FreeAdapt: Unleashing Diffusion Priors for Ultra-High-Definition Image Restoration
Authors: Xiaoan Liu
Xinyi Liu
Yongjun Zhang
Yi Wan
Li Tingyun
Dongdong Yue
Venue: ICLR 2026 Poster (2026)
Keywords: Latent Diffusion Models; Ultra-High-Definition Image Restoration;  Training-Free; Plug-and-Play; Resolution Adaptation
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose FreeAdapt, a plug-and-play framework that leverages frequency and feature guidance to fully unleash diffusion priors for artifact-free, detail-preserving ultra-high-definition image restoration.

Abstract: Latent Diffusion Models (LDMs) have recently shown great potential for image restoration owing to their powerful generative priors. However, directly applying them to ultra-high-definition image restoration (UHD-IR) often results in severe global inconsistencies and loss of fine-grained details, primarily caused by patch-based inference and the information bottleneck of the VAE. To overcome these issues, we present FreeAdapt, a plug-and-play framework that unleashes the capability of diffusion priors for UHD-IR. The core of FreeAdapt is a training-free Frequency Feature Synergistic Guidance (FFSG) mechanism, which introduces guidance at each denoising step during inference time. It consists of two modules: 1) Frequency Guidance (FreqG) selectively fuses phase information from a reference image in the frequency domain to enforce structural consistency across the entire image; 2) Feature Guidance (FeatG) injects global contextual information into the self-attention layers of the U-Net, effectively suppressing unrealistic textures in smooth regions and preserving local detail fidelity. In addition, FreeAdapt includes an optional VAE fine-tuning module, where skip connection further enhances the reconstruction of fine-grained textures. Extensive experiments demonstrate that our method achieves superior quantitative performance and visual quality compared to state-of-the-art UHD-IR approaches, and consistently delivers strong gains across multiple LDM-based backbones.

Published as a conference paper at ICLR 2026
FREEADAPT: UNLEASHING DIFFUSION PRIORS FOR HIGHH-EIDDDEIIIIONN IMAGE RESTORATION
Xiaoan Liu', Xinyi Liu L Yongjun Zhang 12.33 Yi Wan Tingyun Li', Dongdong Yue
1 School of Remote Sensing and Information Engineering, Wuhan University
2 Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA
3 Hubei LuoJia Laboratory
(xiaoanliu, 1iuxy0319, zhangyi, yi.wan, tingyunli, yueyisui edu.
ABSTRACT
Latent Diffusion Models (LDMs) have recently shown great potential for image restoration owing to their powerful generative priors. However, directly applying them to ultra-high-ae hiira-high-definttoon image restoration (UHD-IR) often results severe global inconsistencies and loss of fine-grained details, primarily caused by patch- based inference and the information bottleneck of the VAE. To overcome these issues, present FreeAdapt, a plug-and-pil framework that unleashes the ca- pability of diffusion priors for UHD-IR. The core of Free is a training-free Frequency Feature Synergistic Guidance (FFSG) mechanism, which introduces guidance at each denoising step during inference time, It consists of two modules: Frequency Guidance (FreqG) selectively fuses phase information from a refer- ence image in the frequency domain to enforce structural consistency across the entire image; 2) Feature Guidance (FeatG) injects global contextual information into the self-attention layers of the U-Net, effectively suppressing unrealistic tex- tures in smooth regions and preserving local detail fidelity. In addition, Adeet includes an optional VAE fine-tuning module, where skip connection further en- hances the reconstruction of fine- grained textures. Extensive experiments demon- strate that method achieves superior quantitative performance and visual qual- ity compared to state-of-the-ae UHD-IR approaches, and consistently delivers strong gains across multiple LDM-bbsed backbones.
INTRODUCTION
With the rapid advancement of 4K/8K display and imaging technologies, the demand for Ultra-High-
Definition (UHD) images is increasing dramatically (Wang et al. 2025 Li et al., 2023b Zheng et al., 2021 Zhao et al. 2025 Liu et al., 2025b). However, in real-world capture, UHD images inevitably suffer from degradations such as low light, haze, blur, and noise, which are often caused by insufficient illumination, adverse weather conditions, or equipment limitations (Wang et al. 2025 2024a). As a result, UHD image restoration (UHD-IR) has become a crucial yet highly challenging research field in computer vision, characterized by its massive resolution scale and requirements for preserving fine- fine-grained details (Wang et al.) 2025 Yu et al. 2024b).
To address the challenges of UHD-IR, researchers have proposed a variety of solutions (Zhao al. 2025 Wang et al. 20244a Liu et al. 20255b Su et al.) 2024| Liu et al. [2025a] Wu et al.) 2024a) Existing studies primarily enhance restoration performance by designing innovative network
chitectures and developing advanced training paradigms. For instance, UHDformer (Wang et al. 2024a) employs dual--pth module balance efficiency and accuracy. while ERR (Zhao et al. 2025) decomposes the restoration process into multiple stages for refined modeling- Although these methods have achieved remarkable performance, they unavoidably encounter bottlenecks, as merely modifying network structures is insufficient to overcome the inherently ill-posed nature of image restoration (Xu et al., 2024). For UHD-IR, how to leverage powerful diffusion priors to overcome the bottlenecks remains insufficiently explored.
Recently, Latent Diffusson Models (LDMs) (Rombach et al. 2022) have shown remarkable poten- tial in low-level vision tasks owing to their powerful generative priors (Lin et al.) 2024a; Wu et al.) 2024c Yue et al. 2025 Chen et al. 2025a] (Snn et al., 2025). However, directly applying these pre-trained models to UHD-IR faces several technical bottlenecks. First, due the high computa-

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MESSI
MESSI
MESSI
GT&
(b) Inference
(O) Upompilng
(4) Upsampling
(e)
Figure 1: Visual comparison of different LDM-base strategies on the UHD-LL (Li et al. 2023ap
tional cost and memory demand of self-attention mechanisms, models cannot process an entire UHD image in single pass, making patch-based inference unavoidable. As illustrated in Figure T d), this strategy often introduces stripe-like like artifacts and color inconsistencies, while naive upsampling schemes typically cause blurring or structural distortions. Second, the absence of global context in patch-based inference amplifies the stochasticity of diffusion models, leading to inconsistent high- frequency details in textureless regions. Finally, the Variational Autoencoder (VAE) (Kingma & Welling 2013), as a core component of LDMs, suffers from lossy compression that discards high- frequency information and thereby limits restoration fidelity.
To enable pre-trained LDMs to overcome these challenges, we introduce FreeAdapt, unified framework that combines a plug-and-playy training-free guidance mechanism with an optional VAE fine-tuning (VAE-FT) module. FreeAdapt provides a cost-efficient way to unleash the potential of diffusion priors and adapt pre-trained LDMs and their extensions (e.g., ControlNet (Zhang al. 2023) to UHD-IR. The core of our approach is the Frequency Feature Synergistic Guidance (FFSG) mechanism, which enforces both global consistency and local detail fidelity at each step of patch- based denoising during inference time. Specifically, FFSG is composed of two complementary modules: 1) Frequency Guidance (FreqG), which selectively fuses phase information from a low- resolution reference image in the frequency domain ensure global structural consistency across patches; 2) Feature Guidance (FeatG), which incorporates global context into the U-Net self- attention layers constrain local detail generation and suppress high-frequency hallucinations. In addition, the optional VAE-FT module fine-tunes the VAE decoder with skip connection to ease the information bottleneck and improve the reconstruction of fine textures.
Our main contributions are summarized as follows:
To the best of our knowledge. FreeAdapt is the first plug-an and play diffusion prior frame- work for the UHD-IR task, providing an effective and generalizable solution for pre-trained LDMs and their extensions.
We propose a training-free free, plug-and-pla, synergistic guidance mechanism that, through innovative frequency and feature guidance modules, effectively resolves the artifact and detail hallucination issues in UHD-IR, significantly improving both global consistency and local fidelity.
By introducing skip connection and fine-tuning the VAE decoder, we successfully mitigate the VAE's information bottleneck and improve the fidelity of reconstructed details.
Through extensive experiments across three representative LDMM-ased backbones (LDM (Rombach et al. 2022), StableSR (Wang et al.) 2024b), and DiffBIR (Lin et al.) 2024aa), show that FrecAdapt consistently delivers strong performance improvements, achieving PSNR gains typically above 2 dB over patch-based inference. These gains stem from its ability to correct cross-patch inconsistencies and more effectively exploit pre- trained diffusion priors.
