory ($[T_L , 1]$) using only reconstruction loss (without FTD) could achieve comparable results. Such an ablation would be crucial for validating the specific advantage provided by FTD, but is currently missing.\n2. Insufficient Explanation for Artifacts: The authors do not adequately explain why artifacts occur. If the final distribution aligns closely with Flux’s HR distribution, these artifacts should theoretically not emerge. The current hypothesis attributing artifacts to token similarity lacks empirical validation. Providing visualization of attention maps or investigating whether artifacts result from insufficient training time or limited training data would clarify this issue.\n3. Limited Evaluation on Real Datasets: The paper evaluates mainly on two datasets, with one (DIV2K) relying on synthetic degradations, limiting the strength of the empirical results. Evaluations on larger and more diverse real-world datasets (e.g., RealLR200 in SeeSR or RealLQ250 in DreamClear) would enhance the robustness and credibility of the experimental findings.", "other_comments_or_suggestions": "1. There are some inconsistencies and typos regarding mathematical notations. For example, the formulation presented in Algorithm 1 (line 9) is inconsistent with the corresponding equation shown in Figure 3. Clarifying and correcting these discrepancies would improve readability.\n2. Although Flux is described as a DiT-based diffusion model, Figure 3 visually depicts it as a U-Net structure, potentially misleading readers. Revising the figure to accurately represent Flux’s architecture (DiT-based transformer) would avoid confusion.", "questions_for_authors": "See the weaknesses.", "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": "TPkodihTMR", "reviewer": "Reviewer_uJUz", "summary": "The authors claim that most existing one-step diffusion methods are constrained by the performance of the teacher model, where poor teacher performance results in image artifacts. To this end, the authors proposed a one-step diffusion Real-ISR technique, namely FluxSR, based on FLUX.1-dev and flow matching models. The authors introduce Flow Trajectory Distillation (FTD) to distill a one-step model from the teacher model. The author provides comparative experiments with the state-of-the-art ISR methods. \n\n\n## update after rebuttal\nThe authors have addressed the majority of my concerns. Accordingly, I have updated my score to 3: Weak Accept.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Response to Reviewer uJUz (denoted as R2)\n\n**Q2-1:** The paper lacks innovation. The TV-LPIPS and ADL proposed in the paper are both existing works.\n\n**A2-1:** The main contribution of this paper is the introduction of FTD, and our method is the first work to distill a large-scale flow matching model like FLUX into a one-step model for image super-resolution. Existing one-step methods struggle with large diffusion models. Specifically, VSD (with optional GAN loss) requires two additional copies of the large model in GPU memory, exceeding the capacity of an 80GB A800 GPU. Our FTD is large-model-friendly, requiring only about 55GB of GPU memory per card for training, and 23.7GB for inference with 512px resolution.\n\n**Q2-2:** The authors analyze the possible negative outcomes of VSD or GANs in Sec. 4.1, but lack experimental support.\n\n**A2-2:** We add experiments to support our argument. Specifically, we generate images using teacher models of OSEDiff, AddSR, FluxSR and compute the FID between original T2I images and SR images. FluxSR’s FID is significantly lower than OSEDiff and AddSR, indicating that FTD avoids distribution shift.\n\n| Method   | AddSR | OSEDiff | **FluxSR** |\n|-|-|-|-|\n| **FID**  | 43.9  | 43.0 | **34.9**  |\n\n**Q2-3:** The FTD lacks quantitative and qualitative analysis to verify the effectiveness of this improvement. \n\n**A2-3:** We have presented quantitative results of the FTD ablation in the ablation study section, demonstrating its effectiveness. We will include qualitative comparisons in the supplementary material.\n\n**Q2-4:** Is the outstanding performance of the proposed method mainly attributed to the FLUX model?\n\n**A2-4:** We replace FluxSR's baseline with SD3-medium and retrained it, comparing it with TSD-SR that is also trained on SD3-medium . The results show that FluxSR outperforms TSD-SR, indicating that the performance improvement comes from our method.\n\n| Method | TSD-SR | FluxSR on SD3 |\n|-|-|-|\n| MANIQA | 0.4884 | **0.5310** |\n| TOPIQ  | 0.6526 | **0.6603** |\n| QAlign | 4.2258 | **4.3422** |\n\n**Q2-5:** Lack of corresponding comparison and analysis of model inference speed.\n\n**A2-5:** We compare FluxSR’s computational complexity with other multi-step and one-step methods. Despite using a 12B parameter model, the inference time of FluxSR is no more than 0.1s longer than the current fastest method.\n\n| Methods   | StableSR | DiffBIR | SeeSR | SUPIR | ResShift | SinSR | OSEDiff | TSD-SR | FluxSR |\n|-|-|-|-|-|-|-|-|-|-|\n| Inference step | 200 | 50 | 50 | 50 | 15 | 1 | 1 | 1 | 1 |\n| Inference time/s | 11.503 | 7.798 | 5.926 | 18.359 | 0.806 | 0.131 | 0.167 | 0.138 | 0.228 |\n| MACs/T | 75.81 | 24.52 | 32.34 | 120.41 | 4.90 | 2.09 | 2.27 | 2.91 | 11.71 |\n| # Params/B | 1.39 | 1.62 | 1.99 | 4.49 | 0.17 | 0.17 | 1.40 | 2.21 | 11.99 |\n\n**Q2-6:** What advantages does Large-Model-Friendly Training bring to model training and inference?