GenDR: Lighten Generative Detail Restoration
Authors: Yan Wang
Shijie Zhao
Kexin Zhang
Junlin Li
Li zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion
Super-Resolution
Score distillation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Although recent research applying text-to-image (T2I) diffusion models to real-world super-resolution (SR) has achieved remarkable progress, the misalignment of their targets leads to a suboptimal trade-off between inference speed and detail fidelity. Specifically, the T2I task requires multiple inference steps to synthesize images matching to prompts and reduces the latent dimension to lower generating difficulty. Contrariwise, SR can restore high-frequency details in fewer inference steps, but it necessitates a more reliable variational auto-encoder (VAE) to preserve input information. However, most diffusion-based SRs are multistep and use 4-channel VAEs, while existing models with 16-channel VAEs are overqualified diffusion transformers, e.g., FLUX (12B). To align the target, we present a one-step diffusion model for generative detail restoration, GenDR, distilled from a tailored diffusion model with a larger latent space. In detail, we train a new SD2.1-VAE16 (0.9B) via representation alignment to expand the latent space without increasing the model size. Regarding step distillation, we propose consistent score identity distillation (CiD) that incorporates SR task-specific loss into score distillation to leverage more SR priors and align the training target. Furthermore, we extend CiD with adversarial learning and representation alignment (CiDA) to enhance perceptual quality and accelerate training. We also polish the pipeline to achieve a more efficient inference. Experimental results demonstrate that GenDR achieves state-of-the-art performance in both quantitative metrics and visual fidelity.

Published as a conference paper at ICLR 2026
GENDR: LIGHTEN GENERATIVE DETAIL RESTORATION
Yan Wang, Shijie Zhao", Kexin Zhang, Junlin Li, Li Zhang ByteDance (wangyan.my my, zhaoshi jie. 0526) 8bybedncttdaccccoom
NR-IQA
CLIPIAA
Q-Align
Husnetrral
NUSIO
274
Efficinnc
9316
1.93
Pareare
LPIPS
-QA
TOMO
Dreen
Zoomed I.R
OSEDiff (103ms)
InvSR (115ms)
GenDR (77ms)
Quantitative Comparison
ceffuusion-based models (our method provides more details and higher fidelity with fewer time cost tested with 512 on A A100) (left) and quantitative comparison of representative SR methods (right), both of which demonstrate the advanced performance of the proposed GenDR. (Zoom in for best view.)
Figure 1: Visual comparison between proposed GenDR and recent saate-of-the-ae
ABSTRACT
Although recent research applying text-to-image (T2I) diffusion models to real- world super-resolution (SR) has achieved remarkable progress, the misalignment of their targets leads to a suboptimal trade-off between inference speed and detail fidelity. Specifically, the T2I task requires multiple inference steps to synthesize images matching to prompts and reduces the latent dimension to lower generating difficulty. Contrariwise, SR can restore high-frequent details in fewer inference steps, but necessitates a more reliable variational auto-encoder (VAE) to preserve input information. However, most diffusion- based SRs multistep and use 4- channel VAES, while existing models with 16-channel VAFs are overqualified diffusion transformers, e.g., FLUX (12B). To align the target, present one-step diffusion model for generative detail restoration, GenDR, distilled from tailored diffusion model with larger latent space. In detail, we train new SD2.1-VAEI (0.9B) via representation alignment to expand the latent space without increasing the model size. Regarding step distillation, we propose consistent score identity distillation (CiD) that incorporates SR task-specific loss into score distillation leverage more SR priors and align the training target. Furthermore, we extend CiD with adversarial learning and representation alignment (CiDA) to enhance perceptual quality and accelerate training. We also polish the pipeline to achieve a more efficient inference. Experimental results demonstrate that GenDR achieves state-of-ther the-thhaartt art performance in both quantitative metrics and visual fidelity.
INTRODUCTION
Image super-resolution (SR) is classical low-level problem to recover high-resolution (HR) image from the low-resolution (LR) version (Dong et al., 2016; Lim et al., 2017). Its core aim is to repair missing high-frequency information from complex or unknown degradation in real- world scenarios with the help of learned priors. To reconstruct more realistically. the existing
"Corresponding Author.

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Published as a conference paper at ICLR 2026
methods SRGAN, SRGAN, RealESRGAN introduce generative adversarial networks (GAN) to reproduce the details and have revealed advanced performance. Recently, modernized text-to-image (T2I) models, e.g., Stable Diffusion (SD) and PixArt, have demonstrated their ability synthesize high-resolution images with photographic quality, offering a new paradigm to replenish details.
To assign image SR objective to these T21- oriented diffusion models, Wang et al. (2024b); Lin al. (2024) leverage additional control mod- ules such as controlnet and encoder to guide T2I model to generate HR images from the noise. Although these methods exhibit supe- rior restoration quality compared to GAN-based methods, two disturbing problems persist that restrict the practical usage of diffusion- based SR: slow inference speed and inferior detail fi- delity. To address these issues, OSEDiff (Wu et al., 2024b) employs variational score distil- lation (VSD) (Wang et al., 2024d) through low- rank adaptation (LoRA) (Hu et al., 2022) to di- rectly distill a one-step SR model from the T2I model, which significantly improves through- puts but weakens generation quality. Dream- Clear (Ai et al., 2025) employs two control- nets and PixArt-a (Chen et al., 2023a), generating more faithful results. However, existing methods are caught in the dilemma that improving detail fidelity brings computational overhead (larger base model/additional assistant module), lead- ing inefficiency while accelerating inference results in unacceptable performance drop since they neglect critical underlying.
Initial Distribution
Target Distribution
Mutiple Stop 4
T21 task
Consisann Nicisa
VAE4
HQ-VAE4
learn
from
Mutpple
CC 0RD-27..8 2..89
Goussian Netia
HO HAE3S
SR task
Decal
Easy
LOWALA
High Ficeity
PRD OZ
LQHVATTG
prompts to guide
divergent task objectives make dilemma. Compared to T2I task, SR task generates only details from LQ to HQ but needs to preserve more information from LQ, demanding fewer inference steps and larger latent capability. We visualize the latent distribution on ImageNet- val ImageNet with t-SNE.