2 RELATED WORK
Ultra-High- Definition
Hiirr-iigh--finitionnn Image Restoration. UHD-IR has gained increasing attention due to the rapidly growing demand for high-resolution images (Li et al. 2023b Liu et al. 2025b Wang al.)
2

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2023 Zhao et al. 2025). UHDformer (Wang et al. 2024a) achieved balance between performance and efficiency by introducing a dual-path architecture with correlation matching and channel mod- ulation. Dre (Liu et al., 2025b) employed frequency- enhuancc-- VAE, integrating Fourier and wavelet modules to improve detail fidelity. From a spectral perspective, ERR (Zhao et al., 2025) deconstructed the restoration process into three progressive stages, incorporating multiple structures for phased modeling, Although these methods achieved notable progress, relying on architectural innovations and training paradigms alone cannot fundamentally resolve the inherently ill-posed na- ture of image restoration, leading to inevitable performance bottlenecks (Xu et al., 2024). In the context of UHD-IR, the potential of leveraging diffusion priors has been overlooked. Therefore, this study aims to investigate and utilize the powerful generative priors in pre-trained models to enhance restoration performance for UHD-IR, specifically addressing the persistent issues of insufficient pri- ors and the loss of fine-grained details.
High-resolution Adaptation of Diffusion Models. With the rapid advancement of diffusion mod- els in image generation, high-resolution image synthesis and upscaling have become significant research hotspots (Tragakis et al.] 2024, Huang et al. 2024). Existing approaches fall into two categories. The first (Ren et al., [024 [Zhang et al. (025)) retrains fine-tunes models on high- resolution datasets, which requires substantial data and computational resources. The second (Bar-
Tal et al. 2023; Du et al.) 2024 Lin al. 2024b Huang et al., 2024] Zhang al.. 2024c Quu et al.] 20244] | et al.) 2024b) follows a training-free free paradigm that improves effective resolution by Optimizing the inference procedure. Representative methods such MultiDiffusion (Bar-Ta) Tal
it al.
2023 generate large images by fusing multiple diffusion trajectories, while DemoFusion (Du al.
2024p enhances visual coherence through progressive sampling and skip-residual refinement.
AccDiffusion (Lin et al. 2024b) further emphasizes semantic alignment when guiding patch-level generation. These techniques are highly effective for high-resolution generation, where outputs only need to be perceptually plausible. In contrast, UHD restoration requires strict structural fidelity to the degraded input, and any hallucinated or altered content violates the restoration objective, As re- sult, -orienttd strategies often struggle to maintain onput- consistency in UHD-IR. Meeting restoration requirements therefore relics on two principles: selective injection of reliable global information and preservation of structural alignment throughout denoising. Our frequency and feature guidance modules follow these principles, enabling diffusion priors to maintain global coherence while faithfully preserving input-consistent details.
Diffusion Prior-Based Image Restoration, In recent years, with the breakthrough development of 2022) in image and video generation, their capability as powerful genera-
LDMs (Rombach al.,
tive priors has gradually yy been introduced into low-level vision tasks (Wang et al.] 2024b] Lin et al.]
2024a| Wu et al. 2024c] Yang al. 2024 Yu at. 2024a| Ai et 2024 Zhang et al. [0024]] Chen et al. 2025b] Arora et al.) 2025). StableSR (Wang et al. 2024b) fine-tuned the pre- prainee model with a time-aware encoder and feature modulation mechanism to achieve high-quality image super- resolution. DiffBIR (Lin et al., (2024a) adopted a two-stage strategy to extend the adaptability of diffusion models to blind image restoration tasks. SeeSR (Wu et al. 2024c) designed a degradation- aware text prompt generator to guide more refined super-resolution reconstruction. SUPIR (Yu) jet al. 2024a) incorporated prompts generated by multimodal large language models and employed adapter for prior control. Although these methods have achieved notable ad- vances in image restoration, they primarily concentrated on optimizing performance within native resolution. For UHD-IR, directly applying existing models often results in artifacts such as distor- tions, color inconsistencies, and the loss of fine details caused by the VAE. Therefore, designing a alug-and-play artifact-free, and universal image restoration approach for UHD images, remains an important research direction.
degradation
METHODOLOGY
Preliminary. LDMs (Rombach et al. 2022 are text-to-image diffusion models that perform de-
noising in a latent space. Specifically, LDMs employ a pre-trained VAE (Kingma & Welling 2013) to encode an image into a latent representation 20, followed by training a denoising U-Net co the latent space. The training objective of the LDM is formulated as:
Lidm = E,,.,t. |l|e EØ( ve(va, + VI- a,e, c, (
(l)
where E ~ (0, denotes the ground truth noise at timestep t, c represents the conditional infor- mation, and oe the diffusion coefficient defined in DDPM (Ho al. 2020).
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Frequency Gaidance
&
LFFT
3
(6) Festore Guidance
Faka
(d) Skip Connection
Fwi
Out
Figure 2: Overview of the proposed Freet framework. The degraded UHD input lig is down- sampled and passed through a pre-trained LDM to obtain reference latent 25 for global structural guidance. During iterative patch-based denoising of 2,, frequency guidance (FreqG) fuses the phase spectrum of 2n and 2fe/ to enforce cross-patch consistency, while feature guidance (FeatG) injects global context into U-Net attention layers to suppress artifacts. Finally, the latent z/ decoded by D, where an optional VAE fine-tuning (VAE-FT) module with skip connection enhances high- frequency details, producing the restored UHD output Lree-
Objective. To address the challenges of adapting pre- pre-trained LDMs for UHD-IR, we introduce novel plug-and-play Free framework. Our primary objective is to resolve the prevalent issues of artifacts, global inconsistencies and detail loss that arise when directly applying pre-trained LDMs to UHD-IR, without modifying or fine-tuning of the denoising U-Net.
Overview. As shown in Figure the core of FreeAdapt is training- -ree guidance mechanism that operates during the iterative, patch-based denoising process. This mechanism integrates FreqG and FeatG modules enforce global consistency and preserve local detail fidelity. Additionally, to overcome the inherent high-frequency information loss of the VAE, we introduce an optional VAE-FT module that fine-tunes the VAE decoder with lightweight modifications further boost the fidelity of the restored images.
3.1 FREQUENCY FEATURE SYNERGISTIC GUIDANCE
FFSG is a training- free, plug-and-pe play mechanism compatible with pre-trained LDMs and their ex- tensions, such ControlNet (Zhang et al. 2023). It is designed to address the global inconsistencies and local detail distortions that arise from patch- patch-based inference. As illustrated in Figure 2 within each denoising step, employ gequenccy-ommiin constraints to stabilize freeueeeeenyy structures and textures, while a feature-level attention module suppresses high-frequency artifacts and pro- motes cross-patch consistency. The overall mechanism consists of two main stages: Reference Image Generation and Guided
Iterative Denoising.
Reference Image Generation. LDMs are typically trained at a fixed resolution, making their direct application to UHD images prone to structural distortions and artifacts. To address this, we first generate a reference image that provides reliable low-frequency structural information by leveraging the inherent image-to- ponge-tt-imann to-image capability of the diffusion backbone. Specifically, the degraded UHD input 4g downsampled to the native training resolution (e.g.. 512x512), encoded by the VAE to obtain the conditioning latent, and then used to guide single standard denoising process of the pre-trained LDM, producing clean latent representation = with coherent content and structure. This latent is decoded into the pixel domain, upsampled back to the UHD resolution to form Lref, and re-encoded by the VAE encoder to obtain z for global guidance. Compared with cascaded multi-resolution approaches (Du al. 2024 Lin et al. 2024b). strategy provides structurally reliable reference using only a single denoising process.
Guided Rish-tesooution Iterative Denoising. To address GPU memory limitations in UHD-IR, we adopt a patch-based denoising strategy. At each denoising step t, multiple small patches are cropped from the current high-resolution latent representation and denoised individually. Overlap-
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Walsons
vittsons
Wiatsons
viatsons
20A
GT&
(b) Inference
Freqti
Figure 3: Visual comparison and ablation study of Adept the UHD-LL (b) Patch-based inference suffers from color inconsistency; (c) FreqG contains high-frequency noise; (d) FTSG still exhibits detail loss.
ping regions are blended by smooth averaging to maintain consistency across patch boundaries. Within each denoising step, FreqG and FeatG are integrated into the denoising process, jointly improving both global structural coherence and local detail fidelity.
