LucidFlux: Caption-Free Universal Image Restoration via a Large-Scale Diffusion Transformer
Authors: Song Fei
Tian Ye
Lujia Wang
Lei Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Transformer; Generative Models; Image Restoration
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Caption-Free Universal Image Restoration via a Large-Scale Diffusion Transformer

Abstract: Universal image restoration (UIR) aims to recover images degraded by unknown mixtures while preserving semantics—conditions under which discriminative restorers and UNet-based diffusion priors often oversmooth, hallucinate, or drift. We present LucidFlux, a caption-free UIR framework that adapts a large diffusion transformer (Flux.1) to restoration with minimal parameter overhead. LucidFlux introduces a lightweight \emph{dual-branch conditioner} that injects signals from the degraded input and a lightly restored proxy to respectively anchor geometry and suppress artifacts. A timestep- and layer-adaptive modulation schedule routes these cues across the backbone’s hierarchy, yielding coarse-to-fine, context-aware updates that protect global structure while recovering texture. To avoid the latency and instability of text prompts or VLM captions, we enforce \emph{caption-free semantic alignment} via SigLIP features extracted from the proxy. A scalable curation pipeline further filters large-scale data for structure-rich supervision. 

Across synthetic and in-the-wild benchmarks, LucidFlux consistently surpasses strong open-source and commercial baselines across seven metrics, with clear visual gains in realism, detail, and artifact suppression. Ablations confirm that, for large DiTs, when, where, and what to condition—rather than scaling parameters or relying on text prompts—is the key lever for robust, prompt-free restoration.

Published as a conference paper at ICLR 2026
LUCIDFLUX: CAPTION-FREE PHOTO-REALISTIC IM- AGE RESTORATION VIA A LARGE-SCALE DIFFUSION TRANSFORMER
Song Fei!
The Hong Kong University of Science and Technology (Guangzhou) sfei285@connece hkust-gee -ggst-gg... edu. en
Tian Ye
The Hong Kong University of Science and Technology (Guangzhou) tye610@connect. hkust-qz edu. en
Lujia Wang
The Hong Kong University of Science and Technology (Guangzhou) eewanglj@hkust gz www.gg....uucoooo .ddu.
Lei Zhu"
The Hong Kong University of Science and Technology The Hong Kong University of Science and Technology (Guangzhou)
le wehu@hkust-gre edu. cn
Image Restoration Real-World Samples
Inpust
Flux Output
Imput
Locedt
Crospon
Input
LavidFin Hel
(b) Image Restoration Different Scene
Paice
Text
AGUEST
AGAIST COPOVIES
NAWKET MESH INC RECOROOTS ABSIN FEFFPRST
TO AGMIST WESSN ORPUNES BUnE Buttour
VOTE AGAIST WE OPFOUES WOPLD SAT
Low Quality Input
SceSR
SUPIR
DreamClear
LucidFlux (Ours)
Figure I: We present LucidFlux, image restoration framework built on a large-scale diffusion transformer that delivers photorealistic restorations of real-world low-quality (LQ) images, outper- forming state-of-the-art (SOTA) diffusion-base models across diverse degradations.
Equal contribution "Corresponding author.

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Published as a conference paper at ICLR 2026
ABSTRACT
Image restoration (IR) aims to recover images degraded by unknown mixtures while preserving conuuticc--oniiioons under which discriminative restorers and UNet-based diffusion priors often oversmooth, hallucinate, or drift. We present LucidFlux, a caption-free IR framework that adapts a large diffusion transformer (Flux.1) without image captions. Our LucidFlux introduces lightweight dual- branch conditioner that injects signals from the degraded input and a lightly restored proxy respectively anchor geometry and suppress artifacts. Then, a timestep- and layer-adaprive modulation schedule is designed to route these cues across the backbone's hierarchy, in order to yield coarse-to-fine and context-aware updates that protect the global structure while recovering texture. After that, to avoid the latency and instability of text prompts or Vision-Languaa (VLM) captions, we enforce caption-free semantic alignment via SigLIP features extracted from the proxy. A scalable curation pipeline further filters large-scale data for structure- rich supervision. Across synthetic and in-the-wild benchmarks, our LucidFlux consistently outperforms strong open-source and commercial base- lines, and ablation studies verify the necessity of each component. LucidFlux shows that, for large DiTs, when, where, and what to condition on-rather than adding parameters or relying on text prompts-is the governing lever for robust and caption-free image restoration in the wild.
Model
Project page: https: http:/:///wg//iaiee -ab github. http.//.t.....o..iiiii.......... www..idd/// Code: https:
INTRODUCTION
Images acquired in the wild exhibit mixed, unknown degradations--ense noise, motion blur, lens aberrations, compression artifacts-that erode perceptual fidelity and induce semantic drift in recog- nition and analysis. Image restoration (IR) seeks to reconstruct images with high perceptual fidelity while preserving semantic consistency under such uncertainty and without access to degradation labels or side information. Despite steady progress, this combination of unknown mixtures, realism, and semantic preservation remains stubbornly challenging.
Discriminative restorers based on CNNs and Transformers (Ye et al. (2023; 2022); Chen et al. (2023c); Dong et al. (2016); Liang et al. (2021); Zamir al. (2022); Chen al. (2023b; 2022)) per- form well on synthetic distortions but falter on in-the-will mixtures, often oversmoothing textures or leaving visible artifacts. This gap has motivated generative approaches that leverage diffusion- based text-to-image priors to synthesize plausible structure and detail beyond the reach of purely discriminative models (Yu et al. (2024); Ai et al. (2024); Wu et al. (2024b); Wang et al. (2024a;b); Yue al. (2023): Wu et al. (2024a); Lin et al. (2025a)). Early diffusion- based IR systems predomi- nantly rely on Stable Diffusion (SD) UNet backbones (Rombach et al. (2022)), whose capacity and inductive bias tend to saturate under complex degradations, making it difficult to recover fine detail while maintaining global structure. More recent works have begun to explore transformer-basee diffusion priors for restoration (e.g., DiT-based variants such as DreamClear Ai et al. (2024)), and we explicitly build on this line; in this paper we focus on how to adapt a large, generic DiT such as Flux.1 to real-world IR with lightweight, caption-free conditioning.
Recent advances in diffusion transformers (DiT's open a promising avenue. In contrast to UNet architectures, DiTs employ attention-centric backbones that more effectively couple global context with local detail and carry richer generative priors. For instance, DreamClear (Ai et al. (2024)) builds on PixArt-a (Chen et al. (2023a)), a relatively small (0.6B) DIT, illustrating the promise of transformer backbones for restoration, and related DiT-based IR models are further reviewed in Sec. 2. However, their limited scale constrains robustness to mixed, real-world degradations and im- pedes the concurrent recovery of global structure and fine detail. Large-scale diffusion transformers such as Flux.1 I (Labs (2024)) deliver strong modeling capacity for diverse, mixed- degedddaaaaitoonn image restoration, yet direct transfer rarely works off-the-shelf. Previous ControlNet methods (Yu et al. (2024); Ai et al. (2024); Zhang et al. (2023)) disrupt the parameten-structure balance and underutilize the backbone's temporal and hierarchical division of labor. Unconstrained
conditioning
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injection of degraded observations amplifies artifacts; relying VLM-generated captions further increases latency and risks semantic drift". Meanwhile, backbones at this scale are decisively data- limited: gains follow cata-compute scaling only when trained on curated, large-scale, high-quality sets. Public web corpora fall short for IR-they skew toward aesthetic, compression-heavy images, contain substantial near-duplicates and low-information frames, and rarely cover the long-tail mix- tures of real degradations or provide usable pairs. Without rigorous filtering and cructure-awar selection, large DiTs underutilize capacity and overfit spurious artifacts, underscoring the need for an explicit curation pipeline. Taken together, these tensions point to more structured path, one that schedules conditioning across timesteps and layers, couples robust input handling with caption- free inference, and remains practical to assemble on available datasets.
To operationalize this path, we introduce LucidFlux, a caption-free IR framework that adapts the large-scale Flux.1 diffusion transformer to restoration. The core of our LucidFlux is lightweight dual-branch conditioner-a two-block transformer module that injects signals from the degraded input without inflating the parameters. One branch ingests the low-quality image to anchor the geometry and layout, while the other consumes a lightly restored proxy to suppress hard artifacts; their outputs scheduled through timestep- and layer-adaptive modulation that aligns guidance with the backbone's 's hierarchical roles, yielding searse-to-fine context-ane aware updates that preserve texture while protecting global structure. To avoid the latency and drift introduced by text prompts, we enforce semantic consistency via caption-free alignment with SigLJP, extracting semantic cues directly from the proxy. We pair the model with an automated three-stage curation pipeline- blur detection, flat-region filtering, and perceptual quality scoring-to assemble diverse training sets at the billion-parameter scale.
Our contributions are as follows:
LucidFlux framework. We adapt a large diffusion transformer (Flux.1) to IR with lightweight dual-branch conditioner and timestep- and layer-adaptive modulation, aligning conditioning with the backbone's 's hicrarchical roles while keeping less trainable parame- ters.
Caption-free semantic alignment. A SigLIP-based module preserves semantic consis- tency without prompts or captions, mitigating latency and semantic drift.