\n\n**A2-6:** The Large-Model-Friendly Training (LMFT) strategy reduces memory usage and training time. During inference, it reduces memory usage and inference latency.\n\n| Method   | Training CUDA usage | Training time (per iteration) | Inference CUDA usage | Inference time (512px) |\n|-|-|-|-|-|\n| w/o LMFT | 76.2GB | 6.91s | 34.82GB | 457ms |\n| w LMFT   | 55.4GB | 4.43s | 23.77GB | 228ms |\n\n**Q2-7:** The color markings in Tab.1 and Tab.2 of the paper are confusing.\n\n**A2-7:** In Tables 1 and 2, red and blue bold represent the best and second-best methods among all approaches. The best method in one-step diffusion will also be bolded separately. Red bold indicates it is the best overall, while blue bold indicates it is the best in one-step methods but second-best overall.\n\n**Q2-8:** Lacks a comparison with SUPIR.\n\n**A2-8:** We add a comparison with SUPIR on RealLQ250 datasets in the table below. And we have included an efficiency comparison with SUPIR in **A5**.\n\n| Method      | DiffBIR | SeeSR  | SUPIR  | ResShift | SinSR  | AddSR  | OSEDiff | TSD-SR  | **FluxSR** |\n|-|-|-|-|-|-|-|-|-|-|\n| MUSIQ | 71.61   | 70.53  | 65.91  | 59.45    | 65.38  | 64.23  | 69.56   | 72.10   | **72.65**  |\n| MANIQA | 0.5472  | 0.4971 | 0.3907 | 0.3383   | 0.4264 | 0.3707 | 0.4230  | 0.4596  | **0.5490** |\n| TOPIQ | 0.6835  | 0.6653 | 0.5631 | 0.4709   | 0.5790 | 0.5470 | 0.6075  | 0.6456  | **0.6848** |\n| QAlign | 4.2307  | 4.1652 | 4.1442 | 3.6340   | 3.7426 | 3.8884 | 4.2484  | 4.1682  | **4.4077** |\n\n*RealLQ250*\n\n**Q2-9:** The ablation study in the paper lacks a qualitative comparison.\n\n**A2-9:** We would like to show the qualitative comparison of the ablation studies. However, it is not allowed to upload any images according to the rules of rebuttal. However, the metrics in our paper (MUSIQ, ManIQA, Q-Align) reflect image quality, showing that our method outperforms others in visual results, with TV-LPIPS and ADL effectively reducing high-frequency artifacts. We will include more qualitative comparisons in the revised paper.", "claims_and_evidence": "The author's claims are clear, but the evidence to support these claims is insufficient. There are mainly the following problems: \n\n(1) The authors analyse the possible negative outcomes of VSD or GANs in Sec. 4.1, but lack experimental support.", "methods_and_evaluation_criteria": "The proposed method and evaluation datasets used by the authors are reasonable.", "theoretical_claims": "I have carefully checked the correctness of the proofs for theoretical claims and found no relevant problems.", "experimental_designs_or_analyses": "I have carefully checked the soundness/validity of any experimental designs and analyses, and there are the following problems: \n\n(1) The outstanding performance of the proposed method may be mainly attributed to the FLUX model. If replaced with other baselines, the performance gain brought by the proposed pipeline in the paper is questionable.\n\n(2) The FTD proposed by the authors introduces the SR flow trajectory based on the existing T2I flow trajectory distillation, but lacks quantitative and qualitative analysis to verify the effectiveness of this improvement. \n\n(3) The authors proposed a large model friendly training strategy. What advantages does it bring to model training and inference? The authors should report this in the experimental analysis. \n\n(4) There is a lack of corresponding comparison and analysis of model inference speed in the experiment. \n\n(5) The paper lacks a comparison with the latest SUPIR [1]. An analysis of their performance and efficiency is necessary in the paper. \n\n(6) The ablation study in the paper lacks a qualitative comparison. \n\n[1] Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild, CVPR 2024", "relation_to_broader_scientific_literature": "This paper implements the Real-ISR task based on existing methods (FLUX model, TV-LPIPS, ADL), and further proposes an improved strategy (FTD). The key idea of FTD is introducing the LR-to-HR flow in SR based on the flow matching theory.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "The paper lacks innovation. The TV-LPIPS and ADL proposed in the paper are both existing works. The outstanding performance of the proposed method may be mainly attributed to the introduced FLUX model.", "other_comments_or_suggestions": "The color markings in Tab.1 and Tab.2 of the paper are confusing. There is no specific explanation of what red bold and blue bold mean. What is the difference between them and bold?", "questions_for_authors": "(1) The authors analyse the possible negative outcomes of VSD or GANs in Sec. 4.1, but lack experimental support. The author should support this conclusion with corresponding analysis. \n\n(2) The FTD proposed by the authors introduces the SR flow trajectory based on the existing T2I flow trajectory distillation, but lacks quantitative and qualitative analysis to verify the effectiveness of this improvement. Corresponding experimental analysis should be provided. \n\n(3) Is the outstanding performance of the proposed method mainly attributed to the FLUX model? If it is replaced with other baselines, will there still be performance advantages? Further analysis should be provided. \n\n(4) There is a lack of corresponding comparison and analysis of model inference speed in the experiment. The author should compare the inference speed of the proposed FluxSR with other methods.