Frequency Guidance. To overcome the structural inconsistencies introduced by patch-based de- noising, as shown in Figure3]b) we incorporate FreqG into the iterative denoising process. At each step both the current latent representation 2, and the noised reference latent 2!" are transformed into the frequency domain using Fast Fourier Transformation (FFT), yielding their respective am- plitude and phase spectrum:
FFT(21) Aee
(2)
FFT(2(()= A00
(3)
To ensure that the global structure of the reference is effectively preserved without interfering with texture generation, only the phase components are fused. Specifically, a dynamically low-pass filter (C(L)) is applied to weight the two phase spectrum:
क. = arctan ((1 - k =K(t))(o(())
(4)
K((t) if x
< <w and by 1/1 < c.
T
(5)
0, otherwise
where e a hyperparameter (default 0.15). The filter K((t, defined in Eq. 5 gradually decreases the denoising step progresses, adaptively balancing global structural constraints with flexibility for detail generation. The corrected latent then reconstructed by combining the original amplitude spectrum A, with the fused phase spectrum क. through inverse FFT:
* iFFF
(6)
Feature Guidance. While FreqG enforces global low-frequency consistency, it cannot constrain high-frequency details generated independently within each patch. As shown in Figure Bic). in textureless regions this randomness often introduces spurious details, leading to visual noise tifacts. To address this issue, we introduce FeatG module that injects global contextual information into the self-attention layers of the U-Net. This allows each patch to reference the global semantics provided by the guidance image, thereby promoting inter-patch coherence and suppressing unreal- istic artifacts. Specifically, we first compute the Query (Qrae), Key (K (Kite), and Value (Vrae) of the current high-resolution patch to obtain local attention:
(Quse KTa
Attmiooat = softmar
) Veite
(7)
Vd
5

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where d is the feature dimension. In parallel, extract the patch-aligned query Qid from the reference, together with global keys Kref global and values Vrrff and compute the global attention: "lobatt
(u(rrae) Vd
K global
T
Attnglobal = sof softmax
V global
(8)
where 24 denotes an upsampling operation. The final output obtained by linearly blending the two attentions:
final = (1 - a)) Attmaocal a Attni
where a is set to 0.2 by default. Importantly, this operation applied to the 3rd-8th decoder layers of the U-Net.
3.2 VAE FINE-TUNING
In LDMs, VAE is responsible for perceptual compression, but the lossy compression characteristic causes the loss of high-frequency details such fine textures and text at high resolutions illus- trated in Figure Rd). This limitation makes the VAE a major bottleneck for UHD-IR. To address this issue, introduce optional VAE-FT module that strengthens the decoder's ability to recover fine details while keeping both the encoder and the U-Net frozen, ensuring that the diffusion process remains fully training-free.
During fine-tuning, both a low-quality image and its high-quality counterpart are passed through the shared VAE encoder, producing high-quality latent representation along with residual features extracted from the degraded input. The decoder receives the high-quality latent together with these residual features through skip connections, which provide structural cues that help restore informa- tion lost during encoding. Through this training strategy. VAE-FT learns a task-level prior for detail reconstruction without learning the restoration task itself. Because this prior is task specific rather than model specific, the fine-tuned decoder can be applied across different diffusion backbones without further training, serving as a lightweight auxiliary component that complements rather than alters the diffusion prior. With this training framework in place, we implement VAE-FT using an enhanced decoder designed to efficiently integrate these residual cues.
we enhance the VAE decoder by introducing skip connection combined with parameter-efficient LoRA (Hu et al., (022). Encoder features from the degraded input are first refined through adaptive instance normalization (AdaIN) (Huang & Belongie| 2017), which
As shown in Figure 2
suppresses degradation while retaining structural details, and are then injected into the corresponding upsampling layers of the decoder via Zero-Convolution modules (Zhang al., 2023). The fine- tuning is supervised by a composite loss:
L Ldwi + Lipips + Lissim Lgan
(10)
where Ldwi is an L2 loss in the Discrete Wavelet Transform domain for reconstructing high- frequency details, Lippps ensures perceptual similarity, Lussim maintains structural consistency, and Lgaan introduces adversarial feedback to improve realism and sharpness.
4
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. To comprehensively evaluate the performance of our proposed method on UHD-IR tasks, we conduct experiments on multiple publicly available benchmark datasets. For low-light enhance- ment, we use the UHD-LL dataset (Li al. (023a). For image dehazing, we evaluate performance on the UHD-Haze dataset (Wang et al. 2024a). For image deblurring, we adopt the UHD-Bluu dataset (Wang et al. 2024a).
We employ combination of reference-based and no-reference metrics to pro- vide a comprehensive evaluation of restoration results. PSNR and SSIM (Wang et al. 2004) are used conventional measures, assessing pixel-level reconstruction accuracy and structural similar- ity, respectively. LPIPS (Zhang et al. 2018) and DISTS (Ding et al. 2020) are adopted as perceptual metries to better reflect human visual judgments of image quality. In addition, to further evaluate perceptual quality a reference- frreeeeffeeeee setting, we use no-reference image quality assessors including
Evaluation Metrics.
CLIPIQA (Wang al. 2023). MUSIQ (Ke et al. 2021), and MANIQA (Yang et al.) 2022

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Inpui
Resformner
UHDiormer
DreanUHD
URD-LLAGTT
Wave-Mambo
ERK
LOMI-Dur
DIMBIR- Ours
Topost
DreamUHD
Haze(GT)
Input
Restormer
Ultifrmer
DreamUHD
(HHD-Bar(CT)
FFT FFTformer
FRR
LDM--OurS
DoMBIR--urs
Figure 4: Visual comparison the proposed FreeAdapt against state-of-ther approaches.
Input
LDM Mulifasiin
LDM Demofusion
LDM: Ploelsmith
LUM-Ours wie VAE-FP AT-FT
Figure 5: Visual comparison of training-free diffusion model adaptation methods based on LDM on three UHD-IR datasets: UHD-LL (first row), UHD-Haze (second row), and UHD-Blu BlD- (third row).
4.2 EXPERIMENTAL RESULTS
We conduct extensive quantitative and qualitative experiments multiple UHD-IR tasks to demon- strate the effectiveness diffusion priors and that our method more effectively leverages pre-trained diffusion models in UHD scenarios. For fair and comprehensive evaluation, we compare our ap- proach against two categories methods: (1) task-specific restoration methods, including represen- tative approaches that achieve leading performance on individual restoration tasks, such as SwinIR 202T). Restormer (Zamir al., 2022), Uformer (Wang et al., 2022). UHDFormer
(Liang et al.
(Zou et al. (2024). FFTformer Kong et al. 2023). 4KDehazing (Xiao et al. 2024), DreamUHD (Liu et al.) (025b), and ERR (Zhao
Wann al. 2024ah. UHDFour (Li al. 2023a),
al. 2025);; : (2) training-free diffusion adaptation methods that adapt standard diffusion models to
high-resolution images without additional training, such as MultiDiffusion (Bar-Tal et al., 2023), DemoFusion (Du et al. 2024), and PixelSmith (Tragakis al. 2024p. Moreover, further validate the generality of Free Adapt under different diffusion architectures, we conduct experiments using three representative diffusion backbones, including classical latent diffusion model (LDM) (Rom- bach et al. 2022). the restoration-orientte DiffBIR (Lin et al.) 2024a), and the super-resolution oriented StableSR (Wang et al. 2024b), and evaluate them under the same UHD-IR setting.
Low-Light Image Enhancement. As shown in Table I our method outperforms advanced ap- proaches on no-reference metrics and the perceptual metrics DISTS. Although yields lower scores than non-diffusion methods on full-reference metrics, this is largely because those methods are trained end-to-end with L2 or perceptual losses that are directly aligned with these metrics. How- ever, such optimization often sacrifices the realism of generated details, producing overly smoothed results (Yang et al.) 2024 Wang et al. 2024b Wu et al. 2004c).. The visual comparisons in Figure 4 clearly demonstrate that diffusion prior based methods restore richer and more realistic details, whereas competing methods generally produce blurred outputs, which contradicts the core objective of UHD-IR: recovering fine-grained details.

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Table I: Quantitative comparison of the proposed method against various -aate-fft methods. The symbols and respectively represent that higher or lower values indicate better performance. Bold represents the best and underline represents the second best.