Scalable data curation pipeline. reproducible, three-stage filtering pipeline yields di- verse, structure-rich datasets that scale to billion- parameeer training.
State-of-the-art results. LucidFlux sets new SOTA on a broad suite of benchmarks and metrics, surpassing competitive open- and closed-source baselines; ablation studies confirm the necessity of each module.
2 RELATED WORK
Generative Priors for IR. Large-scale pretrained generative models, particularly text-to-image dif- fusion models (Rombach et al. (2022); Podell et al. (2023); Yu et al. (2025); Yang et al. (2025)), have shown strong capability in synthesizing high-fidelity textures and structures for image restora- tion. Existing approaches build on different backbones, with SUPIR (Yu et al. (2024)) using SDXL, DreamClear (Ai et al. (2024)) relying on PixArt-a (Chen et al. (2023a)). StableSR on SD, SeeSR on SD2, and Resshift (Yue et al. (2023)) and SinSR (Wang et al. (2024b)) trained from scratch. UNet- based systems (e.g., StableSR, SeeSR, Resshift, SinSR, SDXL-based variants) inherit the capacity and inductive-bias limitations of SD-style backbones under complex, mixed degradations, while re- cent DiT-based IR models such as DreamClear demonstrate the promise of transformer priors but typically adopt relatively small, task-specific DiTs and heavy -ostrolNet-stylee adapters. This makes it difficult to fully exploit the capacity of modern large-scale text-to-image transformers for IR. Ad- dressing these challenges, we propose LucidFlux, image restoration framework that adapts the 12B Flux. 1 DiT with lightweight buanchranch conditioner and caption-free conditioning, providing a different operating point within this line of snneraatve-prior-baase IR methods.
"Appendix Sec. A. quantifies the prevalence of degradation related terms in VLM captions, and Appendix Fig. demonstrates how such bias can misguide restoration.
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Semantic Alignment. Preserving semantic fidelity during image restoration is significant challenge. Existing methods often rely on generating captions from degraded images via vi- linn-language models at inference time (Yu et al. (2024); Ai al. (2024); Kong et al. (2025)), which causes additional computational cost and may produce inconsistencies between training and explicitly encode degradation-relate terms, introducing mantic bias from artifacts rather than underlying content (see Appendix Sec. A.I). Alternative strate- gies employ coarse textual cues (Wu et (2024b;a)), but such signals are generally insufficient to capture fine-grained semantic content. In contrast, LucidFlux leverages a caption-free SigLIP-based based semantic alignment module that extracts features from lightly restored proxies and projects them through lightweight Connector into the text-conditioning space expected by Flux.1, 1, facilitating caption bias,
inference. Moreover, such captions
caption-free frreen guidance without additional VLM calls and avoiding degradation- and ensuring that restored outputs maintain high semantic consistency without hallucinations.
METHODOLOGY
Text
TS
CLIP
Diffusion Transformer
Dual-Branch
Restore this image...
x2
Sigl JIP
Connector
Text
Double Stream
Block
Naise
Linear
VAE
Dual- Bual-Branchh
CL
MMDIT
Conditioner
Timestep and Layer- Adaptive Modulation
Zuue Pig
MMD:T
Lightweight
Layer
Timestep- Layer-Adaptive Modulation
Restore Proxy
Index
MMDIT
duo
4
Layer
Index
Time
Lineur
110
VAE
Dual-Branch
$4a
Single Stream
Simsoidal Encoding
Conditioner
DIT Black
MLP
MLP
Timestep
SLLU
Trainable Parmmeter
VAE
LayerNorm
Meg Kear
Inq
Muliipication: addition
Sequence Concatenation
Figure 2: Overview of the proposed architecture for image restoration. Our method integrates dual condition streams (LQ and LRP) with timestep- and layer-adaptive modulation modules, and in- corporates SigLIP semantic priors through connector into Flux-based DiT backbone jointly enhance perceptual quality and semantic consistency.
Our framework is built upon a Flux-based DiT backbone, augmented with two parallel ControlNet branches. The first branch processes the original low-quality image (LQQ), while the second branch takes lightly restored version of the input (LRP) generated by lightweight restoration model. Both streams capture complementary information, which subsequently modulated through timestep- and layer-adaptive modules to align with the DiT feature space. Morcover, incorporate semantic priors extracted from SigLIP and enhanced with a connector, which are injected into the DiT layers to facilitate semantic consistency and fine-gre one-gnained texture restoration.
Practically, we eschew inference captions: Appendix Sec. A.1 quantifies that 17-24% of VLM captions introduce degradation-relatee terms, and Appendix Fig. shows such bias misguides restoration, degrading perceptual quality.
3.1 LIGHTWEIGHT DUAL-BRANCH CONDITIONER
Directly conditioning on the low-quality (LQ) image preserves high high-frequency details but often leaks residual artifacts under mixed degradations; conditioning lightly restored proxy (LRP)
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suppresses artifacts but tends oversmooth textures. Following the dual-branch paradigm proposed in (Ai et al. (2024)), we therefore decouple structure anchoring and artifact suppression into two signals and encode them with a minimal--verread conditioner that interfaces with the Flux.1 DiT backbone without duplicating large blocks.
As illustrated in the top-right of Fig. 2, only the conditioning pathway is shown for clarity, while other components such as timestep embeddings remain consistent with Flux. Throughout, the Flux.1 I backbone and VAE remain frozen for stability and efficiency. The output feature map IRPP of the LRP is computed by:
LRP LRP((
Both Lo and LeP are mapped by the shared Flux.1 VAE encoder E into latents
H xd
ZLQ = E(4o)
ØLRP = E(LRP).
2 Z
(2)
where s is the VAE downsampling factor and d the latent width. The Dual- Bral-Branch Conditioner (DBC) then converts each latent into compact conditioning tokens via two-block MMDiT applied at latent resolution,
PrQ DBC(a.))
ØLRP DBC(#LRP).
where DBC patchifies the VAE latent and projects patches into 2D-positioned sequence before two stacked transformer blocks; weights are not shared across branches, as the LQ stream emphasizes detail-pree preserving. noisc-tolerant cues while the LRP stream favors structure-first suppreeet representations. Using separate parameters avoids competing gradients and preserves branch com- plementarity, while the conditioner remains minimal (two blocks per branch, constant overhead w.r.. layer depth and far smaller than ControlNet-stylle duplication of large DiT). Intuitively, Φ car- ries detail-presst peaeall-preeevnggg yet noisy cues, whereas ØTRP provides artifact-robust structure; the subsequent timestep- and layer-adaptive condition modulation (Sec. 3.2) consumes these two complementary signals for coarse-to-fine context-aware guidance without increasing the conditioner's footprint.
3.2 TIMESTEP- AND LAYER-ADAPTIVE CONDITION MODULATION
Diffusion transformers exhibit cemporal-hierarchiee division of labor: early timesteps reconstruct coarse structures while later ones refine geeh-frequency details; similarly, shallower layers capture low-level edges and deeper layers process semantics (Park al. (2023); Qian et al. (2024)). Apply- ing identical conditioning across all timesteps and layers risks redundancy or conflict. We therefore modulate the outputs of the dual-branch conditioner (DBC) in way that adaptive both timestep t and layer index while keeping the heavy Flux. I backbone frozen. A lightweight modulation head takes sinusoidally encoded (t/T, L) and predicts feature-wise (per-channel) scale and bias for each branch independently:
I.l m
Pm
Modulation(PE((/7) 1/L)), m (TQ,LRP). and gth E IRde.
(4)
These parameters affect an AdaptiveLN-sttyle adjustment,
LG LLQ BOL + 114
t.
*LRP
Ttd
t. OLRP ØLAP + LRP
(5)
We then fuse the branches without additional normalization via feature-wist convex mixture:
1 E (0,
Cond = nt C 01.0 (1- (1- +t))) @LRP.
Predicting a/3 per channel supplies sufficient flexibility to track the backbone's roles across t and without inflating capacity, and independent modulation for LQ vs. LRP preserves their com- plementary inductive biases (detail- preseevige vs. artifact-robust). Keeping modulation inside the lightweight conditioner maintains negligible overhead while enabling coarse-to-fine, timestep- and layer-aware guidance; ablations (Sec. 4.3) show that removing either the temporal or the hierarchical dependency degrades fidelity under mixed degradations.
3.3 SIGLIP FOR CAPTION-FRED SEMANTIC ALIGNMENT
Text-to-image (T2I) diffusion models are typically conditioned on text, and many restoration meth- ods adopt captions semantic guidance (Ai al. (2024); Yu et al. (2024)). During training, such
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captions often derived from clean ground truth, yielding idealized supervision. At inference, however, only degraded inputs are available; captions generated from low-quality images tend to inherit degradation-specifi. artifacts and, when produced by large VLMs, add substantial latency and exacerbate a train-test mismatch (Sun et al. (2024)). We replace caption generation with a caption-free semantic pathway. Concretely, we extract image semantics from the lightly restored proxy ILRP using frozen SigLIP encoder and map them into the backbone's ' textual embedding space via a lightweight Connector:
2g Connector SigLIP((IRP))
The projected semantics 2, are concatenated with small set of prompt tokens (default instruction) to form the multimodal context fed to the DiT' backbone:
Context Concat(zs, c).