\n\n(5) The authors proposed a large model friendly training strategy. What advantages does it bring to model training and inference? The authors should report this in the experimental analysis. \n\n(6) The color markings in Tab.1 and Tab.2 of the paper are confusing. There is no specific explanation of what red bold and blue bold mean. What is the difference between them and bold?\n\n(7) The paper lacks a comparison with the latest SUPIR [1]. An analysis of their performance and efficiency is necessary in the paper. \n\n(8) The ablation study in the paper lacks a qualitative comparison. Corresponding comparisons should be provided. \n\n[1] Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild, CVPR 2024\n\nIf the author could address these issues, I would be inclined to raise my score.", "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": "jcp3aOwQAu", "reviewer": "Reviewer_ozHQ", "summary": "This paper improves on the one-step diffusion-based super-resolution methods that target the real-world image super-resolution (Real-ISR) task by distilling on a larger and more advanced baseline image generation model (FLUX) compared to existing works that leverage Stable Diffusion as a backbone. It introduces Flow Trajectory Distillation (FTD) to address the distribution shift issue of existing methods. It also proposes using total variation as a perceptual loss and the ADL proposed by Guo et al. to emphasize restoring high-frequency details and improving generation quality.\n\n## update after rebuttal\n\nI appreciate the authors' additional experiments and justifications, which have adequately addressed my concerns. From my perspective, it is reasonable that the proposed method improves only on a subset of metrics, as different approaches naturally focus on different aspects of the problem. This is why, despite noting limitations such as lower performance on metrics like PSNR and the presence of over-smooth qualitative results, I initially recommended a rating of weak accept. Overall, I believe the paper's contributions outweigh its limitations, and I am inclined to maintain my recommendation of weak acceptance. Thanks", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Response to Reviewer ozHQ (denoted as R3)\n\n**Q3-1:** Does not include the DrealSR that is widely used in various baselines.\n\n**A3-1:** Thank you for pointing out this issue. The table below shows the quantitative comparison on the DRealSR dataset. From the table below, our FluxSR obtains significantly better results.\n\n| Method   | DiffBIR | SeeSR | ResShift | SinSR | OSEDiff | TSD-SR  | **FluxSR** |\n|-|-|-|-|-|-|-|-|\n| **PSNR**  | 25.91   | 28.35 | 26.42    | 27.33 | 24.20   | 25.93   | 25.92      |\n| **SSIM**  | 0.6190  | 0.8052| 0.7310   | 0.7237| 0.7355  | 0.7423  | 0.7592     |\n| **LPIPS** | 0.5347  | 0.3031| 0.4582   | 0.4444| 0.3429  | 0.3383  | 0.3418     |\n| **DISTS** | 0.2387  | 0.1665| 0.2382   | 0.2262| 0.1763  | 0.1708  | **0.1628**    |\n| **MUSIQ** | 36.18   | 34.51 | 30.52    | 32.79 | 37.22   | 36.18   | **37.82**  |\n| **MANIQA**| 0.5059  | 0.4736| 0.3018   | 0.3907| 0.4793  | 0.4272  | **0.5310** |\n| **QAlign**| 4.2402  | 4.2050| 4.2770   | 4.2704| 4.2503  | 4.2751  | **4.3356** |\n\n*DRealSR*\n\n**Q3-2:** Concern about the over-sharp qualitative results shown in the Appendix. Also, the cost of the aforementioned full-reference metric like PSNR.\n\n**A3-2:** We agree that our FTD is not very effective on PSNR, but the hallucination issue is also not severe since most images still retain high consistency with the LR image in terms of content. We highlight that PSNR does not always align well with human visual perception, which has been widely proved in existing works. Moreover, when the input images are severely degraded, the \"hallucinated\" details generated by our method are reasonable and realistic, and no-reference metrics are better at reflecting the perceptual quality of the images.\n\n**Q3-3:** In line 035 of the appendix, there is a question mask during referencing that needs to be fixed.\n\n**A3-3:** Thank you for pointing out this mistake. We will make the correction.\n\n**Q3-4:** In Figure 1, the proposed FluxSR method seems to generate over-smooth results that do not align with the original low-res image. For example, the helmet in the bottom row ignores all the high-frequency details that can be observed from the LR image.\n\n**A3-4:**  We agree that the generated helmet is smooth, but still visually reasonable. We highlight that our FluxSR is able to adaptively adjust the generative ability according to the risk of generating artifacts, showing better robustness than existing methods. For example, the texture of helmet is very blurry and suffers a high risk of artifacts. Existing methods consistently produce very poor results with severe distortions. Nevertheless, as for the nose and mouth in the face, since the textures can be easily imagined, our FluxSR works very well to produce detailed textures. Moreover, our FluxSR produces better visual details for text (see the second row of Figure 5).