Figure 2: Motivation:
As shown in Fig. 2, we identify divergent task objectives between T21 requiring more inference steps and low-dimensional latent space and SR requiring fewer inference steps and high--iimensionl latent space as the main contributors to the above problems. This finding is based on the following observations:
Divergent generating difficulty: T21 models start from a sampled Gaussian noise to generate both low-frequency content and high-frequency details aligned with textual prompts. Nevertheless, the low-frequency structural and semantic components are well-defined in LR for the SR task, primarily demanding detail generation. Consequently, ideal SR-tailored diffusion model can theoretically use fewer inference steps since it focuses on details and has no need to align with text.
Divergent reconstruction demands: For T2I, expanding latent space inherently increases model complexity and training difficulty, leading most frameworks adopt a 4-channel VAE balance synthesis quality and computational efficiency. Although this trade-off optimizes performance for T21, becomes barrier for reconstruction tasks like SR. VAE4s stumble to preserve intricate details within compressed latent representation, resulting in irreversible detail loss and structural distortion (Dai et al., 2023; Esser et al., 2024).
To address these limitations holistically, this work provides systematic solution, namely GenDR, for real-world super-resolution (SR), particularly targeting faithful and intricate Generative Detail Restoration. To build this model, we introduce several innovations:
Tailored Base Model. Since SR tasks demand larger latent space, we construct GenDR with 16-chamnel base model. However, existing models (DiTss like SD3.5 and FLUX) are overqualified for generating details and prohibitively large to exacerbate the dilemma between quality and speed. For instance. to execute x4 SR for 256x25 256 input on a ILUX-basee SR, the one-step processing costs over 40GB GPU memory and 1.4s runtime, which is 5.3x and 11 11.4x larger than SD2. 1-bsed
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models. Thus, based on SD2.1 and open-source VAEI6, we construct an SD2. -VAE1 suitable base model for diffusion-based restoration tasks.
Advanced Step Distillation. To minimize the inference process to one step, we perform step distillation for GenDR. Unlike existing methods (Wu et al., 2024b; Sauer et al., 2024b) that directly employ score distillation from T2I, we integrate task-specific consistent priors from SR into score identity distillation (SiD) (Zhou et al., 2024b), proposing Consistent score identity Distillation (CiD), which mitigates adverse effects caused by inconsistencies in the training distribution and over-reliance imperfect score functions. Furthermore, we introduce CiDA, which incorporates CiD with representation Alignment and Adversarial learning to accelerate training and restore high-diversity details, avoiding the "fakeness" of Al-generated images. In practice, apply low-rank adaptation (LoRA) and model-sharing strategies to implement the CiDA training scheme more efficiently.
Simplified Pipeline. We construct simplified diffusion pipeline comprised solely of VAE and UNet. We remove the scheduler and conditioning modules and use fixed textual embeddings to enable efficient deployment.
Overall, GenDR gains remarkable improvement over existing models in objective quality/efficieney metries (Tab. 1), subjective visual comparison (Fig. 5), and human evaluation (Fig. 6).
2
RELATED WORK
2.1
GENERATIVE PRIOR FOR SR
Existing generative wrior-baedd SR methods can broadly categorized into GAN prior- based and diffusion prior-based paradigms. In detail, GAN can produce realistic synthesis by enforcing an adversarial updating between the discriminator and generator. Given the generator G and discriminator D, GAN cyclically optimizes them with varied adversarial losses:
C = =ExA xxxx)) [In(1 D(x,)) InD()x)) C =Ex, (nDxx))
where Xg. Xh, and x are SR images, high-quality, and low-quality images for SR tasks. Based on the target, GAN-based SR models (Zhang et al., 2021; Wang et al., 2018) exploit adversarial training to synthesize visually pleasing details at the cost of occasional instability during optimization. As milestone, Real-ESRGAN (Wang et al., 2021) introduced a high-order degradation datapipe to synthesize LR-HR training pairs.
Diffusion model decouples the image synthesis into forward and reverse processes in the latent space to stabilize the training and generated quality. For forward diffusion processing, the Gaussian noise (O, I) is added with the time t-related variance Be E (0, 1) to obtain the immediate latent 2: 2: = ā,z + Bie, where a, diffusion model predicts the noise thus obtaining the initial latent 2n (z. B,,)/āa. For SR tasks, better-starting node, i.e., the LR image 2,, can enable an efficient and simplified reverse function calculate restored latent Zg:
B, and ā = (I o..). During the reverse process, the
21 Be, 60 (e1(22544 āt.
Zg
(2)
where 6o represents the noise-predicting network. Diffusion-based SR models (Wu et al., 2024c; Zhang et al., 2024; Ai et al., 2025; Yang et al., 2024) finetune base model (e.g., UNet and DIT) or train an extra conditioning model (e.g., Encoder and ControlNet) to guide iterative generation that aligns to the low-quality input. For instance, DI (Lin et al., 2024) introduced preclear module and IRControlNet to balance fidelity and detail generation. FaithDiff (Chen et al., 2024b) fine-tuned an auxiliary encoder for SDXL (Rombach et al., 2022). Recently, Yi et al. (2025) proposed Transfer VAE Training (TVT), which tried to solve fidelity problem by training better VAE for diffusion pipeline, which achieves the same compression rate as GenDR.
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2.2 STEP DISTILLATION
To reduce inference cost, massive research has focused on distilling multiple-step diffusion processes into few-step frameworks. As pioneering work, DreamFusion (Poole al., 2022) introduced score distillation sampling (SDS) to transfer knowledge from diffusion to arbitrary generators, which paves the way for step distillation. ProlificDreamer (Wang et al.. 2024d) and Diff-instruct (Luo al., 2023) utilized variational score distillation to enhance generation diversity and stability through probabilistic refinement. Sil) (Zhou al., 2024b) further ameliorated the score distillation with identity transformation for stable training.