(8)
Grounding semantics in LRP stabilizes content under mixed degradations, while the Connector fur- nishes drop-in bridge to the cext-conditioning interface, avoiding any duplication of heavy mod- ules. This design eliminates external captions at both training and inference, reducing latency and removing major source of caption-induced semantic variance (e.g., differences across captioners or paraphrases). Grounding semantics in 2g keeps outputs structurally faithful and semantically aligned to the input.
3.4 SCALING UP REAL-WORLD HIGH- NUGH--UALITY DATA FOR IMAGE RESTORATION
Although large-scale T2I diffusion models are pretrained on hundreds of millions of image-text pairs, they are not tailored for the image restoration task of our work. Training large diffusion transformers for IR requires lask-aligned data at scale with strong structure and perceptual qual- ity. However, publicly available restoration corpora remain modest and/or lack reproducible quality control: DIV2K (800/100) (Agustsson & Timofte (2017)), Flickr2K (2,650) (Agustsson & Timofte (2017)), LSDIR ( 85K with manual curation) (Li et al. (2023)), and SUPIR (20M without dis- closed filtering criteria) (Yu al. (2024)). while DreamClear (Ai al. (2024)) synthesizes 1M pairs at substantial computational cost. This leaves a practical gap between what large DiTs need and what current datasets provide.
To bridge this gap, we introduce, to our knowledge, the first publicly documented and extensively validated IR-siiccfic data filtering pipeline. is automatic (parameters empirically set, pipeline automatic once fixed) and comprises three stages-blur screening, flat-region suppression, and perceptual-quality rapling-explicitly designed retain structure-rich, high-quality images while discarding unsuitable samples.
Data source. Our initial dataset is collected from two sources. First, we collect 2.3M images from the Internet. specifically from Pexels' and Unsplash², using their official image APIs. We query both platforms with 23,914 unique keywords extracted from the Unsplash Lite Dataset', retrieve the associated metadata, and download images via the official URLS provided. This keyword-based pipeline ensures fully reproducible data collection without bypassing platform restrictions. The full keyword list is included in the supplementary materials as keywords txt. In addition, we Caao--Conceeiiiie
incorporate 557K images from the of 2.9M candidate images. This combined pool serves the raw data for subsequent filtering.
dataset (bghira (2023)), yielding a total
noise provide unreliable structural cues and thus unsuitable for training. Following LSDIR (Li et al. (2023)), we quantify the degree of blur using the variance of the Laplacian Sptur (T) = Var ( where denotes an input image. Only images with 150 < S S 8000 (Li et al. (2023)) are retained, ef- fectively excluding both overly blurred and noisy samples. These bounds are empirically hand-tuned based on preliminary experiments and careful visual audits on a held-out subset to balance removal of extreme blur/noise while retaining legitimate shallow-depth-e sorih-offficlll field and low-light scenes.
Blur detection. Images that are heavily blurred or contain excessive
Flat-region detection. Images dominated by textureless regions may bias the model towards producing over-smoothed outputs. To mitigate this, each image is divided into non-overlapping
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CUPIQA Soore Dedrttatiee
Blar Score Ditriadits
FGR Parsertage Detriadias
0008
2.022
00039
30031
180
CUR94 Store
0hr Scnne
M400 (2000)
Figure 3: Comparison of dataset attributes. Our dataset exhibits higher CLIP-IQA scores, lower flatness, and more diverse resolutions than Flickr2K (Agustsson & Timofte (2017)) and DIV2K (Agustsson & Timofte (2017)).
240 x 240 patches, and the edge richness of each patch is measured using the Sobel operator with
Sua = Var (( + (0g1)²). Patches with Sia < 800 are considered textureless, and im- ages containing more than 50% such patches are discarded. Both the 800 patch-level threshold and the 50% image-level ratio are empirically set by manual inspection of edge-statistics distributions and visual audits; they provide conservative balance that suppresses large flat backgrounds yet preserves natural sky/water regions. This ensures that retained images exhibit sufficient edge and texture diversity, essential for high-fidelity restoration. After applying blur and flat-region filtering, 1.28M candidate images remain for the final image quality assessment (IQA) filtering stage.
IQA Filtering for High-quality Data. While LSDIR employs manual curation in its final stage, such human intervention is impractical for scaling to larger datasets. We apply CLIP-IQA to further ensure perceptual quality of our training data. The remaining images are ranked by their percep- tual scores 8;, and only the top 20% are retained, i.e. {i s; 2 quantiley (1*+)),, resulting in 257K high-quality images. The 20% cutoff is empirically chosen after careful inspection at multi- ple percentiles (e.g.. 10/20/30%), trading off perceptual quality against semantic/content diversity. By udditionally incorporating 84K high-quality samples from LSDIR (Li et al. (2023)), the final curated dataset comprises 342K high-quality images. Once these cutoffs are fixed, the pipeline ecutes fully automatically at scale. For generating paired training data, degraded counterparts are synthesized using the Real-ESRGAN degradation pipeline (Wang et al. (2021)) as implemented in (Ai et al. (2024)), across 4 epochs, producing total of 1.36M image pairs. This procedure ensures both diversity and realism in the low-quality inputs, facilitating effective model training. To assess the effectiveness of our filtered data, we randomly select 10K samples and compare their attribute distributions with existing datasets. Figure 3 shows that our dataset achieves higher CLIP-IQA scores, comparable blur scores, lower flatness values that refeect richer textures, and more diverse resolutions than Flickr2K and DIV2K. In Appendix 6, we also analyze semantic diversity using 1-SNE, and it shows that our dataset demonstrates substantially broader semantic coverage.
EXPERIMENT
4.1 IMPLEMENTATION DETAILS
We train large Flux-based generative model, LucidFlux, while frezzing all blocks the Flux back- bone and training only the task-specific modules introduced by our method. Freezing the backbone stabilizes optimization and prevents catastrophic forgetting, while concentrating capacity on the new modules that realize our objective. Under a standard Ly latent loss for velocity prediction, as commonly used in flow-match matching models (Labs et al. (2025)), training runs on 8 8XNVIDIA A800 GPUs with DeepSpeed ZeRO-2. We choose ZeRO-2 because it shards optimizer states and gradi- draa-drraayi reducing memory footprint-withee serves simple forward passes and yields higher throughput than ZeRO-3 in our setting. This enables larger activation budgets at 1024 x 1024 resolution and steady scaling with modest communication overhead. We use Adafactor (Shazeer& & Stern (2018)) with a learning rate of 100 and weight decay 0.01. The per-GPU batch size is 2 with gradient accumulation of steps. giving an effec- tive batch size of 32 across 8 GPUs. We resume our Siglip connector based on Flex. l-appha Redux checkpoint. The full training completes in approximately 7 GPU-days. Following many existing works, we employ SwinIR (Liang et al. (2021)) as lightweight restore proxy.
partitioning model parameters, which pre-
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Table 1: Quantitative comparison across different IQA metrics on RealSR (Wu et al. (2024b)), RealLQ250 (Ai et al. (2024)), DIV2K- LSDIR- Val and DRealSR.