\n\n**Q3-5:** Also, the ablation studies in Tables 3 and 4 only compare four of the eight metrics. Thus, it can be quite incomplete to show the effectiveness of each component. Besides, the reported PSNR is always inferior after incorporating this paper’s designs, which is detrimental to the justification of the claims.\n\n**A3-5:** We expand the comparison to include additional metrics to provide a more complete evaluation of the performance, as shown in the table below. We highlight that our FTD consistently obtains better results on the metrics that measure visual quality. Regarding the PSNR results, it is important to note that PSNR is not always the best indicator of perceptual image quality, especially in severely degraded images. Our method focuses on enhancing perceptual fidelity and more visually realistic output, and it is better captured by no-reference metrics. It may not always align with the traditional PSNR metric, but it contributes positively to the overall perceptual quality. We will clarify this trade-off in the revised manuscript, emphasizing that our method is optimized for visual realism rather than solely maximizing PSNR.\n\n| Method        | **PSNR** | **SSIM** | **LPIPS** | **DISTS** | **MUSIQ** | **MANIQA** | **TOPIQ** | **Q-Align** |\n|--|--|--|--|--|--|--|--|--|\n| w/o FTD       | **26.33** | **0.7580**   | 0.3801    | 0.2200    | 56.02     | 0.3775     | 0.4006    | 3.5170      |\n| FTD (ours)    | 24.67    | 0.7133   | **0.3324**| **0.1896**| **67.84** | **0.5203** | **0.6530**| **4.1473**  |\n\n*Ablation study on FTD*\n\n| $\\mathcal{L}_{\\text{LPIPS}}$ | $\\mathcal{L}_{\\text{TV-LPIPS}}$ | $\\mathcal{L}_{\\text{EA-DISTS}}$ | $\\mathcal{L}_{\\text{ADL}}$ | **SSIM** | **LPIPS** | **DISTS** | **TOPIQ** | \n|--|--|--|--|--|--|--|--|\n| ✓ |  |  |  | 0.6893   | 0.3459    | 0.2096    | 0.6242    |\n|  | ✓ |  |  | 0.6999   | 0.3369    | 0.1933    | 0.6387    |\n|  |  | ✓ |  | 0.7283   | 0.3423    | 0.1970    | 0.6400    |\n|  |  | ✓ | ✓ | **0.7339**| 0.3332    | 0.1915    | 0.6427    |\n| ✓ |  | ✓ | ✓ | 0.7133   | **0.3324**| **0.1896**| **0.6530**|\n\n*Ablation study on different loss functions*", "claims_and_evidence": "The TV-LPIPS component is claimed to emphasize the restoration of high-frequency components; however, in Figure 1, the proposed FluxSR method seems to generate over-smooth results that do not align with the original low-res image. For example, the helmet in the bottom row ignores all the high-frequency details that can be observed from the LR image.\n\nAlso, the ablation studies in Tables 3 and 4 only compare four of the eight metrics. Thus, it can be quite incomplete to show the effectiveness of each component. Besides, the reported PSNR is always inferior after incorporating this paper’s designs, which is detrimental to the justification of the claims.", "methods_and_evaluation_criteria": "Overall, the evaluation criteria used in this paper, such as the metrics and benchmark datasets, make sense to me. This paper includes a range of metrics for evaluation. It adopts the standard RealESR-GAN’s degradation pipeline for creating the training data and DIV2K-val, RealSR, and RealSet65 as the test set, which, however, does not include the DrealSR that is widely used in various baselines.", "theoretical_claims": "The theoretical claims seem to make sense to me; however, I have not thoroughly checked or reproduced the derivations myself.", "experimental_designs_or_analyses": "Overall, the experimental designs in this paper, such as the compared baselines, make sense to me. I acknowledge that there are many more one-step diffusion-based Real-ISR methods at the moment; however, I believe it is sufficient for the authors to only compare with the reported baselines.", "relation_to_broader_scientific_literature": "This paper mainly contributes to proposing the first one-step diffusion for Real-ISR based on a large model with over 12B parameters (FLUX.1-dev), highlighting its practical value.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Despite the practical contribution this paper makes, I am a bit concerned about the over-sharp qualitative results shown in the Appendix. Also, the inferior PSNR results shown in both the main Table and the Tables for ablation studies since these suggest that the super-resolved images can hallucinate some image details, which increases no-reference metrics, however, at the cost of the aforementioned full-reference metric like PSNR.", "other_comments_or_suggestions": "In line 035 of the appendix, there is a question mask during referencing that needs to be fixed.", "questions_for_authors": "Please check out the previous sections regarding my questions and concerns.", "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": "MDTjWnWLmu", "reviewer": "Reviewer_jQYF", "summary": "The paper introduces FluxSR, a novel one-step diffusion model for Real-ISR (Real-World Image Super-Resolution). The primary goal is to reduce the high computational cost associated with multi-step diffusion models while preserving high-quality image generation. The key innovation is Flow Trajectory Distillation (FTD), which transfers the generative capabilities of a large-scale T2I diffusion model (FLUX.1-dev) into a single-step framework. Additionally, TV-LPIPS loss is introduced to suppress high-frequency artifacts, and Attention Diversification Loss (ADL) is used to prevent repetitive patterns.