3 METHODOLOGGY
3.1
16-CHANNELS VAE MATTERS
To tailor a base model with a larger latent space for the SR task, we develop the UNet from SD2. in the latent space of 16-channel VAE*. To speed up training, we conduct full-parameter optimization using the representation alignment strategy (Yu et al., 2024b). Given input image X,, intermediate latent Z,, encoded output h, fol (zz). REPA aligns projected results h, with the representation be = =xx,) obtained by pre-trained encoder E, egg. DÍNOv2 (Oquab et al., 2023):
N
(repa)
- Exx
1 N
Σ
sim (he [n], (hb. (rl))
(3)
In practice, we insert the multilayer perceptron (MLP) as h after the first downsampling block in UNet. To further improve the model's comprehension of image quality attributes, i.e., guality-assessmmg terminologies like compressed and blurred, we refine the base model following the DreamClear (Ai et al., 2025).
3.2 CID: CONSISTENT SCORE IDENTITY DISTILLATION
The existing score distillation methods (Poole et al., 2022; Wang et al., 2024d; Zhou et al., 2024b) based the insight: If generator Gg produces outputs P3 approximating real data distribution Fr, the score model 2/ trained by P. should converge to pretrained model & trained by P...
Score distillation. Given the fixed real score model @ and updated fake score model vh the restoration network Go. generated latent Z, = Go(zi). sampled timestep t, and noise e, the VSD (Wang et al., 2024d) cyclingly update and va by:
(vi) = E,,,a +Be Bic ((t)(e((z;itte) c <p(2it,))) Ong 00
V VoLo
Cg -Er, [llew
el| -||
where w!) is time-aware weighting function. c represents scores (noise) predicted by real/fake score networks. indicaaes the prompt condition.
Score identity distillation. While VSD provides a tractable framework, it neglects the gradient of the fake score network and is difficult to ensure that ey achieves convergence, leading to unstable training and susceptibility to local optima due to inadequate convergence guarantees. Following Zhou et al. (2024b), we leverage score identity transformation to pre-estimate an ideal value ey (Z;;t.c) 3, Vz, logpo (z.) for cs, incidentally reducing the dependence on e,::
(!I) E. R,,cz (Z t, c) - e (z c) SiD
(5)
(2:; () - e)] C E Eraan (2))) (, - ())
Consistent score identity distillation. strategies are tailored for the T2I task. The existing SR diffusion models, egg,, ADDSR (Xie
Despite effectiveness, the dbove-mentioned distillation
'huggingface: sabilityaistabbeediiffiiin---- Phgggingface: ostrislvac-k1-f8d116
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LoRA: V
UNet
Score Nework
Trainable
Frozen
Fale Stare
Shared
CiD Loss
Racildo pre
GenDR:
Diffusion
LoRA: +
REPA Loss
GAN Loss
DINOV2
DisHead: h
Figure 3: Illustration of the proposed CiDA training scheme for GenDR. GenDR and base score network are initialized with SD2.I-VAEI6. The real and fake score networks are implemented by LoRA. LR latent is fed into GenDR to restore SR-latent and representation REPA loss Eq. (3),
et al., 2024) and OSEDiff (Wu al., 2024b) apply Eq. (4) directly Eq. (4) additional loss of regularization without any specific modification. However, for the SR task, directly using Eqs. (4) and (5) poses difficulties since T2I (aligning text embedding) and SR (aligning image embedding) have distinct targets and varying training distributions, leading to quality and content inconsistency. Consequently, these methods employ regression loss (L1, MSE) to ensure consistency between generated latent 2g and HR target Zn:
<(mee) Er,c ||zg z
To address the issue, we optimize the "fixed" real score network with to align its output distribution with the target high-fidelity image manifold, which ensures the real score network generates reliable priors for distillation. Following Zhou et al. (2024a), we regulate Eqs. (4) and (5) in latent space and introduce classifier-free guidance (CFG) (Ho & Salimans, 2022) with quality-related prompts to enhance guidance quality. Overall, the primary CiD can be formulated as follows:
C = -E,,, -6 tBc (zcitt) - 1=]
C. = - (z t,c) zn 11].
((2) E,,,,
- fu.n t, c). fox - -zg)l,
where Jaa denotes scores (latent) predicted by real/fake score networks. [x(2;; = [(2,, ()) x- (z(ztt,e) fZz; t, 2)), represents empty prompt embeddings.
Similar to VSD and SiD, the above step distillation framework is data-free and relies on generated results zg. which introduce instability due Z 's fluctuating quality and content. To mitigate this, we leverage the optimal latent for 0 in Eq. (6) to replace Zy with 2₁, as an identity transformation.
0 Et, (w()) (2,it, fer (2.; c). Ja.n (2;t,c) 2x,)
(8)
Overall, to circumvent the inability to compute Voy., we follow Zhou al. (2024b); Huang al. (2024) to combine C/ and ((3) through empirical weighting & to ensure unbiased optimization:
cicid) =C EC
3.3 CıDA: CID WITH ADVERSARY AND ALIGNMENT
Following DMD2 (Yin al., 2024) and REPA (Yu et al., 2024b), we further develop the CiD (Eq. (9)) with adversarial learning and representation alignment. Based on Eq. (1), we examine the generated latent Z, with pre-trained unet Φ and extra discriminative heads h:
H'
W
(and))
Inh(fo(z)))i..),
(10)
H'WI
==
where H' W denotes the spatial dimensions of the discriminator's output feature map.

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We also reintroduce representation alignment in Eq. (3) as effective regularization term. Combined with the CiD objective in Eq. (9) and adversarial loss in Eq. (10), the final target loss integrates:
C
A1 + Ag Xg
(11)
where 22233 are weighting coefficients balancing the contributions of distillation, adversarial, and alignment terms.
Implemetation with LoRA. CiDA consists of one trainable generator and two trainable score networks, which cost huge GPU memory and computation resources for parameter updating across three UNets. To alleviate this burden, we utilize the low-rank adaptation (LoRA) Hu et al. (2022) for fake/real score networks reduce the optimizable parameters. In addition, we share the base model for score networks and the feature extractor of the discriminator to further reduce the memory footprint of storing local models. In Fig. 3, we illustrate the detailed implementation of the proposed CiDA framework. In Appendix A.2, we present the detailed training schema for CiDA.