Benchmark
ResShift
StableSR
ShnRR
DiMBR
SeuSR
DreamCCear
SUPIR
LacidFlax(Qurs)
Capdon-Free
X
CLIP-IQA+ 7
0,4655
0.3732
0,5402
0.6475
0.6257
0.4461
(1.494
0.6748
QuAlign
2,3311
2,1245
3.1354
3,0490
3,2745
2,4215
3,4720
3,6919
MUSIQ
40.795
29.6691
53.9138
60.0759
61.5222
55,1911
54.9279
DRealSR
MANIQA
0.2687
0.2402
0.3455
0.2675
0.4985
NIMA 1
4.3178
3.9048
4.6226
4.6543
4.6201
3.958
4.5063
4,9625
CLIP-IQA 1
0.4964
0.3383
0.6631
0.6781
0.6760
0.4360
0.5309
0.6879
NIQE
10.3005
8.6022
6,9800
6.1852
6.4502
7,0163
5,9091
4.7034
CLIP-IQA+
0.5005
Q-Align
3.1045
2,4087
3.3615
3.3157
3.6773
3,4622
3.7555
Real-world RealSR
MUSIQ 9
MANIQA
0.2976
49.50
0.2356
39.98
0.3753
57,95
61,7750
0.4744
0.5087
67.57
0.3092
49.48
0.3426
55.68
0.5437
70.20
NIMA
4,7026
1.3639
4,8282
1.8192
4.8957
4,4948
4.6101
5.1072
CLIP-IQA
0.5283
0.66011
NIQE
9.0674
68733
6.4682
6,7000
544944
5,2873
5.2819
4,2893
CLIP-I0++ 1
0.5529
0.5804
0.6054
0.6918
0.7034
0,6810
0,6532
0.7406
Q-Align
3.6318
3,5586
3.7451
3.9757
4,1423
4,0640
4.1347
4_3935
MUSIQ1
59.50
57.25
65.45
67,5313
70.36
67.08
65.81
73.01
RealL.Q250
MANIQA
0.3397
0.2937
0,4230
0.4899
0,4000
0.3826
0.5589
NIMA f
5.0344
5,0838
5.2397
5.3132
5,3146
5:2200
5,0006
5.4836
CLIP-IQ1 1
0.6129
0.5100
0.7166
0.7137
0.7063
0,6950
0.5767
0.7122
NIQE
6.6326
4.6236
5.4425
5.1193
4,4383
3,8700
3,6591
3.6742
CLIP-IQA+
0,5583
0,5760
0,6128
0.6973
0.7116
0,6585
0.6719
0.7492
Q-Align
3,4226
3.7336
3,8509
4.5311
MUSIQ T
60.5932
57,1246
66,0906
69 1822
71,1947
65.8187
67.9074
73,9045
MANIQA
0.3421
0.2902
0.4341
0.5015
0,5104
0A369
0.4148
0,5819
Vall
CLIP-IQA
NIMA
0.6017
5,0300
0,5002
5.1341
0.7166
5,1810
0.7143
5.1941
0.7149
5.2709
0,6663
161663
0.5818
5.1516
5.4834
0,7034
NIQE
6.1976
4,9810
5.3679
PSNR
1833072
3369
180956
20,0389
18,2529
17,5701
17.7567
4393
SSIM
0.4994
0.4819
0.4259
0.5242
0.4684
0.4291
0 0.4482
3837
Synthetic
LPIPS
0.3738
0.3933
0,3919
0.3582
0.3497
0,3621
0.3785
0.4312
CLIP-IQA
0.5248
0.5576
0,5582
0,6977
0.7258
0.6995
0.7126
0.7440
Q-Align
3.5317
34878
3.7095
4,2591
4.3468
4,5959
MUSIQ T
$7.6991
07.888
63.9586
4690
72,0142
70.7186
74.1923
MANIQA 7
0.3408
0.2990
0,4151
0.5356
0.5529
0.5059
0.4822
0,5979
LSDIR-Val
NIMA 1
CLIP-IQA
5.0916
0,5691
0.4991
5.0628
0.6766
5.3353
0,7066
5.3566
5,4245
0.7314
0,6941
5.3773
5. S.3692
0.6105
5.6221
0.6836
NIQE
6.4447
1.2101
5.1771
3.5571
PSNR
17.3040
17,1480
16.8241
188862
17/0782
16:2114
16.1598
14 363688
SSIM
0.3935
0.4036
0.3710
0.4652
0.4113
0.3823
0 0.3636
34997
LPIPS
0.4824
0.4655
0.4637
0.4344
0.3969
0.3720
0144088
0.4148
4.2 COMPARISON WITH STATE-OF-THE-ART METHODS
We evaluate our approach against several state-of-the-ar diffusion-based methods, including ResShift (Yue et al. (2023)), StableSR (Wang et al. (2024a)), SinSR (Wang et al. (2024b)), Diff- BIR (Lin et al. (2024)), SeeSR (Wu et al. (2024b)), SUPIR (Yu et al. (2024)), and DreamClear (Ai et al. (2024)). Following many existing works (Wang et al. (2024a); Wu et al. (2024b); Ai et al. (2024); Yu et al. (2024)), experiments are conducted on both synthetic and real-world benchmark datasets. For the synthetic data, we randomly crop 2,4 patches from the validation sets of DIV2K (Agustsson & Timofte (2017)) and LSDIR (Li et al. (2023)). For DIV2K, we use the five original degradation types: bicubic, unknown, mild, difficult, and wild. LSDIR- Val is generated by applying the same degradation pipeline used during training. For the real-world data, we adopt center-cropped images from RealSR (Cai al. (2019)), DRealSR (Wei et al. (2020) used in (Wu et al. (2024b)) and ReaILQ250 (Ai et al. (2024)). All evaluations are performed at a resolution of 1024 x 1024, with all super-resolution methods using an upscale factor of 4.
Metrics. We evaluate all methods using seven no-reference image quality assessment metrics, in- cluding CLIP-IQA+ (Wang et al. (2023)), Q-Align (Wu al. (2023)), MUSIQ (Ke et al. (2021)), MANIQA ( (Yang et al. (2022)), NIMA (Talebi & Milanfar (2018)), CLIP-IQA (Wang et al. (2023)), and NIQE (Zhang et al. (2015)). as well as three reference-basee metries including PSNR, SSIM (Wang et al. (2004)), and LPIPS (Zhang et al. (2018)). These metrics assess the restoration perfor- mance across perceptual quality, semantic alignment, and structural fidelity.
Qualitative Comparisons. Figure 4 presents visual comparisons on representative samples from R250 SeeSR and DreamClear reduce some degradations but tend to leave residual artifacts or produce oversmoothed outputs with limited texture recovery. SUPIR generates cleaner results yet often loses fine details, leading to overly smooth surfaces. In contrast, our method achieves clearer edges, richer textures, and better semantic consistency with the degraded inputs, especially patterns. These qualitative observations
in challenging regions such as hair, text, and align with the quantitative results in Table 1, further confirming the effectiveness of our approach.
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LQ Ingut
SHSRtt
SASSR
SUPIR
DecanC Tcar
InstPPlssDaass
Figure 4: Qualitative comparisons on R250L Baseline methods either leave noticeable arti- facts or yield over-smoothed textures, while approach restores sharper details. See Figure 9 to Figure 14 in Appendix for more visual comparisons.
Quantitative Comparisons. Table I reports the IQA metric results on real-world and synthetic benchmarks. Our method consistently outperforms prior approaches on perceptual and semantic- oriented metrics, such as CLIP-IQA+, MUSIQ, MANIQA, Q-Align, and NIMA, highlighting its ability to generate visually faithful and semantically aligned restorations. On real-world datasets (e.g., DRealSR, RealSR, Rea0),Q250), LucidFlux achieves clear gains over existing caption- or tag- based methods. For distortion-focusee measures like PSNR and SSIM on synthetic datasets, prior approaches report slightly higher values, yet these metrics are widely recognized as being less cor- related with human perceptual quality. In contrast, our method delivers state-ofttheaa performance on modern IQA benchmarks, supporting the view that advanced IR frameworks should be evaluated with perceptual and semantic quality measures rather than traditional distortion metrics.
Runtime and Model Scale Comparison. We compare LucidFlux with SeeSR, SUPIR, and Dream- Clear terms of runtime and model size in Table 3. Despite using a substantially larger backbone (12B), our LucidFlux achieves competitive total runtime by eliminating the caption preprocess- ing. In contrast, SeeSR, SUPIR, and DreamClear require additional preprocessing and rely on smaller backbones (1. 29B,, 3.5B, 0.6B), resulting higher latency relative to their size. For trainable adapters, LucidFlux maintains a balanced design (1 (I.6B), outperforming SUPIR (1.3B) in represen- tational capacity while remaining more efficient than DreamClear (2.2B).
Comparison with Close-Source Commercial Methods. We further compare our LucidFlux with several widely used commercial image restoration solutions, including HYPIR-FLUX (Group (2025)), Seedream 4.0 (ByteDance Seed Vision Team (2025)). Topaz (Labs (2025)), Gemini- NanoBanana (DeepMind (2025)), and MeiTu SR (MeiTu (2025)). For clarity, these baselines consist of (i) specialized SR/restoration products (HYPIR-FLUX, Topaz, MeiTu SR) that users rely on in practice, and (ii) unified generative or multimodal systems (Seedream 4.0, Bemini-NanoBanana) that also expose restoration/upscaling interfaces and can handle the same degraded inputs. We in- clude the latter not as dedicated IR baselines, but to contextualize LucidFlux against the currett generation of widely deployed unified commercial models. All evaluations same experimental settings, and the identical IQA metrics are used as in the open-source compar- isons. Table 2 reports the quantitative results of different methods. Our LucidFlux achieves the
conducted under the

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Published as a conference paper at ICLR 2026
Table 2: Quantitative comparison across different IQA metries with commercial models on RealLQ250.
Method
CLIP-IQA+
Q-Align
MUSIQ
MANIQA
NIMA 1
CLIP-IQA
NIQE 4
LQ Input
0.6218
2.1693
44.1541
0.3718
3,8664
0.6079
6.0790
Seedream 4.0
0,5002
3.6931
$2.3771
0.2794
4.7024
0.4124
4.9393
Bannna
0.3780
3.3114
44.6310
0.2548
4.6571
0,4434
6.0865
MclTu SR
0.6653
4.1464
66.5936
0,4498
5.2103
0.6663
5.4125
LucidFlux (Ours)
0.7406
4.3935
0.5589
5.4836
0.7122
3.6742
Table 3: Runtime (8) and parameter scale (B).
Table 4: Ablation study on RealLQ250. Evaluation
SeeSR
SUPIR DreamClear
LucidFlux
metrics for three main contributions of our method.
Caption (s)
0.100
5,900
8,700
0.012
Setting
CLIP-IQA
CLIP-IQA-
MUSIQ
Inference (8)
22.380
16.600
28,900
23.612
Dual Branch Conditioner Only
0.585
0.609
Total (S)
22,480
22.500
37,600
23.612
Sigl Alignment
0.600
0.620
62,000
Backbone (B)
0.6
-LCM
0.622
0.635
65.500
Adapter (B. train.)
1.6
1.3
2.2
1.6
.Lrrg HQ Data (Our methodl
0.7122
0.7406
73,0088
Total (B)
2.89
3.9
2.8
13.6
largest scores across all metrics and outperforms other commercial solutions. MciTu SR shows the best performance among compared methods, but its restoration results generally have less details than our LucidFlux. In contrast, our method balances strong quantitative performance with reliable and consistent restoration, which makes it particularly suitable for real-world applications. See our Appendix Figure 8 for qualitative comparisons.