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Response to Reviewer jQYF (denoted as R4)\n\n**Q4-1:** How does the noise-to-image mapping in T2I models fundamentally differ from LR-to-SR degradations in practice? Could you provide a more detailed analysis to support the claim in Figure 2?\n\n**A4-1:** Although $x_t$ in the diffusion process *appears* to be similar to LR, they are fundamentally different. First, the distributions of degradations on these mappings are different. The calculation of $x_t$ is given by \n\n$$\nx_t = (1 - t) x_0 + t \\epsilon,\n$$ \n\nwhich is equivalent to adding Gaussian noise to the image. In contrast, LR is a real-world low-resolution image, which undergoes complex and unknown degradation. In other words, LR images may not be on the T2I trajectory, which is also a condition for the validity of Figure 2. Second, the degradations/noises are added in different domains. T2I adds noise in the latent space, but LR-to-SR can be regarded as adding degradations/noise onto the HR images. We highlight that these differences directly motivates us to reduce the distribution shift and propose our FTD method.\n\n**Q4-2:** Can you provide a quantitative comparison of inference time, MACs, and parameter count for FluxSR vs. other one-step and multi-step methods? This would help support the efficiency claims.\n\n**A4-2:** Yes, we have compared FluxSR's computational complexity with other multi-step and single-step methods, including inference speed, MACs, and parameter count. Despite using a 12B parameter model, the inference speed of our method is less than twice that of the fastest one-step diffusion ISR method.\n\n| Methods   | StableSR | DiffBIR | SeeSR | SUPIR | ResShift | SinSR | OSEDiff | TSD-SR | FluxSR |\n|--|--|--|--|--|--|--|--|---|--|\n| Inference step | 200 | 50 | 50 | 50 | 15 | 1 | 1 | 1 | 1 |\n| Inference time/s | 11.503 | 7.798 | 5.926 | 18.359 | 0.806 | 0.131 | 0.167 | 0.138 | 0.228 |\n| MACs/T | 75.81 | 24.52 | 32.34 | 120.41 | 4.90 | 2.09 | 2.27 | 2.91 | 11.71 |\n| # Params/B | 1.39 | 1.62 | 1.99 | 4.49 | 0.17 | 0.17 | 1.40 | 2.21 | 11.99 |\n\n**Q4-3:** Can you provide a visual comparison of TV-LPIPS vs. LPIPS alone? This would help illustrate the effectiveness of the proposed perceptual loss.\n\n**A4-3:** Thank you for your suggestion. We would like to show the qualitative comparison of the ablation studies. However, it is not allowed to upload any images according to the rules of rebuttal. In fact, our TV-LPIPS loss achieves better visual results than the LPIPS loss. As shown in Table 4 of the paper (the first two rows), this can be verified by the improved metrics that measure visual quality, including MUSIQ, ManIQA, and Q-Align. We will include more qualitative comparisons in the revised paper.\n\n**Q4-4:** How sensitive is FluxSR to different training datasets? Would training on a different set of noise-image pairs alter its performance?\n\n**A4-4:** We generate 10k noise-image pairs on the 220k-GPT4Vision-captions-from-LIVIS dataset as new training data. After training with a larger dataset, there are no significant changes in the quantitative results. However, we observe that the model trained with the larger dataset generated fewer high-frequency artifacts in the actual generated images.\n\n**Q4-5:** Can the proposed FTD method be extended to video super-resolution? Would additional temporal constraints be needed?\n\n**A4-5:**  Our FTD is indeed a promising approach for image SR, and we believe it could be extended to video SR with some adjustments. Incorporating temporal constraints (e.g., optical flow, temporal smoothness, feature alignment and propagation) between frames is crucial to avoid introducing flickering or artifacts across consecutive frames, and helps preserve motion consistency and smoothness. Due to the time limit, we leave it for future work.\n\n**Q4-6:** No Supplementary Material is provided.\n\n**A4-6:** In fact, we have provided supplementary material.\n\n**Q4-7:** A comparison to alternative single-step SR methods (e.g., GAN-based SR models like ESRGAN, Real-ESRGAN) would help contextualize the approach.\n\n**A4-7:** We have provided a comparison with non-diffusion methods in the supplementary material. Here, we present a comparison with BSRGAN, Real-ESRGAN, SwinIR, LDL, and FeMASR, as shown in the table below.\n\n| Method       | RealSR-JPEG | BSRGAN | ESRGAN | Real-ESRGAN | SwinIR | LDL   | FeMaSR | **FluxSR** |\n|--------------|-------------|--------|--------|-------------|--------|-------|--------|------------|\n| **MUSIQ**    | 50.54       | 65.58  | 42.37  | 63.22       | 63.82  | 63.22 | 64.88  | **70.75**  |\n| **MANIQA**   | 0.2927      | 0.3887 | 0.3100 | 0.3892      | 0.3818 | 0.3897| 0.4017 | **0.5495** |\n| **TOPIQ**    | 0.4118      | 0.5812 | 0.3864 | 0.5368      | 0.5306 | 0.5358| 0.5736 | **0.6670** |\n| **QAlign**   | 3.4416      | 3.9730 | 2.9680 | 4.0442      | 3.9661 | 4.0038| 4.0162 | **4.2134** |\n\n*RealSet65*", "claims_and_evidence": "1. The claims about FluxSR's performance improvements are well-supported by quantitative and visual results in Tables 1, 2, and Figure 5.