3.4 SIMPLIFIED DIFFUSION PIPELINE
To enable efficient inference without relying on complex preclear models or conditioning mod- els, we design GenDR using simplified archi- tecture comprising only a UNet and VAE. Since GenDR executes a one-step calculation, we elim- inate the scheduler by empirically assigning āe 3, = 0.5 across all timesteps t. Addition- ally, we discard the text encoder and tokenizer, replacing them with several fixed-prompt em- beddings stored locally to reduce computational overhead and ensure deterministic generation. Fig, shows the extremely laconic pipeline of GenDR.
Faad imbsriig drnming, CTS 000000000 88
UNet
GenDR
Figure 4: Illustration of proposed GenDR pipeline.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Training details. For SD2.I-VAEI6. we train on selected high-quality images (Wang et al., 2022; Rombach et al., 2022). The training resolution is 512x512 and 1024x 1024, which aligns to SD2.1 and SDXL. We use -fffl-of (Rajbhandari et al., 2020) and gradient accumulation (steps=8) extend the batch size to 2048 8 NVIDIA A100 GPUs. The fixed learning rate le and default Adam is adopted to optimize UNet during 100k iterations.
For GenDR, we initialize Go and fo. with SD2.1-VAE16-511 for Go on the LSDIR (Li et al., 2023), FFHQ (Karras et al., 2019), and above-selected images. As fo.ú. we set LoRA rank and alpha as 64 and 128, respectively. Referring to previous setups (Lin et al., 2024; Wu et al., 2024c;b), we randomly crop 512x512 image patches to generate LR-HR pairs through mixed pipeline of Real-ESRGAN (Wang et al., 2021) and APISR (Wang et al., 2024a). During 50k iteration training. networks are optimized via AdamW W optimizers with batchsize 1024 and learning rate 5 on NVIDIA A100-80C We start- tiietttimtttep... t2 500 for GenDR, and randomly sample timesteps / E [20.-. 10 (first 10k)/1, 0.01, and 0. 1, respectively. Specifically, we use (() C/ sg[llz) h To.c(2.) 1, e)ll as the time-related function to modulate CiDA loss.
and conduct full parameter training
979} for CiDA score networks fo.. The loss coefficients 21, are
Testing details. Following recent work (Yue et al., 2024b), we evaluate the proposed GenDR with the synthetic dataset ImageNet-Test (Deng et al., 2009) and several real-world test sets, RealSR (Cai et al., 2019), RealSet80 (Yue et al., 2024b), and ReaILR200 (Wu et al., 2024c). The ImageNet-Tee Test contains 3000 images under multiple complicated degradations. RealSR has 100 HR-LR image pairs taken by Nikon and Canon cameras. The RealSet80 and ReaILR200 contain 80 and 200 low-resolution images without ground truth. In the test phase, official codes and settings examine the

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Published as a conference paper at ICLR 2026
Image 9
Real ESRGAN
StableSR-33 -0
DUMBIR-50
SecSR-50
DreamCleur 50
LQ
HATGAN
SinSR
Lincola
Real-ESRGAN
StableSR
DifiBIR-50
SceSR-50 -00
DreamClear-50 500
ReaG-N
SinSR-1
InvSR-1
GenDR.I
Figure 5: Visual comparison of GenDR with other methods for x4 task on RealSet80 dataset.
compared models for fairness. For GenDR, the positive/negative prompts are fixed to provide general discription'.
Evaluation metrics and compared methods. We leverage seven commonly used metrics to eval- uate image quality, including full-reference metrics (FR-IQA): PSNR, SSIM (Wang al., 2004), LPIPS (Zhang et al., 2018). and TOPIQ (Chen et al., 2024a), and no-reference metrics (NR-IQA): PI (Blau et al., 2018), NIQE (Mittal al., 2012), LIQE (Zhang al., 2023), MANIQA (Yang et al., 2022), ClipIQA (Wang et al., 2023), MUSIQ (Ke et al., 2021), ARNIQA (Agnolucci et al., 2024), Q-Align (Quality) (Wu al., 2024a), and DeQA (You al., 2025). We also conduct user and MLLM preference studies to obtain comprehensive evaluation of image quality.
4.2 COMPARISON WITH
Quantitative comparison. To comprehensively evaluate the performance of GenDR, we compare it with numerous state-of-th of-the-art methods, including Gaseb models: RealESRAN (Wang et al., 2021) and Real-HATGAN (Chen et al., 2023b), multiple-step diffusion models: StableSR (Wang al., 2024b), DifTBIR Lin al. (2024), SeeSR (Wu et al., 2024c), and DreamClear (Ai et al., 2025), one-step diffusion models: SinSR (Wang al., 2024c), OSEDiff (Wu et al., 2024b), and InvSR (Yue et al., 2024a). Inclusively, our GenDR obtains superior restoration quality across all three benchmark datasets. As shown in Tab. 1, GenDR surpasses all existing one-step models by a large margin and can compete with multiple-step diffusion models. Specifically, GenDR achieves the highest LIQE, MUSIQ, and Q-Align on all benchmarks. Moreover, add total parameters and inference time in Tab. 1, where the GenDR is the fastest and second smallest diffusion model. Compared to recent DreamClear, the GenDR gains about 89.5 acceleration and only uses half of the parameters.
Qualitative comparision. Fig. 5 exhibits the visual comparison for GenDR with other approaches. Generally, GenDR presents the best quality in terms of blurry removal and detail recovery. For Image
"reatism photo, best quality, realistic detailed, clean, high righuresolutton, best quality, smooth plain area, high-fidelity, clear edge, clean details without messy patterns, high-resolution, noise, high-fidelity, 4K, 8K, perfect without deformations, photo taken the style of Canon EOS -style raw"
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Table 1: Quantitative comparison (average Parameters, Inference time, and IQA metries) on both synthetic and real-world benchmarks. The sampling step number is marked in the format of "Method name-Steps" for diffusion-ber based methods. The best results for all methods are highlighted in bold and underlined, while the best one-step diffusion methods are reported in italic.