4.3 ABLATION STUDY
We ablate our three contributions in testing RealLQ250 and report the quantitative results in Table 4. Starting from the Dual-Branch Conditioner (DBC) trained on LSDIR, our CLIP-IQA CLIP-IQA+ / MUSIQ scores are 0.585/0.660 100.0600961...0 1,82,, and all three scores are improved after adding caption- free SigLIP semantic alignment. Our timestep- and layer-adaptive condition modulation (TLCM) further improves score performance, and scaling to our curated large-scale high-quality data provides the largest jump over TLCM on three metrics. The progression indicates that SigL.P alignment stabilizes semantics; TLCM exploits the DiT hierarchy; and data curation supplies structure-rich supervision, and thus all three modifications on DBC are required for the final outcome.
Caption-Free vs. Saptton-BBseed Semantic Pathways. To isolate the effect of the caption-free design, we evaluate three variants built on the same Flux.1 backbone: (i) GT captions (LLaVV on the ground-truth clean images), (ii) VLM captions (LLaVA the lightly restored proxy), and (iii) caption-free free (ours) using the SigLIP semantic pathway without captions. Full quantitative results and qualitative comparisons are provided in Appendix A.3.
5 CONCLUSION
LucidFlux demonstrates that caption-frre photo-realistic image restoration is best achieved by when, where, and what to condition large diffusion transformer, rather than by adding parameters prompts. A lightweight dual-branch conditioner-groune restored pnoxy-and timestep- and layer-adaptive modulation schedule recover high- frequecyy detail while preserving global structure and suppressing artifacts, all with a frozen Flux. backbone. SigLIP-based semantics provide training-inference consistency without captions. To make post- training practical, we introduce, to our knowledge, the first publicly documented and extensively validated IR data- tata-filtering pipeline. It fully automatic once syper-parametert are fixed and scales to 342K high-quality images and 1.36M paired samples, supplying structure-rich supervision at the capacity needed by large DiT's. Across real and synthetic benchmarks, LucidFlux delivers state- of-the-art perceptual quality and semantic fidelity with competitive runtime and minimal trainable overhead. We hope the pipeline, data recipe, and design insights provide reliable foundation for restoration in the wild, and inspire future work learned data selection, fidi extensions, and higher-resolution backbones- all while retaining caption- free inference.
in the degraded input and a lightly
Acknowledgmentss This work is supported by the Guangdong Science and Technology Department (No. 20242DZX2004), the Nansha Key Area Science and Technology Project (No. 2024ZD006), and the National Natural Science Foundation of China (Project No. No.82572383).
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A APPENDIX
A.1 LIKELIHOOD OF IENLIDATIOO--LLATEE TERMS IN CAPTIONS GENERATED BY DIFFERENT MULTIMODAL LARGE LANGUAGE MODELS
When using captions from VLM as semantic guidance for restoration tasks, a potential risk that these models may unintentionally introduce degradation-relater terms (e.g., blur, noise, or low reso- lution). Such bias can mislead the restoration model by attributing degradations to input images even when they are not visually apparent. To quantify this effect, we evaluate the occurrence of degradation-rrlated descriptions in captions generated by a set representative VLMs on Re- alLQ250, specifically LLaVA-v1.6- 1.6--Viuna--la 13B (Liu et al. (2024)) and Qwen2.5-VL 1-------B--nnneeee (Team (2025)). Each caption is produced using the same prompt as DreamClear (Ai et al. (2024)), i.e., "describe the key subjects and style", which is designed to neutrally guide the model to- ward content description without explicitly emphasizing or suppressing degradation cues. We then employ Geemin-22.5-Flas--Imm tain degradation-related mentions, Each caption is processed with structured instruction to extract and categorize any degradation-related terms.
external evaluator to analyze whether captions
Prompt A.1 (Identifying Quality Degradations in Image Captions)
You professional image quality analysis expert. Carefully analyze the following image de- scription and identify any image quality issues that either explicitly mentioned implicitly
implied.
Image description i con.contnt]
(capt
Your task is to identify quality issues mentioned in the description. Focus on:
Sharpness issues such as blur, unelear details, defocus
Noise, grain, artifacts
Low resolution, compression traces, or general quality problems
Overexposure, underexposure, or color distortion
Physical damage such as scratches, stains, aging
Return results strictly in the following JSON format, without any additional explanation or text:
caption_content
"degraddation_keyywwoe ["Ext degradat on-related terms "]
" bttggraaddtion__ttettrooe
"Blur-relatedd: ("blur,, "unclear", "defocus"),
"Noise-related": ["ncise", "grain", "artifacts"),
"quality-related": ("resolution", "compressicn"), "Exposure-related":; ["overexposure", "underexposure", color issues"), "Damage-related": ("damage", "stains", "aging"]
"degradationnscove :acriiionssore::::__ 0.0,
sseverity_level" /aatt///ooo//ooddaeeeeeeee
"primary_issues" (MMiin issue types"), "analysis sumbary"__mm "Briei analysis summary"
Scoring standard:
degradation score: 0 means no degradation, 0.3 minor, 0.3-0.6 moderate, 0.6 severe no quality issues are mentioned in text, set arrays empty, score severity _leeel= "None" Important: Only return pure JSON format results, without markdown code blocks or extra commen- tary.
If a caption contains terms that explicitly refer to image degradations, such as blur, noise, low resolu- tion, or compression artifacts, we consider it a degradation- relatee caption. Table presents the like- lihood of captions containing such degradation-relate terms. We observe that LLaVA-vI. 13b produces degradation captions in 17% of cases, whereas Qwen2.5-VL. B.-------B--Inteeee ex- hibits higher occurrence of 24%. This indicates that different VLMs vary in their tendency to
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Table 5: Occurrence rates (%) of degradaatio--rlltte terms in captions generated by different VLMS on R255LQ250.
Model
VL Vicuna- 13B
Owen2.5-VL-7B-Imnte
With Degradation (%)
17
24
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Informe
Infarase dognial
Infaves: degoakd cezice
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24LC515
24LC515
24LC515
24LC515 I/PHR& 0227
24LC515 IPHRE 0227
I/PHR6 0227
MPHR& 0227
1/PHR&
0227
0222
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Figure 5: Impact of captions with and without degradation descriptions on restoration results. The second to fourth columns illustrate that inconsistent captions generated by the same VLM across different runs lead to variations in the restoration outcomes. The fifth and sixth columns show that captions containing explicit degradation descriptions misguide the restoration model and result in inferior quality compared with captions focusing purely on content and style.
introduce degradation cues into captions, which may potentially bias downstream restoration tasks these captions are directly used as supervision.
A.2 IMPACT OF DEGRADATION-RELATTA CAPTIONS ON MODEL RESTORATION
To further investigate the influence of degradation-rreltted descriptions in VLM-generated captions on restoration performance. we conducted experiments using two types of captions generated by M..VA-v1.-vvieuna 13b. The first type uses the prompt "Describe the key subjects and style" to generate captions without emphasizing image degradations, while the second type uses the prompt "Describe the key subjects and style, retain the descriptions of degradations on the image" to pro- duce captions that explicitly include iegradation-relatee content.
As shown in Figure 5, two patterns emerge from the qualitative results. First, captions generated by the same VLM using the neutral prompt exhibit variability across multiple runs, resulting in differ- ences in the restoration outputs for the same input image. Second, when captions explicitly include degradation-relan related descriptions, the model's restoration performance is adversely affected, produc- ing outputs of lower perceptual quality compared with captions that focus solely on key subjects and style. These findings indicate that both the consistency and content of VL.M-geneated cap- tions can significantly influence downstream restoration performance, underscoring the importance of controlling for degradation-relatee content when employing such captions guidance.
These observations further highlight the practical limitations of relying on VLM-generated captions during inference. The variability in captions leads to inconsistent restoration results, the presence of degradation-related descriptions can mislead the model and reduce output quality, and generating captions introduces additional computational overhead. Together, these factors underscore the ad- vantages of a caption-fre free approach, which avoids reliance on potentially inconsistent or misleading textual guidance while reducing inference cost and maintaining robust restoration performance.
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Table 6: Ablation on caption usage under fixed Flux.1 backhone RealSR. GT captions
generated using high-quality reference images: VLM captions are generated from the low-quality inputs at inference time. Latency refers to additional inference overhead.
Method
CLA+
Q-Align
MUSIQ
MANIQA
NIMA
CLIP-IQU
NIQE
Latency
GT caption (LLaVA on GT)
0,7111
3,8713
70.1654
0.5686
5.0632
0,7032
4,6775
+10.426
VLM cuption (LLaVA LRP)
0.7060
388168
69.4371
0.5533
5.0475
0.7030
4.6249
+10.057s
Ours (captionfrree) (ree)
0.7074
3.7555
70.2005
0.5437
5.1072
0.6783
4.2893
+0.012
Table 7: Comparison of different backbone update strategies Reall @250..