\n2. The claim that FTD prevents distribution shift is plausible but not directly validated by additional distribution analysis.\n3. The effectiveness of TV-LPIPS is quantitatively supported but lacks a visual comparison.", "methods_and_evaluation_criteria": "1. The proposed FTD method is conceptually sound and effectively translates flow matching principles to super-resolution.\n2. The evaluation criteria (PSNR, SSIM, LPIPS, DISTS, MUSIQ, MANIQA, TOPIQ, Q-Align) are appropriate for Real-ISR tasks.\n3. However, adding inference speed and computational cost metrics would strengthen the evaluation.", "theoretical_claims": "The Flow Trajectory Distillation formulation is mathematically consistent with flow matching theory. No formal proofs are included, but the derivations appear correct.", "experimental_designs_or_analyses": "1. The experimental setup is reasonable, using pre-generated noise-image pairs instead of real datasets.\n2. The ablation study on loss functions is well-structured, but missing a visual comparison of TV-LPIPS.\n3. A missing comparison of inference efficiency limits conclusions about computational benefits.", "relation_to_broader_scientific_literature": "1. The paper builds on prior diffusion-based Real-ISR methods (e.g., OSEDiff, SinSR, TSD-SR) and flow matching models (e.g., ReFlow, InstaFlow).\n2. The discussion of one-step vs. multi-step models is well-grounded in prior research.\n3. The connection to large-scale T2I diffusion models (e.g., FLUX, SDXL) is relevant.", "essential_references_not_discussed": "A comparison to alternative single-step SR methods (e.g., GAN-based SR models like ESRGAN, Real-ESRGAN) would help contextualize the approach.", "other_strengths_and_weaknesses": "Strengths:\n1. The paper is well-written and clearly structured.\n2. The mathematical formulation of FTD is well-integrated with flow-matching theory.\n3. Introducing ADL for SR is a new contribution.\n\nWeaknesses:\n1. Some claims about the differences between T2I and SR mapping require stronger justification. The paper argues that the T2I noise-to-image mapping differs significantly from the LR-to-SR degradation process, necessitating FTD. However, in recent large-scale T2I models, the final stages of denoising already address degradations similar to LR-to-SR, making the proposed motivation less compelling.\nA more thorough analysis (e.g., comparing distributions of features extracted from T2I and SR models) would strengthen this claim.\n2. No quantitative evaluation of computational efficiency. The paper claims FluxSR achieves efficient inference, but there is no quantitative comparison of inference speed, MACs (Multiply-Accumulate Operations), or parameter count. A table comparing these metrics against one-step and multi-step baselines would clarify the trade-offs between computational efficiency and performance.\n3. Lack of a visual ablation for TV-LPIPS. The ablation study in Table 4 demonstrates that TV-LPIPS improves perceptual quality, but a visual comparison (before and after applying TV-LPIPS) would provide a more intuitive understanding. Adding side-by-side images showing the effect of TV-LPIPS vs. LPIPS alone would strengthen the justification.", "other_comments_or_suggestions": "1. Can you provide a quantitative comparison of inference time, MACs, and parameter count for FluxSR vs. other one-step and multi-step methods? This would help support the efficiency claims.\n2. Can you provide a visual comparison of TV-LPIPS vs. LPIPS alone? This would help illustrate the effectiveness of the proposed perceptual loss.\n3. How does the noise-to-image mapping in T2I models fundamentally differ from LR-to-SR degradations in practice? Could you provide a more detailed analysis to support the claim in Figure 2?\n4. How sensitive is FluxSR to different training datasets? Would training on a different set of noise-image pairs alter its performance?\n5. Can the proposed FTD method be extended to video super-resolution? Would additional temporal constraints be needed?", "questions_for_authors": "None.", "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": "7n2zTs8S1d", "reviewer": "Reviewer_mAgS", "summary": "The paper introduces FluxSR, a novel one-step diffusion model for real-world image super-resolution (ISR), leveraging flow trajectory distillation (FTD) to distill a multi-step diffusion model into a one-step model. The authors propose several innovations, including TV-LPIPS as a perceptual loss and attention diversification loss (ADL) to reduce high-frequency artifacts. The method achieves promising performance in both quantitative and qualitative evaluations, outperforming existing one-step and multi-step diffusion-based Real-ISR methods.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Response to Reviewer mAgS (denoted as R5)\n\n**Q5-1:** Could the authors provide more details on the scalability of FluxSR? For instance, how does the method perform on larger or more diverse datasets, and what are the implications for real-time applications?