Metrics
Methods
#Params
PSNRT
SSIM
LPIPS
NTQF
LIQET
ClipIQAt MUSIQI
Q-Alignt
Real-ESRGAN
GAN-CNN
26.62
0.7523
0.2303
4.4909
3.8414
0.5090
64.81
3,4230
Real-HATGAN
GAN-Tram -rans
27.15
0.7690
0.2044
4.7834
3.5717
0.4594
63.43
3.3244
StableSR-50
SD 2.1-base
26.00
0.7317
0.2327
4,9378
3,6187
0.5768
64.54
3,4378
ImageNet Test
D5
SD 2.1 -hase
25.45
0.6651
0.2876
4,9289
4.6378
0,7486
73.04
4.3228
SeeSR-50
2-hase
25.73
0.7072
0.2467
4.3530
4.5384
0.6981
72.25
4.2412
DreamClear-50
PixArt-a
24.76
0.6672
0,2463
5.3787
4,4298
0.7646
70.08
4.0919
SinSR-1
LDM
26.98
0.7308
0.2288
5,2506
3,9410
0.6607
67.70
3,8090
OSEDifI-1
SD 2.1-base
24.82
0.7017
0.2431
4.2786
4.5609
0.6778
71.74
4,0674
InvSR-1
SD Turbo
23.81
0.6777
0.2547
4,3935
4.5601
0.7114
72.38
3,9867
GenDR-1
SD 2.1-VA116
24.14
0.6878
0.2652
4.1336
4.8096
0,7395
74.68
4.3612
Real-ESRGAN
16.70M
25.85
0.7734
0.2729
4.6788
3.3372
0.4901
59.69
3,9185
Rcal-HATGAN
20.77M
26.22
0.7894
0,2409
5.1189
2.8875
0.4336
58.41
3,8353
StableSR-50
1410M
24.52
0.6733
0.3658
3.4665
3.5612
0.6897
66.87
3,9862
DifTBRR-50
1717M
26,28
0.7251
0.3187
5.8009
3,3588
0.6743
64.28
3,9182
RealSR
SecSR-50
2524M
26.19
0.7555
0,2809
4.5366
3,7728
0.6826
66.31
3,9862
DreamClear-50
2212M
24.14
0.6963
0.3155
3.9661
3.5452
0.6730
63.74
3,9705
SinSR-1
119M
25.99
0.7072
0 0,4022
6.2412
3.0034
0.6670
59.22
3,8800
OSEDifI-1
1775M
24.57
0.7202
0,3036
4.3408
3,9634
0.6829
67.
4,0664
InvSR-1
1289M
24.50
0.7262
0.2872
4.2218
4.0346
0.6919
67.47
4,2085
GenDR-1
933M
23.18
0.7135
0.2859
4. 1588
4.1906
0.7014
68.36
4.2388
Metrics
Methods
Runtime4
PIJ
ARNIQAT DeQAt
NIQE
LIQET
ClipiQAt
MUSIQU
Q-Align
Real-ESRGAN
36ms
3.8843
0.6538
3.8906
4.1517
3.7392
0.6190
64.49
4. 1696
Real-HATGAN
116ms
4.1817
0.6378
3,8444
4.4705
3.4927
0.5502
63.21
4.1077
StableSR-50
3731ms
3.0314
0.6776
3.8787
3,3999
3.8516
0.7399
67.58
4,0870
RealSer80
Dif
6213ms
3.9544
0.6802
4.0668
5.1389
4.0472
0.7404
68.72
4.3206
SccSR-50
6359ms
3.7454
0.7170
4.0728
4.3749
4.2797
0.7124
69.74
4,3056
DrcamClear-50
6892ms*
3.4157
0.6738
3,9429
3.7257
3,9628
0.7242
67.22
4.1206
SinSR-1
120ms
4.2697
0.6638
3,9713
5.6103
3.5957
0.6634
63.79
4,0954
OSEDifT-1
103ms
3.6894
0.6883
4.0916
3.9763
4.1298
0.7037
69.19
4.3057
InvSR-1
115ms
3.4524
0.7172
4.0045
4.0266
4,2906
0.7271
69.79
4,3014
GenDR-1
77ms
3.5518
0.7321
4.1289
3,9750
4.5248
0.7424
71.57
4.4525
inference time calculated under using
A100 GPU image.
from RealSet80, GenDR is the only model to restore the entire chrysanthemum and correctly split foreground and background. As Image Lincoln, GenDR recovers clearer eyes and more natural skin from the degraded input. The visual results demonstrate the splendid recovery performance of the proposed GenDR. Appendix A.4.4, we provide more visual comparisons.
User studies and MILM evaluation. For thorough comparison between diffusion-based SR, we conduct user studies to compare images restored by GenDR against SinSR, OSEDiff, and DreamClear. Fig. shows that our GenDR convincingly outperforms other diffusion-based models. For fur- MLLMs like Q-Align and DeQA, to enable human- like evaluation. Particularly, GenDR surpasses the second place by large margin. Compared to recent InvSR, Q-Align and DeQA improved by 0.12 and 0.15.
Image Quality
SR-LK Alignment
Ge1DR.1
Sii5k]
Ge1B811
Clando
ther evaluation,
Figure 6: User studies on image quality and alignment.
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Table Ablation study on varied step distillation strategy for OSEDiff and GenDR on RealSet80.
Table 3: Ablation study varied prompt generat- ing setting for GenDR.