Dataset
Method
PSNR
SSIM 1 LPIPS +
DISTS
CLIPIQA t MUSIQ
MANIQA
Uformer
22.57
0.904
0.258
0.170
0.335
32.63
0.259
Restormer
23.46
0.906
0.268
0.160
0.402
36.05
0,265
Swinir
22.12
0,905
0.214
0.114
0.385
33.51
0.305
UHDFour
28.59
0.918
0.235
0.140
0,411
28.08
0.280
UHDformer
27.10
0.926
0.232
0.138
0.360
35.83
0.304
UHD-LL
Wave-Mamba
29.84
0.941
0.185
0.117
0.410
41.78
0.337
DreamUHD
27.73
0.929
0.220
0.132
0.378
38.52
0.311
ERR
27.57
0.933
0.214
0.148
0.501
42.28
0.344
L.M--Ous
22.21
0.887
0.253
0.101
0.569
49.07
0.372
StableSR -urs
22.42
0.887
0.244
0.093
0.560
48.35
0.364
DifiBIR-Ours
23.99
0.900
0.233
0.092
0.564
48.37
0.364
Uformer
23.38
0.937
0.136
0.069
0,283
31.97
0.267
Restormer
23.10
0.930
0.157
0.076
0.294
33.69
0.264
Swinir
24.09
0.943
0.101
0.038
0.288
32.07
0.285
UHDformer
22.58
0,942
0.118
0.049
0.301
31.72
0,288
4KDehazing
22.50
0,906
0.185
0.145
0.329
35.60
0.281
UHD-HHaze
DrCamUHD
24.36
0.945
0.116
0.048
0.282
33.08
0,280
ERR
25.10
0.949
0.119
0.051
0.282
31.17
0.292
LDM-Ours
21.59
0.934
0.104
0.044
0,403
44.38
0.343
StableSR-Ours
22.80
0.945
0.092
0.033
0.393
43.63
0.333
DifiBIR-Ours
25.50
0.953
0.077
0.028
0.404
42.18
0.337
Uformer
28.88
0,851
0.205
0.103
0.284
29.21
0.250
Restormer
29.57
0.860
0.210
0.115
0.300
28.86
0.243
Swinir
28.30
0.834
0.190
0.086
0.269
28.10
0.236
FFTformer
26.28
0,825
0.215
0.105
0.288
31.22
0.236
UHDformer
28.81
0.843
0.233
0.127
0.299
27.31
0.233
UHD-Blur
DreamUHID
27.15
0,808
0.284
0.172
0.265
24.33
0.205
ERR
29.71
0.861
0.206
0.106
0.267
29.28
0,251
LDM-Ours
26.91
0.828
0.166
0.076
0.347
34.39
0.278
StableSR Ours
27.42
0.831
0.162
0.077
0.364
36.10
0.278
DiftBIR-Ours
28.16
0.851
0.145
0.059
0.378
38.64
0.290
Image Dehazing. In the evaluation the UHD-Haze dataset, the DifTBIR- -urr model demon-
strates comprehensive superiority. As shown Table [ it achieves the best performance on both reference-based metrics and perceptual metrics. Notably, the perceptual metric DISTS, our ap- proach delivers a remarkable improvement of about 26.3% over the second-bes best method SwinIR. Furthermore, the visual results in Figure iillustrate that method more effectively restores color saturation and contrast while avoiding distortions and detail loss, providing strong evidence for the effectiveness of diffusion priors.
Image Deblurring. As shown in Table our adapted diffusion prior-based model also achieves strong performance. On the perceptual metric LPIPS, DifIBIR-Ous achieves an improvement of about 29.6% over ERR, highlighting the effectiveness of diffusion priors. Moreover, the visual comparisons in Figure demonstrate that method effectively handle complex motion blur, producing images with sharp edges and well-preserved textures. In contrast, competing methods often suffer from ringing artifacts or fail to completely remove blur.
Comparison with Training-Frees Diffusion Adaptation Methods, To further evaluate the gen- erality of our guidance mechanism, Table 2 compares training- free diffusion adaptation strategies across three representative backbones: LDM, DiffBIR, and StableSR. Across all UHD-IR tasks, our FFSG modules consistently yield clear improvements over patch-based inference, as reflected the FFSG Gain rows, and outperform MultiDiffuusio, DemoFusion, and PixelSmith on PSNR, LPIPS, and MUSIQ. A key factor behind this performance gap is that existing methods were originally designed for high-resolution generation, where perceptual plausibility is prioritized, whereas UHD restoration demands strict consistency with the degraded input. Our FFSG mechanism is explicitly tailored for restoration, enforcing global structural coherence and input-aligned texture synthesis.
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Table 2: Quantitative comparison of diffusion model adaptation methods using LDM (Rombach et al. 2022). StableSR (Wang et al.) 2024b), and DifBBIR (Lin et al. 2024a). PI indicates patch- based inference. Bold and underlined entries denote the best and second-best results, respectively. Red values represent positive gains, while blue values indicate negative gains.
UHD-LL
UHD-Haze
UHD-Blur
Model
PSNR / LPIPS MUSIQ
PSNR LPIPS / MUSIQ
PSNR / LPIPS MUSIQ
LDM-PI DoM-uuliiiiffusion
18.91 / 0.386 44.89
19.27 / 0000//099///// 42.63
23.61 /0.213 35.37
20.13/ 0.399 32.41
18.59 0/59/0.22 0.240 38.71
25.16 / 0.364 26,49
NDM-Demofusior
21.74 0.417 23.09
19,40/ 0.2992 32.17
23,81 / /0,400/ 22.27
LDM-Pixelsmith
20.64 0.397 /1.15
20.64 / 0.224 42.97
24.07 / 0..22 29.75
LDM-Ours w/o VAE-FT
21.88/ 0.283 45.67
21.37 0.163 / 44.75
26.58 0.198 35.52
LDM-Ours
22.21 0.253 49.07
21.59 / 0.104 44.38
26.91 / 0.166 34,39
FFSG Gain
+2,96 -0.103 +0.78
+2.11 -00028 +2.13
+2.97 -0.015 -2.01/ +0.15
VAE-FT Gain
+0,33/ -0.030 / +3.40
+0,22 / -0.059 / -0,38
+0.33/ -0.032 / -1.13
StableSR-PP
09.14/0.369 445.34
19.85 0. 40.28
24.10/ 35.74
SoobleSR-Mulidifffussoo
19.52/ 0.355 37.71
20.14 0.228 / 40.37
24.80 /.2259 32.47
SaableR-Demoofusin
21.59 0.393 25.65
20.17 0.211 34,25
25.31 /0.322 23.38
ChableSR-Pixelsmn
20.79 / 0.361 /33448
20.92 / 0.18 / 41,26
25.65 /0.297 32.62
StableSR-Ours w/o VAE-FT
21.96 0.270 47.20
22.51 0. 44.02
27.07 0.195 36,80
StableSR-Ours
22.42 0.244 48.35
22.80/ 0.092 43.63
27.42 0.162 36.10
FFSG Gain
+2.82 -0.098 / +1.86
-0.5000.040/ +3.74
+2.97 -0.007 ++1.06
VAE-FT Gain
-0.026/ +1.14
+0,29 / -0.051 / -0.40
+0.35 -0.033 -0.70
DiffBIR-PI Mulii--Muliiiifuuionn
21.61 0.2280 45.38
/ 43/ 41.58
26.65 / /0.174 38.09
22.18 0.285 40.24
23.35 0.141 38.21
24.87 / 00.176 32.73
DeiBBi--Demofusoon
23.22 0.335 24.82
23.44 / 0,224 32.59
26.06 / 00,80/ / 26.74
DiffBIR-Pixelsmtth
23.45 0.316 32.44
23,90 / 0.225 40.20
24.79 / 00.91 / 29.11
DifiBIR-Out w/o VAE-FT
23.66 0.252 45.85
24.96 / 0.127 42.41
27.68 / 0.175 38.97
DiftBIR-Ous
23.99
0.233 48.37
25.50 0.077 42.18
28.16 0.145 38.64
FFSG Gain
+2.05 / -0.028 / +0.48
+1.61/ +0.82
+1.03 /1.0340.001 +0.87
VAE-FT Gain
/ /0.009 +2.52
+0.54/ -0.22
+0.48/ -0.33
As shown in Figure 5] innecaatioo-orientte baselines often introduce blurring, distortions, or incon-
sistent textures. In contrast, diffusion models equipped with FFSG produce sharper details, more stable structures, and visually coherent results that remain faithful to the input.
4.3
ABLATION STUDY
Table 3: Ablation study of the proposed methods.
Table 4: Comparison of Fusion Methods of LDM.