Training Strategy
CLIP-IQA 1 NIQE
MUSIQ
Memory
Attention-only Fine-Tuning
0.654
3.52
66.27
60.30 GB
Full Fine- Munn-Tunigg
0,594
3.796
63.30
76.16 GB
Ours
0.7122
3.6742
73.01
76.53 GB
A.3 ABLATION ON CAPTION USAGE FOR CLEAN ATTRIBUTION OF THE CAPTION-FREE DESIGN
To isolate the effect of our caption-frre formulation, we evaluate three variants that share the same Flux. I backbone and differ only in their use of captions: (i) captions generated by LLaVA from the ground-trute reference image, (ii) captions inferred from the lightly restored proxy as used at test time, and (iii) our caption-free variant. As shown in Table 6, both caption- settings introduce 10 seconds per image due to VLM processing. Moreover, even the idealized GT captions do not translate into superior image quality-the caption-free model achieves the best MUSIQ (70.2005), NIMA 1072), and NIQE (4.2893), while VI.M captions further degrade performance across all metrics. These results confirm that performance improvements do not stem from captions but from a more robust caption-free free design, which also avoids substantial inference latency and external de- pendencies.
A.4 COMPARISON OF BACKBONE UPDATE STRATEGIES
We compare different strategies for updating the DiT backbone, including attention-only updates, full fine-tuning of all parameters, and our frozen-bace backbone adaptation. As shown in Table 7, updat- ing only the attention layers results in stable training but reaches performance ceiling (CLIP-IQA 0.654 / MUSIQ 66.27), suggesting that adapting cross-attention alone is insufficient for handling real-world degradations. Full fine-tuning increases the model's adaptability but can cause catas- trophic forgetting, leading to reduced perceptual quality (CLIP-IQA 0.594 MUSIQ 63.30). Our approach obtains the best perceptual performance (CLIP-IQA 0.7122 MUSIQ 73.01) while main- taining stable optimization. Although its memory footprint is similar to full fine-tuning, it avoids destabilizing the generative prior and therefore offers a more favorable balance between performance and training reliability.
A.5 ADDITIONAL RELATED WORKS
Large-Scale Image Restoration Datasets. The availability of large, high-quality datasets is critical for training generative restoration models Chen et al. (2024; 2025); Lin et al. (2025b). Existing datasets exhibit notable limitations: LSDIR (Li et al. (2023)) provides 85K images but depends on manual filtering, SUPIR (Yu et al. (2024)) collects 20M images without disclosing quality con- trol procedures, and DreamClear (Ai et al. (2024)) generates 1M images via SDXI. fine-tuning at cost of 1280 V100 GPU days. To overcome these constraints. LucidFlux employs a fully auto- mated three-stage filtering pipeline integrating blur detection, flat-region detection, and perceptual quality assessment. This approach produces diverse, structurally rich datasets that reproducible, scalable, and suitable for training billion- parameeer diffusion backbones efficiently.
Transformer-based T2I models (DiTs). Recent text-to-image systems increasingly adopt Transformer backbones- either diffusion transformers (DiTs) or rectified-flow transformers (RFTs)-whiel scale well and capture long-range dependencies in latent space (Peebles & Xie
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(202)); Ye et al. (2025); Chen et al. (2026)). Stable Diffusion 3 (SD3) introduces Multimodal Diffu- sion Transformer (MMDiT) with separate weights for image and text tokens and bidirectional infor- mation flow; is trained with rectified flow and improved noise sampling biased toward perceptually relevant scales, yielding stronger text comprehension and typography (Esser et al. (2024)). Pix proposes efficient DiT recipe-three sereeethrreeseee -trreestage stage training (pixel dependency, text-image alignment, waw--ccattonnnnggccceeeee
aesthetics), injecting cross-attention into DiT, and dense torealistic quality at fraction of typical compute (Chen al. (2023a)). FLUX (Labs (2024)) scales rectified-flow Transformer (rather than diffusion transformer), with open- nen-weight variants (e.g., dev/schnelll built around cross-attention over text embeddings. Building on this line, Lucid- Flux leverages large MM-DiT backbone (Flux.1) and specializes conditioning for caption-free restoration, improving detail fidelity while preserving semanties.
1024px pho-
A.6 EXTENDED DATASET ANALYSIS
To further examine semantic diversity, visualize the CLIP image text embeddings using t-SNE. We randomly sample 10K images from our filtered data, while using all available images from Flickr2K and DIV2K. As shown in Figure 6, our dataset spans substantially broader semantic range, reflecting richer and more diverse image-text concepts. This confirms the advantage of our dataset supporting models that rely on wide semantic generalization.
Figure 6: t-SNE visualization of CLIP image-text embeddings. Our dataset covers a broader se- mantic range than Flickr2K and DIV2K, indicating richer image-tex text diversity.
A. ANALYSIS OF FILTERING SETTINGS AND SEMANTIC COVERAGE
In this section, we report the sample sizes resulting from different filtering configurations and an- alyze their corresponding semantie coverage. The filtering pipeline applied sequentially in the following order: blur detection, flat-region detection, and IQA filtering.
For the baseline setting, we first apply blur detection by retaining images with Laplacian variance in the range 150 < Sbtur < 8000. Next, flat-region detection excludes images containing more than 50% textureless patches, where a patch is considered textureless if its gradient variance satisfies
800. Finally, we retain the top 20% of images according to their CLIP-IQA scores. After applying these three steps, 234,287 samples remain in the baseline configuration.
Saar Var /(0,1)²+00,
Table 8 summarizes the number of retained samples for each threshold configuration. It can be observed that stricter thresholds (e.g., Blur Variant (Set 1). Flat Variant (Set 3), and CLIPIQA Variant (Set 5)) reduce the sample size, whereas more relaxed thresholds (e.g., Blur Variant (Set 2), Flat Variant (Set 4), and CLIPIQA Variant (Set 6)) increase it.
Semantic Coverage Analysis. To evaluate semantic diversity and coverage under different con- ditions, we randomly sample 1k, 5k, and 10k images from each configuration and visualize their embeddings using t-SNE, as shown in Fig. 7. The results demonstrate that under the same 10k sample size, different filtering settings yield largely similar semantic coverage. As shown in the t-SNE visualization, the distributions of baseline and variant settings (Blur Variant, Flat Variant, and CLIPIQA Variant) exhibit only minor differences in semantic diversity, indicating that threshold variations have limited impact the overall semantic coverage when the sample size is fixed. On
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Table 8: Sample Size After Filtering for Different Settings
Setting
Threshold
Sample Number
Pool
None
2,900,747
Our Baseline
Blur 150-8000, Flat 800, 50%, CLIPIQA 20%
234,287
Blur Variant (Set 1)
Blur 200-6000, Flat 800, 50%, CLIPIQA 20%
219,966
Blur Variant (Set 2)
Blur 100-10000 Flat 800, 50%, CLIPIQA 20%
245,090
Flat Variant (Set3) 3)
Blur 150-8000, Flat 1000, 40%, CLIPIQA 20%
163,482
Flat Variant (Set
Blur 150-8000, Flat 600, 60%, CLIPIQA 20%
301,637
CLIPIQA Variant (Set 5)
Blur 150-8000, Flat 800, 50% CLIPIQA 10%
123,007
CLIPIQA Variant (Set 6)
Blur 150-8000, Flat 800, 50%, CLIPIQA 30%
330,618
Table 9: Image quality metrics under different filtering settings.
Higher is better for all metrics
except NIQE, where lower is better.
Setting
CLIP-IQA+ t
Q-Align f
MUSIQ 1
MANIQA f
NIMA
CLIP-IQA
NIQE 4
Our Baseline
0.7406
4.3935
73.0088
0,5589
5.4836
0.7122
3,6742
Blur Var. (Set 1)
0.7023
4.3041
71.3748
0.5057
5,4614
0.6622
3,4348
Blur Var. (Sct 2)
0.7072
4.2464
71.8887
0.5140
5,5129
3,8099
Var. 3)
0.7011
4.2164
71.3910
0.5161
5,4096
0.6573
3.6507
Flat Var. (Sct
0.6913
4,2795
70.2861
0.4973
5.4570
0.6584
3.3471
CLIPIQA Var. (Set
0.6749
3,9777
69.8807
0.4827
5.2303
0,6226
3,5555
CLIPIQA Var. (Ser
0.6897
4.2487
70.3159
0.4891
5.3737
0.6418
3.4848
the other hand, under the same baseline setting, increasing the sample size from Ik 5k 10k leads to clear expansion of semantic coverage. The t-SNE plots show that as the sample size grows, the clusters broaden substantially, covering wider range the semantic space. This demonstrates that sample size plays more significant role than specific threshold settings in enhancing the diversity and robustness of the dataset.
These findings confirm the stability and effectiveness of our data curation strategy, showing that semantic coverage remains consistent across different filtering configurations when sample size is fixed, while being substantially improved by increasing the number of samples.
A.6.2 IMPACT OF FILTERING SETTINGS ON IMAGE QUALITY METRICS
We evaluate the resulting datasets under each filtering configuration using seven no-reference im- age quality assessment metrics: CLIP-IQA+, QAlign, MUSIQ, MANIQA, NIMA, CLIP-IQA, and NIQE. As summarized in Table 9, our baseline configuration yields the highest scores across nearly all metrics, confirming its effectiveness in selecting high-quality, visually coherent images.