SD2.1
Strategy
Metriest
Method
Prompi Prompt Extract
Time
#Params
#MACs
MUSIQ
LIQE
ClipiQA
MUSIQ
Q-Align
Embed
VSD
4.1298
0.7037
69.19
4,3057
Null
15ms
92ms
1263M
1637G
70.66
VAE4
CiDA
4.3184
0.7230
70.13
4.3858
DAPE
21ms
15ms
113ms
1775M
2638G
71.74
VSD
4.1248
0.6911
4.3732
Qwen2.5
3.09s
16ms
3.18s
8.3B
71.08
SiD
4.2528
0.7016
69.33
4.3912
Fixed
15ms
92ms
1263M
1637G
71.57
VAE16
CiD
4.4432
0.7150
70.61
4.4278
Fixed
77ms
933M
1623G
71.57
CIDA
4.5248
0.7424
71.57
4.4525
Table 4: Quantitative comparison between stable diffusions on 20k prompts from COCO 2014.
GenEvalt
Methods
FID..
Overall
Single Obj.
Two Obj.
Counting
Colors
Position
Color Attr.
SD1.5
0.43
0.97
0.38
0.38
0.76
0.04
0.06
13,45
SD2.1
0.50
0.98
0.51
0.44
0.85
0.07
0.17
13.45
SD2.1-VAE16
0.48 (-0.02)
0.98
0.53
0.35
0.79
0.08
0.12
27.89 (+14.44)
SDXL
0.55
0.98
0.74
0.39
0.85
0.15
0.23
18.4
IVECO
IVECO
IVECO
11/E0O
IVECO
rricans
workd
Dack
rricans
workt
Dack
rricans
workt
Dack
Fair Resullianns
go!
Fair Depulkacm
P
farr Dapulllans
letter
ery
Netterr
ery
Netterr
VSD-VAE4
CIDA VAE4
CIDA-VAE4
VSD-V
CIDA-VAE16
VAB4
VABI6
Figure 7: Visual comparison between VAE4 and VAE16, VSD and CiDA on RealSet80 and RealSR.
4.3 ABLATION STUDY
Effectiveness of CiD and CiDA. To examine the effectiveness of the proposed distillation approach, we use VSD (Wang et al., 2024d), SiD (Zhou et al., 2024b), CiD, and CiDA to distill SD2.1 (OSEDiff) and SD2. SD2.1-VAND V-V66 (GenDR). Tab. 2 shows the quantitative comparisons, where the proposed CiDA yields a 0.08 improvement on Q-Align for both OSEDiff and GenDR. Specific to each loss function, the CiD accounts for 0.05 Q-Align, and adversarial learning contributes the remaining 0.03. In Fig. 7, we provide a visual comparison for models trained by CiDA and VSD, where CiDA can provide vivid details and alleviate structural distortion.
Effectiveness of 16 channel VAE. Since GenDR is based on proposed SD2.1-VARS 1VV.16 we first evaluate the new SD2.1-VAE16 in Tab. 4 by calculating GenEval (Ghosh et al., 2023) and FID on COCO 2014 (Lin et al., 2014), where the performance of SD2. 1-2.11 slightly decreases compared 1, indicating that in the T2I task, 4-channel VAB4 is more preferable than 16-channel for SD2.1. However, the performance reverses in the SR task. As exhibited in Tab. 2, compared to the 4-channel VAE model, 1D2.1-VAEIL has similar or even higher objective scores. Moreover, the subjective comparison in Fig. 7 shows that VAF16 preserves more details and generates faithfully. Specifically, in the upper left case, the SD2. 1-VAE11--baaee model restores the license plate and inlet grille. The results demonstrate that applying 16-1166000 16-channel VAE matters for even small diffusion UNet in the SR task.
Effectiveness of simplified pipeline. For more efficient inference, we replace the text encoding modules and scheduler in GenDR with fixed embeddings. In Tab. 3, we compare the proposed GenDR pipeline with different prompt extractors (DAPE (Wu et al., 2024c), Qwen2.5VL-7B (Team, 2024)) and strategies, where our design is the most efficient and maintains a similar IQA score with content-related prompts. We provide more results prompt selection in Appendix A.4.1.

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5 LIMITATIONS
Despite our studies demonstrating that VAE-16 can alleviate detail fidelity degradation in the SR task, even for 0.9B UNet, further study VAE with larger latent channels is not included due to the validated effectiveness of 16-channel VAE and the high training expenses for entire SD models. Another limitation lies in the training cost of CiDA. While we use LoRA and deepspeed to optimize the training process, CiDA needs large GPU memory, which restricts its extension in DiT models.
6 CONCLUSION
This work presents GenDR for real-world SR by tailoring the T2I model in an SR-favoree manner, ie., using high-dimensional latent space to preserve and generate more details and one-step inference to improve efficiency. In detail, we develop an SD2. 1-VAE16 providing better trade-off between reconstruction quality and efficiency. Based on it, we introduce a consistent score identity distillation (CiD) that incorporates SR priors into T21-oriented step diffusion to promote consistency and stability. Moreover, we improve CiD with adversarial learning and REPA to achieve better realistic details. Overall, GenDR obtains remarkable quality--fficiency trade-off.
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A APPENDIX
A.1
PROOF FOR CID
The key point for CiD is to replace 2g fo- (ze, with optimal 2n the following identity transformation:
Ez (an) p(ppp)))lssl 1), 2n)) = Ez (
am)p(e)) (S (), 2n)]-
(12)
MSE loss between SR and HR latent Eq. (6) can be formulated as:
0 e (ll Zn 11
(13)
Apparently, the optimal solution for Eq. (13)
fo. (Zz, 1) Bg p [2n]-
(14)
Then, CiD is proved by replacing the optimal value for Zy:
) xx)p))))))))))))))) lif (Ze, t), Zgyl Ex.-p(z)) ((fo (z,, t), for (zz, t))] Ez ((())) ) (1 (Zc: (), (eanm((allz)) [2n])]
(15)
Ex, ppp ~p( (z, (Z), t), [n)]
Ez plan p(x.) K[ Ze. (), kn)]
A.2 ALOGRORITIM DETAILS
In Algorithms and 2, we summarize the training schemes for tively. Specifically, employ two-stage training strategy (Yin et al., 2024; Zhou et al., 2024b) for GenDR to accelerate training. In the first stage, we train GenDR as vanilla SR model with L1 loss and perceptual loss to align the output latent distribution with the target. This processing effectively reduce the overall training time and improve the robustness of the training. the second stage, train GenDR with the proposed CiDA framework to make the results more realistic.
and GenDR, respec-
A.3 EXPERIMENTAL DETAILS
A.3.1 DATASET PREPARATION
The diffusion-bassee SR aims to train generative model creating clear details, while the images from the existing open-sourced datasets are from uniform in terms of quality, blur, blackness, aesthetics. and compression artifacts. Thus, we conduct a dataset preselection to filter images with IQA and aesthetics metrics, where 19.2% of images from the original datasets are preserved.