FreqG
FeatG
VAE-FT
PSNR t
LPIPS
Fusion method
SSIM f
DISTS
X
X
18.91
0.386
Patch-based Inference
0.823
0.145
X
X
21.76
0,314
Skip Residual
0.839
0.312
V
V
21.88
0.283
FFT Fusion
0.863
0.187
22.21
0.253
FeaqG
0.865
0.121
To validate the effectiveness of each proposed component, we conduct series of comprehensive ablation experiments. All experiments are performed on the UHD-LL (Li et al. 2023a), with pre- trained LDM equipped with standard patch- based denoising strategy serving as the baseline model.
Effectiveness of Frequency Guidance. As shown in Figure [bb. Figure BJb) and Figure 6 the baseline model produces severe stitching artifacts and color inconsistencies when directly applied with patch-based inference. After incorporating our proposed FreqG, global consistency is signifi- cantly improved, and TableBJuurthe shows notable improvements in PSNR and LPIPS. To validate the superiority of our fusion strategy. we also compare it with alternative designs, including spatial- domain fusion (skip residual from DemoFusion Du et al. 2024) and FFT spectrum fusion (Yang et al. 2025). As presented in Figure 6 the former fails to distinguish the roles of low- and high- frequency information during the diffusion process, resulting in blurred outputs, while the latter leads to severe color distortions. In addition, the quantitative results in Table show that both alter-

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Input
Patch-based Inference
Residual
Fusien
FreqC
Figure 6: Qualitative comparison of different diffusion fusion methods.
natives substantially degrade perceptual quality, with much worse DISTS scores compared to ours. These comparisons clearly demonstrate the effectiveness of our phase-only fusion strategy.
Effectiveness of Feature Guidance. Building upon FreqG alone, further incorporate the pro- posed FeatG module. As shown in Table this addition yields further improvements on perceptual metrics such as LPIPS. The visual comparisons in Figure g also demonstrate that FeatG effectively suppresses hallucinated noise and unrealistic textures in smooth regions, producing more faithful and coherent local details.
Effectiveness of VAE Decoder Fine-tuning, Fi- nally, we validate the effectiveness of the pro- posed VAE fine-tuning module. Within the full guidance mechanism, we compare the perfor- mance of the standard VAE with our fine-tuned VAE decoder. shown in Table and Table 3 the fine-tuned decoder achieves significant im- provements on PSNR and LPIPS, while the visual results in Figure 3 further confirm its superiority in reconstructing fine details. Moreover, to exam- ine the role of the introduced skip connection, conduct additional ablation studies. The compar- isons in Figure 7 clearly indicate that skip con- nection effectively alleviates the information bot- tleneck and enable the recovery of sharper high- frequency details, whereas removing them leads noticeable detail loss.
GT
wiss Skip ooanetion
vl Skip conncetion
Figure 7: Ablation study on the skip connection in the VAE-FT module.
CONCLUSION
In this paper, we propose FreeAdapt, a plug-ant plug-and-play framework designed unleash the poten- tial of pre-trained diffusion priors for UHD-IR. FreeAdapt integrates FreqG to correct global low- frequency structures and colors, ensuring cross-patch consistency, while FeatG introduces global context into the U-Net attention layers suppress unrealistic high-frequency details in smooth re- gions. addition, we design an optional VAE-FT module, where skip connection further enhances the reconstruction of fine textures. Extensive experiments demonstrate that method not only achieves significant improvements in perceptual metrics over state-of-the-an restoration methods but also consistently outperforms other diffusion adaptation approaches, highlighting its superiority in fully exploiting diffusion priors.
Limitations and Future Work. Despite its effectiveness, FrecAdapt has limitations. As iterative denoising approach, it is time-consuming and computationally heavy. In future work, we will distill guidance into one- few-step generative models for efficiency and extend its applicability beyond U-Net-based designs to emerging frameworks such as Diffusson Transformers (DiTs).
Practical Applicability. Although iterative diffusion sampling is computationally demanding. LDMs=FreeAdapt is well suited for offline, quality-oriented UHD restoration workflows where visual fidelity takes precedence over runtime. Representative scenarios include film and televi- sion remastering, digital cultural heritage restoration, and large-scale remote sensing analysis, all of which routinely operate under non-real-time constraints and can therefore benefit from the superior perceptual fidelity enabled by diffusion priors.
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Acknowledgment. This work was partially supported by the National Natural Science Foundation of China (Grant No. 42530106, 42571507). the State Key Laboratory of Micro- Saero-Spaceeratt Rapid Design and Intelligent Cluster (Grant No. MS01240125).
Ethics Statement. This work aims to enhance the visual quality of ultra images, with primary applications in consumer photography, media content production. The objective is to mitigate quality degradation caused by hardware limitations or challenging environmental condi- tions, thereby contributing positively society. Given the increasing versatility of image restoration technologies, we explicitly advocate against their misuse, such as fabricating misleading content or employing them for malicious purposes.
Reproducibility Statement. We state that FreeAdapt is highly reproducible. Complete implemen- tation details are provided in Appendix A. All critical hyperparameters and algorithmic procedures necessary for reproducing our results are thoroughly documented in the Methods section and the supplementary appendix.
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A IMPLEMENTATION DETAILS
We adopt Stable Diffusion 2. 1a-- as the pre-trained generative prior in our experiments. During both the fine-tuning of the VAE-FT module and the inference of all compared methods, UHD im- ages are cropped into 512 x 512 patches with a stride of 256. For the VAE-FT module, we apply parameter-efficient LoRA to the VAE decoder with rank of 16. Training is conducted degraded-clean images from the UHD-LL (Li et al. 2023a UHD-Haze (Wang et al.) 2024a), and UHD-Blur (Wang al. 2024a) datasets. The learning 8, for a total of 60,000 steps. Notably, we train a single unified VAE-FT module for all restoration tasks. This module is directly applied across different tasks and even when switching base models (e.g., from LDM to DiffBIR), without requiring retraining, which demonstrates strong generaliza- tion ability. All experiments are implemented four NVIDIA RTX 3090 GPUs.
paired
is
For the backbone diffusion models, we employ task-specific pre-trained LDM (Rombach et al., 2022]. StableSR (Wang al. 2024b) and DiffBIR (Lin al., 2024a) to validate the broad adapt- ability of the FreeAdapt framework. Across all experiments, the number of denoising steps T is fixed to 50.
We provide the complete inference pipeline of our proposed FreeAdapt framework in Algorithm 1 The process begins with the generation of global reference latent representation rref which pro- vides reliable low-frequency structural information. Subsequently, during the high-resolution itera- tive denoising loop, FreqG (Ginee) and FeatG (Gtea) modules are jointly integrated at each step to progressively correct global inconsistencies and suppress unrealistic high-frequency artifacts, lead- ing high-fidelity UHD) restoration.
Algorithm 1: FreeAdapt Inference Pipeline
Input: Degraded UHD image Itg, pre-trained LDM (ea, VAE), total denoising steps T Output: Restored UHD image Irec
// Stage 1: Reference Generation
4r + Downsample(Iig)
// Downsample to native resolution // Encode the low-res image condition
Cr VABencode(Trr):
2T I);
// Initialize Gaussian noise in latent space
for t = to do
// Iterative denoising with LDM condition ref < Upsample(VAi))) (VA (x (x))); // Decode and upsample in pixel domain // Final clean reference latent // Stage 2: Guided High-Resolutionn Denoising
L 4 + co( 4r):
ref VAEencode (Tre));
20
Ggg VAE
// Encode UHD image as the main condition
2T ~ 1)
// Initialize high-resolution Gaussian noise
for to do
ref + add noise
20 noise ZT, 1): // Add corresponding noise to reference // Crop current latent into patches // Crop condition into corresponding
P. + CropPatches =x)
Cly + CropPatches Cig)
patches
// Initialize set for denoised patches foreach patch (pt, cp) E (Pr, do rre GeCCorrepondingPPcht"""""
P
// Get reference patch // Apply Frequency Guidance // Denoise patch with condition and Great
P/. + (Pt. ref),
Pt-1 t (p,,t,
inside U-Net
Pt- Pi-1 (p:-1}
21-1 +- StitchPatches(P..il))
// Stitch patches back with blending
// Stage 3: Reconstruction
rrec + VAE -
// Decode with standard or
VAE
return Irec
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(4) Sensilivitty Analy sis Froquency Guidlanee Filter Cocfficient (c)
(b) Sensitivity Analysis Fexture Guidanee Fision Wright (0)
213
11.23
205
000
013
Value (c)
Weight (0))
Figure & Hyperparameter sensitivity analysis on the UHD-LL dataset.