Stricter shresholds-such as Blur Variant (Set 1), Flat Variant (Set 3), CLIPIQA Variant (Set 5) 5)-generllly reduce quality scores, particularly in QAlign and MUSIQ, which are sensitive to semantic clarity and structural detail. For example, selecting only the top 10% by CLIP-IQA (Set 5) lowers QAlign from 4.39 to 3.98 and MUSIQ from 73.01 to 69.88, indicating a loss of diverse but still high-quality content. Conversely, relaxed thresholds-like Flat Variant (Set 4dma more texture-pont poor or blurry images, resulting in decreased MANIQA (0.4973) and higher NIQE (3.35), reflecting reduced perceptual fidelity.
Notably, even when CLIP-IQA not the evaluation metric, the baseline maintains strong perfor- mance across all indicators, suggesting that the multi-stage filtering strategy enhances overall image quality beyond the selection criterion alone. These findings, together with the semantic coverage analysis, demonstrate that the baseline offers well-calibrated trade-off between data quality, diver- sity, and scale.
A.7 EXTENDED VISUAL COMPARISONS
To provide a more comprehensive evaluation, we present extended qualitative results across all benchmark datasets. Figures 9-14 include representative examples from RealL.Q250 DRealSR,
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Figure 7: t-SNE visualization of semantic coverage under different configurations. The first row displays the baseline setting with sample sizes of 1k, 5k, and 10k (left to right). The second and third rows show variant settings at 10k samples: Blur Variant Set (2nd row, 1st col), Set 2 (3rd row, 1st col); Flat Variant Set (2nd row, 2nd col), Set 4 (3rd row, 2nd col); CLIPIQA Variant Set 5 (2nd row, 3rd col), Set 6 (3rd row, 3rd col).
RealSR. DIV2K-Val, and LSDIR-Val. These comparisons consistently demonstrate that our method produces sharper edges, more faithful textures, and better preservation of semantic structures com- pared with existing open-source state-of-the of-the--rrttt approaches. The additional results further corrobo- rate the advantages of our approach observed in the main paper.
A.8 VISUAL COMPARISON WITH CLOSE--OORGG COMMERCIAL METHODS
Figure illustrates representative visual results on RealLQQ50.. HYPIR-FLUX and Seedream 4.0 fail to fully remove degradations, leaving noticeable residual artifacts. Topaz suppresses degra- dations more effectively but generates flat and over-smoothed textures. Gemini-NanoBanana pro- vides visually plausible outputs but often struggles to recover high-frequency details. MeiTu SR shows relatively strong restoration ability, producing sharper and more natural results compared with most commercial counterparts. Among the evaluated models, LucidFlux consistently delivers the sharpest structures and most faithful details, particularly in fine-grained regions, while maintain- ing high structural fidelity and reliability.
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A.9 INFERENCE DETAILS
For all experiments, we use the FlowMatch Euler sampling introduced for SD3''s rectified-flow for- mulation and implemented in Diffusers' www.VaichhfuerrDssreeeehhheeee sample FLUX (Labs (2024)) and inherit the adaptive shift adjustments from the official Flux im- plementation, ensuring stable sampling dynamics and consistent step-wise updates, All inference is performed with 28 sampling steps in FP16 precision and utilizes the wavelet color alignment method fro (Ai et al. (2024)) and the full default instruction is restore this image into high-quality, clean, high-resolution result.
(Esser et al. (2024)) to
A.10 LIMITATIONS
While LucidFlux attains strong perceptual quality and semantic fidelity, several practical limitations remain:
Large model scale. LucidFlux is built high-capacity DiT backbone (Flux.1), This provides rich generative priors but entails substantial parameter count and compute cost. Training generally requires multi-GPU setups; for inference, high-end GPUs are preferable maintain reasonable throughput.
Inference GPU memory. VRAM usage during inference is sizable and grows with input resolution and batch size. Transformer- based diffusion exhibits quadratic attention complexity with respect to token count, so higher resolutions can quickly amplify memory pressure. This constrains deploy- ment on memory-limited devices unless tiling or resolution reductions are used.
Sampling steps vs. quality. High-quality outputs typically require more than 515 denoising steps. Fewer steps may lead to over-smoothing and loss of fine textures. This introduces a latency-quality trade-off that can be restrictive for real-time or interactive applications.
Mitigations. Promising directions include model compression (distillation/pruning)) low-precision inference, memory-efficient attention, and step reduction via progressive distillation. These opti- mizations are orthogonal to our method and could reduce compute and memory while preserving quality.
A.11 ACKNOWLEDGMENTS
We thank the maintainers and communities behind Flux.1 (Labs (2024)), PixArt-o (Chen et al. (2023a)), SD/SDXL (Rombach al. (2022); Podell et al. (2023)), SigLIP, and the dataset providers for LSDIR, DIV2K, Flickr2K, RealSR, and DRealSR for making their resources available to the research community.
Intended application scope. LucidFlux is designed for benign restoration of non-sensitive, natu- ral photographic images with mixed, unknown degradations (e.g., sensor noise, motion blur, com- pression). Appropriate uses include consumer photo enhancement, archival preservation, academic research, and benchmarking under unknown degradations. The model aims to improve perceptual quality while preserving semantics, but as a generative diffusion system it may synthesize plausible details not present in the input.
Out-of-scope scenarios. The method is not intended for domains that require pixel-accurate fi- delity or expert supervision (e.g., medical imaging, scientific microscopy, satellite/remote sensing, or legally binding forensic evidence). It is also not tailored for document restoration or OCR-critical text recovery.
Prohibited discouraged uses. LucidFlux should not be used circumvent privacy, safety, or consent-succ as deblurring or enhancing faces, license plates, or personally identifiable content for surveillance re-identification; removing watermarks intentional obfuscation; fabricating or altering imagery for deception; or generating identity-sensitive content (e.g., deepfakes). When restoration might affect downstream decisions about people, human oversight is required.
Operational caveats. Because performance depends on degradation type and sampling steps, out- puts should be reviewed before downstream use, especially in safety-critical regulatory contexts.
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HYPIR-FLUX
SeeSrear
Genitti NatoBaiaia
LacddFasrChrs
Figure 8: Qualitative comparison with commercial models on Reall @250.
If exact visual truth is required, classical reconstruction baselines or domain-specific methods with uncertainty quantification are preferable.
A.12 LLM USAGE
We used large language models solely for editorial assistance- to refine grammar and phrasing, improve clarity and flow, and condense overly verbose passages. No ideas, methods, code, figures, citations, results were generated by LLM, and unverifiable content introduced. All technical content, study design, experiments, analyses, and conclusions were conceived, executed, and validated by the authors, who take full responsibility for the manuscript.
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LO
Sinst
SeeSR
SUPIR
Dream Clem Clear
LucidFlux
Figure 9: More examples visual comparison with open-source state-of-the-art methods on Re- alLQ250.
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LQ Input
SinSR
SeeSR
SUPIR
DreamClear
LucidFlux
Figure 10: More examples of visual comparison with open-source state-of-the-ar methods on Re- all.0
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+
トオ
LO Minnn
K Ko
ススス
SinSR
SceSR
SUPIR
OOT
Dream Clear
LucidFlux Flux
Figure/ 11: More examples of visual comparison with open-source state-of-the-ar methods on DRealSR.
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LQ
Input
SinSR
SecSR
SUPIR
Dream SeeamClar Clear
Figure 12: More examples of visual comparison with open-source state-of-the-ar methods on Re- aISR.
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LQ
Input
SinSR
SeeSR
SUPIR
DreamClea
LucidFlux
Ground Truth
More examples of visual comparison with open-source state-of-the-art methods on Div2k- Val.
Figure 13:
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LQ
Imput
GRE
SinSR
SeeSR
SUPIR
Dream DreamClear Clear
LucidFlux
Ground Truth
Figure 14: More examples of visual comparison with open-source of-te-of-thearr methods on LSDIR- Val.