A.3.2 USER AND MILM PREFERENCE STUDIES
User study. Following Wu et al. (2024b); Sauer et al. (2024a), we conducted a user study to evaluate the human feedback of GenDR. We randomly sampled 10 real-world LQ images from RealSet80. During testing, LQ images and two SR images restored by GenDR and SinSR/InsSRDrreanClee were presented to volunteers to select the best-matched image in quality and fidelity attributes, respectively. In Fig. 8, we show the LQ images used for the user study.
B
433
2 436
$1425105
MLLM preference. Since the user study com- pares only 10 images and 4 methods, we addi- tionally develop an arena using DepictQA to make pairwise comparisons between the SR re-
Figure 8: The LQ images used for the user study. sults of various models.
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Algorithm 1: Training Scheme of SDD.1-VAEI6
Input: Pretrained SD2.1 network E,s. SD2.I-VAE16 ea, encoder of 16 channel VAE &vAr, pretrained DINOv2 encoder EDIOO, Lmin 0, mmx 999, 0.1
Initialization <- repeat
Sample image and prompt from batch B; Calculate latent with (vAe(X) and extract representation he (X.),
Sample E Siminn...,,aa and e VO, I), calculate forward diffusion z, = ā.Zo + Beet, and let
(eg. h2) colzeit,c)
Update / with A = VoCo, where
he h(h))
Le ||co c1
[bs] [((h)))]
until achicving convergence or the training budget exhausted Output: Go
Algorithm 2: Training Scheme of GenDR
Input: Pretrained diffusion network fus generator Go. real score network fo. fake score network fe, discriminative head h,, encoder ε, finit 199, tini 20, 1max 979, guidance scales K 7.5, A1,2.3 10, 0.01, 0. 1.2
Initialization < μ., initialize LoRA 1h o. dishead
b Updating GenDR with vanilla SR loss
repeat
Sample latent pairs (Za, 2)) and prompt from batch B; Sample t E (fmin...... fmax) and ~ ~N(0
and let (zg; h,) Go(zi,t,))
Update Go with @ r7 VoLo where
1|2
Zi. 12 C/ Cieerp))
(Zg, Zh.)
until achieving convergence or processing 20M images
& Updating GenDR with CiDA
repeat
Sample latent pairs (za, 2z) and prompt from batch B; Sample i (lmsn).... (mxx) } and I).
Go(zi; c): Stop gradient for zg. di and ug.: (esg[22) + Beee. Zh.1 = 0. + Ayet
and let (2g,
Update with h = Lh. where
H/ w
LA H'W
E (In(1 (nl + Inhe (eso
Update with @ - V and 1p with v where
zwl12. L. = (1 sg(2,]11
Sample E (lmiin...... [max) and E ~ (0, I): Stop gradient for d. v. h, get w(()= = C/sgillan Sonn (2: 1,e,),l ell. extract representation hgg E(xa). and let 2, a.zy + B.er
Update Go with 0 VoLo. where
clold)) =Xiw(1) ((155)) (z::), ((1- (2;;tt) 2n. + (21s4)) W
(nh(f (R,)) hs h(h)))
Da
HWW
|b [[(b))]]
until processing 20M image pairs the training hudget is exhausted Output: Go
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A.4 ADDITIONAL RESULTS FOR GENDR
A.4.1 ABLATION STUDIES
Comparison between varied prompt extraction strategies. compared several prompt extraction strategies (Null, Fixed, DAPE (Wu et et. 2024c), Qwen2.5-7B (Team, 2024)) in Tab. 3 and found their objective metrics are quite similar. However, in visual comparison, prompts with more semantical descriptions bring more details. Fig. 9 illustrates two examples in which enriching prompts bring improved realism.
Null (77ms)
Fixed (77ms)
DAPE (113ms)
Qwen2.5 (3.18s)
"brunch, close up, eye, frog, green, limb, perch, red, sit, tree frog"
"The image shows vibrant rod-eyed tree frog
Null
Fixed prompt
"The image depicts a single, delicate pink flower in full bloom....
"heautiful, bloom, flower, lonus, pink, pond, water lily"
Null
Fixed prompt
Figure 9: Comparison between images restored by GenDR with different prompt extraction methods.
To evaluate the effectiveness of our strategy for input demanding high semantic control, we test various prompt extraction strategies in the more complex face restoration task. In Fig. 10, visualize the comparison between fixed prompts with Qwen2.5-VL. generated prompt with extremely- degraded face input. Generally, GenDR with fixed prompt achieve comparable even better reconstruction quality than Qwen2.5VL's prompt, demonstrating the effectiveness and generality of the proposed strategy.
1.Q
Fixedl (77ms)
(77ms)
Qwen2.5 184)
"The image shows woman with short dark hair, focing
Le
Fixed prompt
Fixed prompt (face)
"The shows man with short dark hair, facing
LQ
Fixed prompt
Fixed prompt (face)
Figure 10: Comparison between images restored by GenDR with different prompt extraction methods in face restoration task. Fixed prompt (face) denotes prompts with face-related descriptions.
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Comparison between varied VAEs. In Tab. 5 and Fig. 12, examine the existing VAEs with 4/16 channels. Generally, the B6-channel-basee VAEs obtain better reconstruction capability in terms of both QA metrics and subject evaluation. Our VAE is the most lightweight VAE16 that advances existing VAE4 by a large margin and VAE16 from SD3. In Fig. 1, we further visualize their latent distributions via t-SNE. The VAE4 is uniform than VAEs and reveals clustering. This phenomenon confirms the inferior representation capability of VAE4 than VAE16 since they encode the same 50k images from ImageNet validation. Overall, these results demonstrate findings that for T21 tasks, VAE16 brings more difficulty in generation but better reconstruction, while VAE4 is more suitable for small models but suffers from detail failure.