B HYPERPARAMETER ANALYSIS
To evaluate the sensitivity of the introduced hyperparameters, we conducted a systematic analysis on the UHD-IL dataset. Our study focuses on the two key parameters governing the proposed syn- ergistic guidance mechanism: the filter coefficient e in FreqG and the fusion weight in FeatG. Fig- ureßß @re@proovdde provides an overview of the evaluation results, from which it can be observed that FreeAdapt maintains strong robustness across broad parameter range. The default configuration (c 0.15, a = 0.20) achieves consistently favorable performance across different tasks and backbone mod- els, indicating that these settings fall within stable operating regime and do not require extensive tuning.
Analysis of Frequency Guidance Filter Coefficient (c). For the Frequency Guidance module, the coefficient e determines the bandwidth the low-pass filter responsible for stabilizing global phase information. In Figure aa), the curves illustrate that when e < 0.10, the constraint on low- frequency structure becomes insufficient, occasionally leading to color shifts or structural inconsis- tencies across patches. Conversely, values above 0.20 restrict the flexibility of the diffusion process, resulting in a slight reduction in texture diversity. Within the interval E [0.10, 0.20], however, the performance curves remain nearly flat, demonstrating that the frequency-dome domain guidance is intrinsically robust to moderate changes in the filter bandwidth.
Analysis Feature Guidance Fusion Weight (o). For the Feature Guidance module, the fusion weight a controls the strength of global context injected into local patches. The trend in Figure Bb) shows a clear trade-off between artifact suppression and detail preservation as a varies from 0.0 to 0.5. Under-guidance (a < 0.10) fails to adequately suppress high-frequency hallucinations in smooth regions, which negatively affects perceptual quality despite reasonable PSNR values. Over-guidance (a 0.30) places excessive emphasis low-resolution reference features, lead- ing to over-smoothing and a noticeable degradation in both PSNR and LPIPS. A stable performance plateau emerges within [0.15, 0.25), where 0.20 offers the most balanced trade-off between structural consistency and texture fidelity.
Correlation with Upscaling Factor. We further examine the relationship between hyperparameter choices and the upscaling factor. When restoring images upsampled to 8K resolution, patch-based inference faces increased uncertainty due to larger spatial gaps, and slightly higher values of and help anchor global structure and suppress hallucinations, For smaller upscaling ratios, patches inher- ently preserve more structural information, and correspondingly smaller settings of these parameters allow the model to focus more effectively on refining high-frequent frequerey details. Despite this correla- tion, the default configuration (c = 0.15, a = 0.20) proves consistently robust across standard UHD) restoration scenarios, eliminating the need for task-specific tuning.
C LIMITATIONS AND FUTURE WORK
Despite the encouraging results achieved by FreeAdapt, our method still has certain limitations, which also point to promising directions for future research.
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Computational Efficiency. The primary limitation of our approach lies in its computational and time cost. As an iterative denoising framework, restoring a UHD image requires performing dozens of denoising steps, which may become a bottleneck in scenarios demanding fast responses. Although our method is training-free and therefore saves substantial resources during adaptation, the inference speed still has room for improvement. A highly promising direction to distill our synergistic guidance mechanism into one-step or few-step generative models. Through knowledge distillation, expect to retain high restoration quality while improving inference efficiency by an order of magnitude, making the method more practical for real-world applications.
Practical Applicability and Use Cases. While LDMssFreeAdatt is not optimized for real-time or edge-side deployment, it is highly suitable for offline UHD restoration workflows where perceptual fidelity takes precedence over computational cost. In professional film and television remastering, for instance, frame-level processing is typically performed offline, and longer runtimes are accept- able for achieving visually coherent and artifact-free results. In the digital preservation of cultural heritage, the ability to recover nuanced textures and subtle structural details is far more critical than processing speed, making LDMs+FreeAdapp favorable choice. Similarly, large-scale remote sensing pipelines routinely handle extremely high-resolution satellite imagery in batch mode, where offline processing and high-quality reconstruction directly benefit downstream tasks. These exam- ples highlight that, despite its iterative nature, L.Ms*FrreAdapt provides clear practical value in domains where quality-centric restoration is essential, and future acceleration efforts may further broaden its applicability.
Benchmark Limitations and Generalization to Real-World Data. Current UHD-IR benchmarks rely on synthetic degradation pipelines because collecting paired real-world UHD data remains ex- tremely challenging. We acknowledge that this limits the diversity and realism of degradation pat- terns. As high-quality UHD datasets continue to emerge, we consider extending evaluation to real- world benchmarks an important direction for future work. This will allow more comprehensive assessment of FreeAdapt's generalization capability beyond synthetic settings and will help validate its robustness under practical, unconstrained degradations.
Architectural Adaptability. The current FFSG design primarily targets U-Net based latent diffu- sion models and their extensions such as ControlNet (Zhang et al. 2023). In this setting, FeatG is integrated into the self-attention layers of the decoder, and FreqG benefits from the hierarchical multi-scale feature processing of the U-Net. Recent progress in diffusion modeling has, however, architectures, most notably Diffusion Transformers (DiTs), which
shifted toward Transformer-bar now form the backhone of many state-of-the-arr diffusion systems. Although the present implemen- tation is built upon U-Net structures, the underlying principles of FFSG are not inherently tied to this architecture, and both guidance components can be adapted to DiTs with appropriate modifications.
FreqG is fundamentally architecture-agnosse tion before the denoising step. The fused latent can be passed through the standard patchification and tokenization stages of DiT model without changes to the backbone, which makes the frequency- domain guidance naturally compatible with Transformer-batt based diffusion architectures. FeatG. in contrast, requires adaptation since its current form relies on U-Net style self-attention. A feasible extension is to introduce a modified DIT block in which the local self-attention computed on high- resolution patch tokens is complemented by a global cross-attention mechanism that attends to ref- erence tokens extracted from the low-resolution guidance image. The outputs of these two attention paths are subsequently combined using the same linear blending strategy described in Eq. 9, after which the resulting tokens are processed by the standard feed-forward layers of the DiT block. This formulation preserves the core objective of FeatG, namely maintaining local detail fidelity while injecting global contextual cues, yet aligns it with the token-based computation of DiT models.
because operates directly on the latent representa-
D COMPUTATIONAL EFFICIENCY ANALYSIS
We provide a quantitative analysis of the computational efficiency of FreeAdapt in Table 5 which reports parameters, FLOPs, peak GPU memory usage, and inference latency for restoring a single 4K image (3840 2160). Although diffusion- based UHD restoration is inherently more computa- tionally demanding than regression- basesd architectures, FreeAdapt introduces only a marginal over- head compared with the baseline patch-based inference pipeline. For example, LDM-Qurr increases
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Table 5: Quantitative comparison of computational efficiency for restoring single 4K image. PI denotes patch-based inference, and DF denotes DemoFusion.
Model
Params
FLOPs
VRAM
Latency
ERR
1.31
307.52 G
7.11 GB
0.63
UHDformer
0.34
399.01 G
12.65 GB
0.60
Wave-Mamba
1,26 M
948.76 G
14.54 GB
1.36
UHDFour
32.08 M
596.13
2.48 GB
0.22
SwinIR
15.06
2764.31
6.85 GB
1.01
DrDamUHD
1.16
496.44 G
14.65 GB
0,56
FFTformer-Pl
15.79 M
58.93
3.60 GB
51.79
Restormer-Pl
19.94 M
46.60 T
2.51 GB
28.69
Uformer-PI
50.88 M
38.43
2.31 GB
13.79
LDM-PT
1243.19 M
954.62 T
6.23 GB
178.69
StableSR-PI
1295.69 M
1112.82 T
7.07 GB
212.32 S
DiffBIR-PI
1596.53
1233.20 T
7.27 GB
228.74
LDM-DF
1243.19 19 M
1236.31 T
6,26 GB
231.57
StableSR-DF
1295.69
1442.54 T
7.10 GB
278.09 s
DiffBIR-DF
1596.53 M
1718.30 T
7.31 GB
298.70 S
LDM-Ours
1243.19 M
1023.10 T
6.33 GB
192.67 s
StableSR-Oun
1295.69 M
1192.62T T
7.21 GB
232.93 s
DiffBIR-Ours
1596.53 M
1408.88
7.39 GB
247.03
FLOPs by approximately 7% relative to LDM-PI, yet yields substantially improved perceptual qual- ity. This cost-performance ratio highlights the efficiency of our guidance mechanism.