\n\n**A5-1:** We generate 10k noise-image pairs on the 220k-GPT4Vision-captions-from-LIVIS dataset as new training data. After training with a larger dataset, there are no significant changes in the quantitative results. However, we observe that the model trained with the larger dataset generated fewer high-frequency artifacts in the actual generated images. For real-time applications, further optimization, such as model pruning, quantization or more efficient techniques, would be helpful to improve real-time efficiency without sacrificing performance.\n\n**Q5-2:** Are there any plans to further refine the model to address this issue? Could the authors explore additional regularization techniques or architectural changes to mitigate these artifacts?\n\n**A5-2:** Yes, we increase the weights of the anti-artifact losses. Specifically, we increase the weight of TV-LPIPS to 2 and the weight of ADL to 0.2. In practice, we do not observe the high-frequency artifacts anymore, which demonstrates the effectiveness of the proposed anti-artifacts losses. We will include these results into the paper.\n\n**Q5-3:** While the paper compares FluxSR with other diffusion-based methods, it would be beneficial to include a more detailed comparison with non-diffusion-based approaches, especially in terms of computational efficiency and real-world applicability.\n\n**A5-3:** Thank you for your suggestion. We compare the computational efficiency and quantitative comparison of FluxSR with non-diffusion methods in the table below. Compared to non-diffusion methods, FluxSR produces significantly better visual results with higher image quality. Although FluxSR has higher computational complexity, it generates more realistic images. In highly degraded scenarios, if the user aims to generate high-quality images and is not too strict about inference speed, we believe FluxSR holds greater value.\n\n| Methods         | RealSR-JPEG | BSRGAN | ESRGAN | Real-ESRGAN | SwinIR | FeMaSR | FluxSR  |\n|-----------------|-------------|--------|--------|-------------|--------|--------|---------|\n| **Inference time / s** | 0.042       | 0.042  | 0.042  | 0.042       | 0.200  | 0.082  | 0.228   |\n| **MACs / T**    | 0.294       | 0.294  | 0.294  | 0.294       | 0.478  | 0.476  | 11.71   |\n| **# Params / B**| 0.017       | 0.017  | 0.017  | 0.017       | 0.027  | 0.033  | 11.99   |\n\n*Complex Analysis*\n\n| Method       | RealSR-JPEG | BSRGAN | ESRGAN | Real-ESRGAN | SwinIR | LDL   | FeMaSR | **FluxSR** |\n|--------------|-------------|--------|--------|-------------|--------|-------|--------|------------|\n| **MUSIQ**    | 50.54       | 65.58  | 42.37  | 63.22       | 63.82  | 63.22 | 64.88  | **70.75**  |\n| **MANIQA**   | 0.2927      | 0.3887 | 0.3100 | 0.3892      | 0.3818 | 0.3897| 0.4017 | **0.5495** |\n| **TOPIQ**    | 0.4118      | 0.5812 | 0.3864 | 0.5368      | 0.5306 | 0.5358| 0.5736 | **0.6670** |\n| **QAlign**   | 3.4416      | 3.9730 | 2.9680 | 4.0442      | 3.9661 | 4.0038| 4.0162 | **4.2134** |\n\n*RealSet65*\n\n \n**Q5-4:** The paper does not explore how well the method performs when applied to different types of image degradation beyond the tested datasets.\n\n**A5-4:** We evaluate FluxSR on face restoration task. Although no specific training set was used, our method still achieves good results. The specific quantitative results are shown in the table below.\n\n| Method           | RestoreFormer++ | VQFR   | CodeFormer | DAEFR  | PGDiff | DifFace | DiffBIR | OSEDiff | OSDFace | **FluxSR** |\n|------------------|-----------------|--------|------------|--------|--------|---------|---------|---------|---------|------------|\n| **MUSIQ**        | 71.484          | 70.906 | 74.001     | 72.698 | 68.599 | 65.116  | 72.272  | 69.322  | 73.935  | **75.908**  |\n| **MANIQA**       | 0.4902          | 0.4909 | 0.5034     | 0.4934 | 0.4460 | 0.4189  | 0.5839  | 0.4713  | 0.5162  | **0.6765**  |\n| **ClipIQA**      | 0.6950          | 0.6769 | 0.6918     | 0.6696 | 0.5653 | 0.5737  | 0.7441  | 0.6321  | 0.7106  | **0.7604**  |\n\n*WebPhoto-Test*\n\n\n| Method           | RestoreFormer++ | VQFR   | CodeFormer | DAEFR  | PGDiff | DifFace | DiffBIR | OSEDiff | OSDFace | **FluxSR** |\n|------------------|-----------------|--------|------------|--------|--------|---------|---------|---------|---------|------------|\n| **MUSIQ**        | 71.332          | 71.417 | 73.406     | 74.143 | 68.135 | 64.907  | 75.321  | 66.538  | 74.601  | **76.198**  |\n| **MANIQA**       | 0.4767          | 0.5044 | 0.4958     | 0.5205 | 0.4531 | 0.4299  | 0.6625  | 0.4616  | 0.5229  | **0.6665**  |\n| **ClipIQA**      | 0.7159          | 0.7069 | 0.6986     | 0.6975 | 0.5824 | 0.5924  | **0.8084** | 0.6235 | 0.7284  | 0.7847     |\n\n*Wider-Test*", "claims_and_evidence": "The paper provides ample empirical evidence to support its claims, including quantitative results, qualitative comparisons, ablation studies, and theoretical justifications. The proposed method, FluxSR, demonstrates its improvements over existing approaches in terms of image quality, computational efficiency, and artifact reduction. However, the paper also acknowledges some limitations, such as the computational cost of training and the presence of periodic artifacts, which could be addressed in future work.", "methods_and_evaluation_criteria": "The paper employs a comprehensive set of methods and evaluation criteria to demonstrate the