28


Reviews:
[{"review_id": "EEq8otauL6", "reviewer": "Reviewer_aM2Z", "summary": "This paper proposes LucidFlux, a caption-free UIR framework that adapts the large diffusion transformer Flux.1 without relying on image captions.", "questions": "1. The paper positions itself by claiming that UNet-based diffusion priors in UIR often lead to oversmoothing, hallucination, or semantic drift. However, numerous recent and successful UIR methods utilize DiT-based architectures. Could the authors clarify the specific limitations of these existing DiT-based priors to more solidly justify the necessity of their proposed approach and distinguish it from this relevant body of work?\n2. The proposed dual-branch conditioner appears functionally similar to the conditioning mechanism in DiffBIR, which also utilizes a restored image proxy. Could the authors elaborate in detail on the fundamental technical and conceptual differences between their conditioner and prior arts, clarifying the specific novelty and advantages introduced by their dual-branch design?\n3. The timestep- and layer-adaptive modulation is presented as a key contribution. Given that adaptive feature modulation is a well-established technique for injecting conditions in diffusion and other generative models, what is the specific innovation in your implementation? Please clarify how your method differs from and advances upon this common paradigm.\n4. Why not compare DiffBIR across all experiments?\n\nMinor Suggestion:\n1. It is recommended to use \\citep rather than \\cite.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 2, "strengths": "1. The paper is well-written, the results are great,  and the experiments are solid.\n2. A lightweight dual-branch conditioner is incorporated that injects signals from both the degraded input and a lightly restored proxy.\n3. A novel timestep- and layer-adaptive modulation schedule then routes these conditioning cues throughout the backbone's hierarchy. \n4. To ensure semantic alignment without the latency and instability of text prompts or MLLM captions, caption-free alignment is enforced using SigLIP features extracted from the proxy image.", "weaknesses": "1. It is claimed that \"Universal image restoration (UIR) aims to recover images degraded by unknown mixtures while preserving semantics—conditions under which discriminative restorers and UNet-based diffusion priors often oversmooth, hallucinate, or drift.\" However, there are lots of DiT-based diffusion priors in UIR. This main challenge is not solid.\n2. As for the first contribution, \"a lightweight dual-branch conditioner\", it seems very similar to DiffBIR. The main difference is that DIffBIR only uses a lightly restored proxy as conditions.\n3. As for the second contribution, \"timestep- and layer-adaptive modulation\", it is also a common technique for injecting conditions.\n4. \"A SigLIP-based module preserves semantic consistency without prompts or captions.\" The motivation is great but the contribution is minor.", "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": "CzB62nPtNi", "reviewer": "Reviewer_V2EC", "summary": "The authors propose LucidFlux, a Flux.1 diffusion transformer adapted to the universal image restoration task. \n\nStarting from a frozen Flux.1 backbone, four trainable modules are added which are trained on 1.36 million LQ-HQ image pairs. These image pairs are synthesized using the Real-ESRGAN degradation pipeline, starting from 342k HQ images which the authors in turn obtain by applying a proposed filtering pipeline to 2.9 million candidate images.\n\nThe proposed LucidFlux model obtains impressive qualitative results and performs well compared to recent open- and closed-source methods.", "questions": "- In Section 3.4 you write \"First, we collect 2.3M images from the Internet\". This is quite vague indeed? Could you give more details on how and from which sources you collect these images?\n- Perhaps I'm missing something obvious, but what loss is used to train the model?\n\n\n\n\nMinor things:\n- Duplicate reference of \"Eirikur Agustsson and Radu Timofte. Ntire 2017 challenge on single image super-resolution: Dataset and study\"?\n- \"MLLM\" should perhaps be defined when it's used in the abstract and at the beginning of Section 3.\n- In Figure 2, $\\phi_{LQ}$ and $\\phi_{LRP}$ could perhaps be added as output of the two Dual-Branch Conditioners blocks?\n- Line 218: LQ and LRP might not need to be defined again here?\n- Line 223: \"These parameters effect\" --> These parameters affect\"?\n- In equation 4, I would probably consider changing the circle symbol to something else.\n- \"T2I\" is defined at the start of Section 3.4, but not at the start of 3.3.\n- The \"Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models\" reference is missing year and venue.\n- \"IQA\" should probably be defined somewhere.\n- Line 453: \"and three scores are enlarged after adding caption-free\" --> \"and all three scores are improved after adding caption-free\"?\n- Line 464: \"and the identical IQA metrics are used in the open-source comparisons\" --> \"and the identical IQA metrics are used as in the open-source comparisons\"?\n- Line 482, \"by large DiTs.Across real\": typo.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is well written overall, components of the proposed approach are explained well in Section 3. I found the paper quite interesting and enjoyed reading it overall.\n- LucidFlux seems to perform well compared to recent open- and closed-source methods.\n- LucidFlux achieves seemingly very impressive qualitative/visual results.", "weaknesses": "- Incorrect citation formatting across basically the entire paper. For example, line 69: \"Discriminative restorers based on CNNs and Transformers Dong et al. (2016); Liang et al. (2021); Zamir et al. (2022) perform well on...\" --> \"Discriminative restorers based on CNNs and Transformers (Dong et al., 2016; Liang et al., 2021; Zamir et al., 2022) perform well on...\". This makes parts of the paper quite difficult to read, but it's a simple fix.\n- Some details regarding the data collection and model training are missing.", "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": "tt16HIklQH", "reviewer": "Reviewer_arXC", "summary": "This paper presents LucidFlux, a new approach to universal image restoration. The framework leverages a large-scale diffusion transformer  to restore real-world images degraded by various unknown distortions. A key contribution of this work is the development of a caption-free semantic alignment method, which replaces traditional text-based approaches with SigLIP-based semantic features derived from a lightly restored proxy image. In addition, the method incorporates a dual-branch conditioner and a timestep- and layer-adaptive modulation schedule, which together help to enhance both the structural integrity and texture details of the restored images. The framework also includes a scalable data curation pipeline to filter large scale image datasets, ensuring high-quality supervision throughout the training process.", "questions": "1. The core design of the dual-branch conditioner appears to be functionally equivalent to the paradigm used in DreamClear [1]. What is the key methodological difference that sets this work apart and establishes its novelty?\n\n2. Although the paper mentions that experiments were conducted at a resolution of 1024, there is a lack of detail regarding the settings for comparison models, especially for super-resolution techniques. Important parameters, such as the upscale factor, are not clearly defined. \n\n3. Although the paper claims that LucidFlux outperforms commercial models, the comparison includes Gemini-NanoBanana and Seedream 4.0, which are not specialized image restoration models. The authors do not clarify why these models were included in the comparison—whether they are considered to have performance comparable to specialized restoration models, or if there were no other commercial restoration models available for comparison. Providing more context on this choice would help clarify the relevance of these comparisons.\n\n4. Beyond fully freezing the backbone, have you explored partial unfreezing (e.g., selected cross‑attention or upper layers)? Please report impacts on perceptual metrics and PSNR/SSIM, as well as stability and compute/memory trade‑offs.\n\n5. Your curation uses fixed thresholds (e.g., Laplacian variance 150–8000, Sobel flat‑patch threshold ≈800 with 50% image‑level ratio, CLIP‑IQA Top‑20%). Could you report sensitivity to these choices (e.g., Top‑10/30%, different Sobel cutoffs) and their effect on metrics/semantic coverage?\n\n6. For a clean attribution of the caption‑free design, can you add a comparison on the same Flux.1 backbone with (i) ideal GT captions and (ii) inference‑time VLM captions, and report both quality and latency?\n\n\n[1] Ai Y, Zhou X, Huang H, et al. DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation[J]. Advances in Neural Information Processing Systems, 2024, 37: 55443-55469.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. LucidFlux introduces a caption-free semantic alignment method that reduces reliance on external captions. The model employs timestep- and layer-adaptive modulation to ensure efficient recovery of both fine textures and global structure. Additionally, it introduces a scalable and publicly documented data curation paradigm, ensuring high-quality supervision for large-scale image restoration.\n\n2. The method demonstrates state-of-the-art performance across both synthetic and real-world benchmarks, consistently surpassing commercial models in both quantitative and qualitative evaluations. It offers valuable insights to the community regarding performance between commercial models and the open-source domain.\n\n3. The paper provides a well-structured and systematic explanation of the proposed framework. The design rationale behind key components is clearly articulated, offering convincing insights into how they work in concert to enhance restoration quality.", "weaknesses": "1. The idea of LucidFlux is similar with DreamClear, which affects its novelty. The authors shall clarify the differences between them.\n\n2. Some details of compared methods are missing. Clarifying these settings would improve the transparency and comparability of the results.\n\n3. The contribution of caption-free is not so clear. Some experimental results with the same Flux.1 backbone can be useful.", "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": "KUzqXGt0Z2", "reviewer": "Reviewer_8VrS", "summary": "This paper proposes LucidFlux, an image restoration framework that adapts a large diffusion transformer (Flux.1) for restoring images degraded by unknown mixtures, without relying on captions. The key contributions include a lightweight dual-branch conditioner that anchors geometry and suppresses artifacts by injecting signals from both the degraded input and a lightly restored proxy. Additionally, a timestep- and layer-adaptive modulation schedule enables context-aware, coarse-to-fine restoration. To ensure semantic alignment without text prompts, LucidFlux leverages SigLIP features from the proxy. The framework also introduces a scalable data curation pipeline for structure-rich supervision. Together, these innovations enable robust and efficient restoration with minimal parameter overhead.", "questions": "Refer to Weakness", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The proposed method is simple and easy to follow.\n2. The proposed method achieves state-of-the-art performance on real-world super-resolution datasets.", "weaknesses": "1. The usage of the term \"Universal image restoration\" is not accurate. In the low-level vision field, \"Universal image restoration\" usually refers to all-in-one image restoration tasks [1,2,3] in most papers. However, in my opinion, this paper mainly addresses real-world image restoration tasks.\n\n2. In terms of model architecture, the proposed method adopts an identical dual-branch design as DreamClear, showing limited structural innovation.\n\n3. I believe the proposed dataset is an important contribution of this paper. However, it is unclear whether it will be open-sourced.\n\n4. The experiments comparing the proposed method with commercial models are not meaningful, as many of the models used for comparison are not super-resolution models.\n\n5. This paper uses FLUX as the base model, which has significantly more parameters and computational cost compared to previous works. The authors need to provide inference efficiency comparisons with prior methods.\n\n\n References\n\n[1] Controlling Vision-Language Models for Universal Image Restoration. ICLR, 2024.\n\n[2] Universal Image Restoration Pre-training via Degradation Classification. ICLR, 2025.\n\n[3] Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model. CVPR, 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": {}}]

Author rebuttal:

Decision: Accept (Poster)