The overall computational load arises from three inherent properties of diffusion-based UHD restoration: the iterative nature of diffusion sampling, the substantial parameter count of pre-trained generative backbones, and the redundancy introduced by patch-based inference, where overlapping patches must be processed repeatedly to avoid boundary artifacts. Despite these sources of over- head, FreeAdapt avoids additional multi-scale stages or multi-pass fusion, enabling to remain more efficient than methods such as DemoFusion while consistently achieving higher restoration fidelity.
Future work will investigate techniques for further reducing inference complexity, including step distillation, architectural acceleration, and low-bit quantization, to make high-fidelity UHD restora- tion increasingly practical in resource-limited environments.
E
UHD IMAGE DERAINING RESULTS
To comprehensively evaluate the applicability of our method to UHD-IR tasks, we additionally duct experiments on a UHD image deraining dataset, 4K-Rain (Chen et al.) 2024). We compare -rupuudppee diffusion models (LDM, StableSR, and DinBIR) against state-of-the-art de- raining and UHD restoration approaches, including Uformer (Wang et al. 2022), Restormer (Zamir
al.
2022).
SwinIR (Liang al. 2021). UDR-Mixer (Chen al. 20044. UDR-S2Former (Chen
et al.
2023
DreamUHD (Lu et al. 2025b), and ERR (Zhao et al.) 2025).
Table summarizes the quantitative results on the 4K-Rain134 dataset. Traditional deraining mod- els (c.g., Uformer and Restormer) achieve strong performance on distortion-oriente metrics, but diffusion-based variants equipped with FreeAdapt show competitive or superior perceptual qual- ity. In particular, DiffBIR-Ours achieves the best second-bes scores on LPIPS, DISTS, CLIP- IQA, MUSIQ, and MANIQA, demonstrating its strong capability preserving realistic textures and producing perceptually faithful outputs under heavy rain degradation. In addition, Figure 9 provides qualitative comparisons on UHD rainy images. Existing approaches often produce over- smoothed regions, residual streak artifacts, inconsistent textures when encountering complex rain patterns. contrast, diffusion backbones equipped with FreeAdapt generate clearer struc- tures, better--reserved textures, and more coherent details, yielding visually stable restoration results
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Input
GT
Restormer
Swin
UDR_S2Gon S2former
StableSR-Ours
DirBIR-Ours
Input
GT
Restormer
Swin
UDR
ERR
StabeSR-Ours
DinBIR-Ours
Figure 9: Visual comparison between FreeAdapt and state-of-the-an UHD-IR methods the 4K- Rain 13k dataset.
Table 6: Quantitative comparison on 4K- 4K-Rainla 13k dataset (Chen et al. 2024 The symbols and
respectively represent that higher lower values indicate better performance. Bold represents the best and underline represents the second best.
Method PSNR f
SSIM f
LPIPS
DISTS 1
CLIPIQA f MUSIQ
MANIQA 1
Uformer
34.67
0.953
0.120
0.072
0.319
33.44
0.256
Restormer
34.09
0.955
0.132
0.088
0.304
33.51
0.256
Swinir
31.57
0.940
0.126
0.070
0,229
32.37
0.236
UDR-Mixer
34.28
0.950
0.133
0.086
0.344
33.17
0.255
UDR-S2former
27.43
0.913
0.199
0.105
0.358
29.64
0.228
DreamUHD
29.26
0.912
0.221
0.131
0.241
30.07
0.206
ERR
34.43
0,951
0.120
0.070
0,366
33.60
0.261
LDM-Ours
25.89
0.900
0.174
0.066
0.379
34.73
0.283
StableSR-Ous
28.04
0.912
0.157
0.056
0.379
34.23
0.277
DifiBIR-Our
29.11
0.937
0.111
0.039
0.384
34.98
0.284
across diverse scenes. These findings confirm that FreeAdapt generalizes well to the UHD deraining task and maintains high perceptual fidelity across both quantitative and qualitative dimensions.
F MORE EXPERIMENTAL RESULTS
To further demonstrate the superiority of our approach, provide additional full-resolutie quali- tative comparisons in Figure [0] Figure II and Figure 122 on the UHD-LL (Li ct al.) 2023a). UHD-
Haze (Wang et al. 2024a), and UHD-Blur (Wang et al. 2024a) tasks, respectively. These results clearly show that, compared with other state-of-the-art methods, our method consistently achieves advantages in recovering fine textures, suppressing artifacts, and preserving color fidelity.
Figure 1 Figure 14 and Figure 15 present visual comparisons between our FreeAdapt framework and other diffusion adaptation methods two different backbones, L.DM and DiffBIR, across the
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UHD-LL, UHD-Haze, Haze, and UHD-Blur tasks. The results show that methods such MultiDiffusion and DemoFusion often produce blurry outputs, repeated contents, or unnatural textures when han- dling complex scenes. In contrast, our method. through effective synergistic guidance, consistently generates structurally correct and detail-coherent high-quality images.
G STATEMENT ON THE USE OF LARGE LANGUAGE MODELS
In the preparation of this paper, we used large language models (LLMs) as an auxiliary tool. The primary role of LLMs was to assist with language polishing, phrasing improvements, grammar checking, and enhancing the clarity and fluency of certain sentences, ensuring that our research ideas and technical details are expressed more accurately and professionally. Importantly, LLMs were not involved in the core aspects of this work, including research conception, experimental design, result generation, or data analysis.
Input
GT
Restormer
UHDformer
Wave-Mamba
ERR
LDM-Ours
DinBIR-Ours
Input
GT
Restormer
UHDformer
LDM-Ours
Figure 10: Visual comparison between FreeAdapt and state-of-the-art UHD-IR methods the UHD-LL dataset.
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Input
GT
Restormer
UlDffomme
Uformer
ERR
LDM-Ours
DifTBIR-On
Input
Restormer
UlIDformer
Uformer
ERR
LDM-Ours
DitBIR-Ours
Figure 11: Visual comparison between FreeAdapt and state-of-the-art UHD-IR methods on the UHD-Haze dataset.
Input
GT
Restormer
UlDifomer
FFTYormer
ERR
LDM-Ours
DirBIR-
Input
GT
Restormer
UHDiormer
FFTformer
ERR
LDM-Ours
DiffBR--Ors
Figure 12: Visual comparison between FreeAdapt and otate-of-the-art UHD-IR methods on the UHD--Blur dataset.
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Input
GT
LDM-Muutidiiftssion
LDMM-Demofusion
LDM-Pixelsmith
LDM-Ours VAE-FT
Inpot
GT
HHrBIR-Multiiffuusi
DifBBIR-Demffusio
DiffBiR-Pixclmitth
DifiBIR-Ours w/o VAE-FT
Figure 13: Visual comparison of FreeAdapt against training-free diffusion-baan based adaptation methods on the UHD-LL dataset.
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Input
LDM-Muutidifussion
LDM-Demofussin
LDM-Pixelsmihh
LDM-Ours w/o VAE-FT
Input
MuitHR-Muiiiiffusionn
DifBil--Demofusion
Pinssit-
DifBBR--Our VAE-FT
Figure 14: Visual comparison of FreeAdapt against training- free di diffusion-baasee adaptation methods on the UHD- Haze dataset.
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Input
GT
Motidifliiitaasion
LDM-Demaofusion
LDM-Pixebmiith
LDM-Ours VAE-FT
418, 144
8TH.. 208
#18 144
18555 44A
168148 $EA
168466 EAA
ITD COL
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ANERS, 18445,
ANTUS, 18465,
CARERNE
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Input
GT
DinBIR-Multidffussion
448 20a
448 248
$4H
108444 d4a
168144 did
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1130 SUZAAFA
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the OITALL CheP) REAETT 5.9 EEEE.
Stcepr SAPERRL
303E7TE LAPEEAL
DimmiiR-Demofusion
DiftsiR-Pisesmihh
DiftBIR-Ours w/o VAE-FT
Figure 15; Visual comparison of FreeAdapt against training-free diffusion- based adaptation methods on the dataset.