effectiveness of FluxSR. \n\n- The proposed FTD, TV-LPIPS perceptual loss, and ADL are key innovations that contribute to the model's superior performance. \n\n- The evaluation includes both quantitative metrics and qualitative comparisons, along with thorough ablation studies to validate the contributions of each component. \n\n- The results show that FluxSR achieves SOTA performance in real-world ISR with only one diffusion step.", "theoretical_claims": "The paper provides a solid theoretical foundation for the proposed FluxSR model, supported by flow matching theory, mathematical formulations, and empirical evidence. The key innovations (FTD, TV-LPIPS perceptual loss, and ADL) are well-justified and contribute to the model's superior performance in real-world ISR. The theoretical claims are validated through extensive experiments, ablation studies, and visual comparisons, demonstrating the effectiveness of FluxSR.", "experimental_designs_or_analyses": "The paper employs a comprehensive set of experimental designs and analyses to validate the effectiveness of the proposed FluxSR model. The experiments include quantitative evaluations, qualitative comparisons, ablation studies, and comparisons with non-diffusion methods. The results demonstrate that FluxSR achieves SOTA performance in real-world ISR with only one diffusion step, while also addressing key challenges such as computational efficiency and artifact reduction. The ablation studies provide insights into the contributions of individual components (FTD, TV-LPIPS, ADL), and the visual comparisons highlight the model's ability to generate realistic and detailed images.", "relation_to_broader_scientific_literature": "The paper builds on and advances existing research in image super-resolution, diffusion models, and flow matching theory. By introducing flow trajectory distillation (FTD), TV-LPIPS perceptual loss, and attention diversification loss (ADL), the authors address key challenges in real-world SR, such as computational efficiency, artifact reduction, and perceptual quality. The proposed FluxSR model achieves SOTA performance with only one diffusion step, providing a new direction for real-world SR research.", "essential_references_not_discussed": "The references are relatively comprehensive.", "other_strengths_and_weaknesses": "- Strengths\n\n1) The proposed flow trajectory distillation (FTD) is a novel approach that effectively bridges the gap between noise-to-image and LR-to-HR flows, preserving the generative capabilities of the teacher model while enabling efficient one-step inference.\n\n2) The method achieves impressive results, outperforming existing one-step and multi-step diffusion-based methods across multiple datasets. The qualitative results demonstrate that FluxSR generates more realistic and detailed images compared to other SOTA methods.\n\n3) By reducing the inference steps to one, FluxSR significantly reduces computational overhead and inference latency, making it more practical for real-world applications.\n\n- Weaknesses\n\n1) While the method reduces inference steps, the training process still requires significant computational resources, particularly due to the use of large models like FLUX.1-dev. This could limit its accessibility for researchers with limited resources.\n\n2) Although the authors propose ADL and TV-LPIPS to address high-frequency artifacts, the paper acknowledges that periodic artifacts are not entirely eliminated. This suggests room for further improvement in artifact reduction.\n\n3) The method relies heavily on the pre-trained FLUX.1-dev model, which may limit its generalization to other domains or tasks. The paper does not explore how well the method performs when applied to different types of image degradation beyond the tested datasets.", "other_comments_or_suggestions": "None.", "questions_for_authors": "1) Could the authors provide more details on the scalability of FluxSR? For instance, how does the method perform on larger or more diverse datasets, and what are the implications for real-time applications?\n\n2) Given that periodic artifacts are still present, are there any plans to further refine the model to address this issue? Could the authors explore additional regularization techniques or architectural changes to mitigate these artifacts?\n\n3) While the paper compares FluxSR with other diffusion-based methods, it would be beneficial to include a more detailed comparison with non-diffusion-based approaches, especially in terms of computational efficiency and real-world applicability.", "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:

Meta-review: This paper proposes a one-step diffusion method based on flow trajectory distillation for image super-resolution. The paper originally received 2xWeakReject and 3xWeakAccept. The main concerns include insufficient explanations, limited evaluations and novelty, lack of discussions on computational efficiency, etc. The authors have provided rebuttals and addressed most concerns of reviewers. Afterward, two reviewers raise their ratings and all reviewers lean to accept this paper. The authors are suggested to carefully revise the paper and incorporate newly conducted experiments according to the comments and discussions.
Decision: Accept (poster)