24


Reviews:
[{"review_id": "HJOuEzHAnT", "reviewer": "Reviewer_B33R", "summary": "The paper proposes FreeAdapt, a training-free framework to adapt pre-trained diffusion models (e.g., LDM, DiffBIR) to ultra-high-definition image restoration (UHD-IR) tasks. The method first generates a structure-preserving reference image at a lower resolution, then during UHD latent denoising, applies (1) frequency-domain guidance that fuses low-frequency phase information from the reference to enforce global structural and color consistency across patches, and (2) feature-level guidance that injects reference features into self-attention to provide global context and suppress hallucinated textures. In addition, an optional lightweight VAE decoder fine-tuning module with multi-scale skip connections and LoRA is introduced to better reconstruct high-frequency details. Experiments on several UHD-IR benchmarks show consistent gains over both traditional UHD restoration networks and other training-free diffusion-based adapters, especially in perceptual metrics and visual fidelity.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper proposes a novel training-free framework that adapts diffusion priors to ultra-high-resolution image restoration via designed FFSG mechanism.\n2. The plug-and-play design can be seamlessly applied to different diffusion-based restoration models.\n3. The paper is clearly written and well structured.", "weaknesses": "1. While the paper correctly reviews MultiDiffusion, DemoFusion, and PixelSmith as training-free methods for high-resolution generation, these are later treated as baselines for UHD restoration without a dedicated discussion of the additional consistency constraints that restoration requires. It would be helpful to more explicitly clarify in what sense these generation-oriented methods are adapted to restoration, and how input–output consistency is enforced or evaluated beyond standard full-reference metrics.\n2. This paper lacks comparisons with the latest state-of-the-art uper-resolution or image restoration methods; many works mentioned in the related work section (e.g., DreamUHD, StableSR) are not included in the experimental evaluation, which makes it difficult to fully assess the competitiveness of the proposed approach.\n3. FFSG is implemented by directly modifying the self-attention blocks in the U-Net decoder, and the paper does not provide any discussion or experiments on how to extend the proposed guidance mechanism to diffusion transformers. Given that DiT-style models are becoming the mainstream choice for strong diffusion priors, this U-Net dependency may substantially restrict the method’s impact.", "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": "2mIuaHTKUl", "reviewer": "Reviewer_gDB6", "summary": "The paper addresses Ultra-High-Definition Image Restoration (UHD-IR), i.e. restoring 4K/8K images degraded by noise, blur, haze, low-light etc. The authors argue that existing UHD-IR methods (e.g. UHDFormer, ERR, Wave-Mamba) rely on architectural tricks and still hit bottlenecks due to the ill-posed nature of restoration. To overcome this, the paper proposes to leverage pretrained latent diffusion models (LDMs) as powerful generative priors. Directly applying LDMs to UHD images is problematic (patch-based inference causes stitching artifacts, loss of global context and detail due to VAE bottleneck). The core contribution is FreeAdapt, a training-free, plug-and-play framework that adapts off-the-shelf LDMs (and extensions like ControlNet) to UHD-IR. During inference, FreeAdapt applies a novel Frequency-Feature Synergistic Guidance (FFSG) mechanism and an optional VAE fine-tuning (VAE-FT). Specifically, at each denoising step, Frequency Guidance (FreqG) replaces the phase spectrum of the current latent patch with that of a low-resolution reference image, enforcing cross-patch structural consistency, while Feature Guidance (FeatG) injects global contextual features (from the reference) into the U-Net's self-attention to suppress hallucinated textures. The reference is obtained by downsampling the input and running one LDM denoising pass, yielding coherent low-frequency structure. Finally, a lightweight fine-tuning of the VAE decoder (with skip connections) is optionally applied to alleviate compression losses and sharpen details.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper's approach appears novel. While diffusion priors have been used in low-level vision, adapting them to ultra-high-res restoration with plug-and-play guidance is new.\n2. The method adopts a Plug-and-play design, which can be compatible with different diffusion models.\n3. The paper is generally well-organized and clearly written.", "weaknesses": "1. The paper should further clarify the distinction between high-resolution image restoration and high-resolution image generation. To my knowledge, DemoFusion is a method for high-resolution generation rather than restoration. The key difference lies in that restoration tasks emphasize maintaining consistency between the low-resolution and high-resolution images, whereas training-free high-resolution generation methods do not impose such a constraint.\n2. Computational cost: diffusion inference with dozens of steps and many patches is slow and resource-intensive. Indeed, patch-based diffusion on 8K images is inherently heavy.\n3. FFSG is designed for U-Net-based LDMs (with self-attention). Emerging diffusion architectures (e.g. Diffusion Transformers) may not readily accommodate the same attention-based feature injection. A more important point is that the DiT architecture is currently the mainstream (such as SD3 and FLUX), which limits the application of the method presented in this paper.\n4. This method introduces several new hyperparameters. The paper only provides default values for these parameters, but does not offer any sensitivity analysis on how these hyperparameters affect the final results. This makes it impossible for readers to determine whether the method is easy to adjust and whether a large amount of cumbersome parameter tuning is required when applied to new tasks or backbone models.\n5. The paper lacks comparisons with the latest state-of-the-art super-resolution or image restoration models, which makes it difficult to fully assess the effectiveness of the proposed method.", "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": "ChXlk3MjQI", "reviewer": "Reviewer_H5xg", "summary": "This paper introduces a training-free method FreeAdapt to utilize pre-trained diffusion priors for ultra-high-definition image restoration. The core of the framework is a training-free frequency-feature synergistic guidance mechanism, which enforces global structural consistency through frequency guidance and suppresses artifacts while preserving local detail fidelity via feature guidance. By fine-tuning VAE decoder, it further enhances the reconstruction of texture and detail. Extensive experiments demonstrate the effectiveness of the proposed method.", "questions": "● More experiments need to be conducted to demonstrate the proposed ``training-free'' approaches without fine-tuning decoder network.\n● The authors are recommended to provide image results in supplementary material.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "● The proposed frequency and feature guidance strategies effectively resolves the structure inconsistencies and artifacts in patch-based inference results.\n● By fine-tuning VAE decoder, it further enhances the reconstructed texture and mitigates the lossy compression characteristic.\n● Extensive experiments demonstrate the effectiveness of the proposed method on three synthetic benchmark.", "weaknesses": "● Training the VAE decoder conflicts with the authors' claim of ``training-free approach''. In fact, in Fig. 3, using only FreqG/FeatG yields only marginal performance gains. I highly doubt the validity of the restoration results, which stems from fine-tuning the VAE decoder network across three synthetic degradation scenarios.\n● The part of ``reference image generation'' is confusing. Transforming a degraded image to a clean image require performing sdedit process or utizling a pre-trained I2I backbone. The authors need to provide more details about it.\n● The testing scenarios are limited. Current benchmark are constructed by applying several synthetic degradation factors, which can be fitted by only fine-tuning the decoder network with the corresponding datasets. The authors are suggested to perform experiments on AI-generated or real-world benchmark.", "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": "V8JuVTntIV", "reviewer": "Reviewer_PoKA", "summary": "The paper proposes FreeAdapt, a plug-and-play framework designed to enhance ultra-high-definition image restoration (UHD-IR) using diffusion priors. To address the global inconsistency and detail loss issues of existing latent diffusion models (LDMs) caused by patch-based inference and VAE bottlenecks, the paper introduce a training-free Frequency Feature Synergistic Guidance  mechanism that provides guidance at each denoising step. FFSG includes two modules: Frequency Guidance, which fuses phase information from a reference image in the frequency domain to ensure structural consistency, and Feature Guidance (FeatG), which injects global contextual information into U-Net self-attention layers to suppress artifacts and preserve fine details. Additionally, an optional VAE fine-tuning module with skip connections is proposed to further improve texture reconstruction. Experiments show that FreeAdapt achieves superior quantitative and qualitative performance compared to existing UHD-IR and diffusion-based adaptation methods.", "questions": "If the authors can address the aforementioned issues, I would consider raising my evaluation score.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. It achieves promising results in terms of perceptual quality metrics.  \n\n2. Exploring the application of diffusion models in UHD image restoration is an interesting direction.", "weaknesses": "1. The second claimed contribution — the frequency and feature guidance modules — appears highly similar to the approach proposed in [1]. Please clearly describe the differences between your method and [1]. If there are no substantial distinctions, these components should not be presented as core contributions.\n\n2. How does the proposed method compare in terms of computational efficiency with existing UHD-IR approaches? Although I understand this may be a limitation of the current method, the paper does not provide any relevant data. Please report a quantitative comparison including GPU memory usage, inference speed, number of parameters, and FLOPs.\n\n3. Applying diffusion models to UHD image restoration incurs substantial computational costs, making deployment on edge devices challenging. Compared with existing methods such as UHDformer and ERR, the practical applicability and significance of the proposed approach remain unclear. A discussion of its real-world deployment potential, efficiency trade-offs, and specific use cases is necessary.\n\n4. The paper lacks comparative results on UHD image deraining.\n\n[1] FAM Diffusion: Frequency and Attention Modulation for High-Resolution Image Generation with Stable Diffusion. CVPR 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)