VAE4 (SD2.1)
VAE16 (Ostris)
VAE16 (SD3.5)
VAE16 (FLUX)
Figure 11: Visualization of the latent distribution of varied VABs.
Comparison of varied fixed prompt. We compare several fixed prompts collected from existing work (Yue et al., 2024a; Wu et al., 2024c; Yu et al., 2024a) to find a more effective prompt for local inference embedding. The positive prompts are follows:
Pi (InvSR (Yue et 2024a)): "Cinematic, high-contrast, peoto-realistic, 8k, ultra HD, meticulous detailing, hyper sharpness, perfect without deformations"
P2 (SUPIR (Yu et al., 2024a)): "Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed, realisse, maximum detail, 32k, Color Grading. ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations"
P3 (GenDR): "clean, high-resolution, best quality, smooth plain area, high-fidelity, clear edge, realistic detailed, 8k, perfect without deformations"
P4 (GenDR): "realism photo, best quality, realistic detailed, clean, high-resolution, best quality, smooth plain area, high-fidelity, clear edge, clean details without messy patterns, high-resolution, no noise, high-fidelit,, clear and sharp edge, 4K, 8k, perfect without deformations, great details, 8K, photo taken in the style of Canon EOS -style raw"
The negative prompts are as follows:
N1 (InvSR (Yue et al., 2024a))): "Low quality, blurring, ipeg artifacts, deformed, over-smooth, cartoon, noisy, painting. drawing, sketch, oil painting"
N2 (SUPIR (Yu et al., 2024a)): "puinting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, CG Style, 3D render, unreal engine, blurring, dirty, messy, worst quality, low quality. frames, watermark, signature, jpeg artifacts, deformed, low-res, over-smooth"
Visual comparison between var- VAEs. The best and running-up methods are high- ious VAEs. Detail distortions arise in faces and texts for VAE4. (Zoom-in for best view.)
Table 5: Quantitative comparison between various Figure 12:
lighted in bold and underlined.
VAE from
SD2.1
SDXL
SD3.5
FLUX
Ours
VAE Channel
16
#Params
0.9B
2.6B
12B
0.9B
Paaram (VAE)
83.65M
83.65M
83.65M
83.65M
57.27M
PSNR (dB)
27.20
27.69
30.67
32.30
30.94
SSIM
0.7857
0.8021
0.8952
0.9214
0. 1969
104TA 941
HEVEM SDXL
290 145
LPIPS
0.1503
0.1466
0.0833
0.0702
0.0752
derived from Ostris's 's vac-kl-f8-d16
HQ
SD2.1
Ours
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Table Quantitative comparison (average ClipIQA and MUSIQ) on RealLR between varied fixed (Positive/Negative) prompts with CFG-1.2.
Null
Pl
P2
P3
P4
Average
Null
0.7243/70.62
0.7234/7161
0.7118/71.35
0.7173/71.77
0.7195/71677
0.7193/71440
NI
0.7240/70.58
0.7274/7167
0.7145/71.43
0.7225/71.83
0.7243/71.73
0.7225/71445
N2
0.7232/70.45
0.7252/71.30
0.7128/71.26
0.7200/71.70
0.7223/7166
0.7207/71.26
N3
0.7277/70.82
0.7274/71.77
0.7140/71.54
0.7224/71.93
0.7242/71.85
0.7231/71.58
Average
0.7248/70.62
0.7259/71.59
0.7133/7140
0.7206/71.81
0.7226/71.71
0.7214/71.42
Table 7: Quantitative comparison (average PSNR, SSIM, LPIPS, ClipIQA, MUSIQ, LIQE) on RealSR with varied CFG factors.
CFG
PSNR
SSIM
LPIPS
ClipIQA
MUSIQ
LIQE
1.0
22.78
0.7009
0.2836
0.7012
68.13
4. 4.1690
1.1
22.67
0.6978
0.2872
0.7023
68.27
4.1909
1.2
22.55
0.6946
0.2908
0.7027
68.38
4.2076
1.3
22.43
0.6912
0.2945
0.7023
68.43
4.2172
22.31
0.6877
0.2983
0.7021
68.46
4.2225
8v58
1.5
22.31
0.6801
0,2983
0.7021
68.46
4.2227
1.6
22.31
0.6801
0,3060
0.7007
68.44
4.2147
1.7
21.92
0.6761
0.3099
0.6989
68.39
4.2004
Figure 13: DepictQA preference.
Table 8: Perceptual ablation study step distillation strategy and loss terms. We omit perceptual and MSE term of 3-7 row for simiplification.
RealSR
RealSer80
Loss
PSNR
LPIPS
ClipIQA
MUSIQ
ClipIQA
MUSIQ
OSEDiR
24.57
0.3036
0.6829
67.30
0.7037
69.19
L Limee (ipe)
26.72
0.2494
0,5235
58.19
0,5806
60.50
(ladv)
26.40
0.2731
0.6187
62.58
0.6544
65.10
Lived)
24.79
0,2544
0.6496
65.01
0.6911
68.82
Clsid)
24.88
0,2649
0.6511
65.39
0.7016
69.33
Cisid)
24.65
0.2527
0.6532
66.85
0,7150
70.61
Cicid) + glady)
22.62
0.2899
0.7020
68.30
0.7416
71.49
glcid) + glado) ((reps)
23.18
0.2859
0.7014
68.36
0.7424
71.57
N3 (GenDR): "noise, blur, oil painting, dotted, compressed, painting, aliased edge, low quality, over-sharp. low-resolution, over-smooth, jpeg artifacts, low quality, normal quality, dirty, messy"
As shown in Tab. 6, the highest ClipIQA and MUSIQ are obtained with Null-N3 and P3-N3, respectively. We also examine the positive/negative prompts individually. In summary, P1 leads to better ClipIQA while P3 induces advanced MUSIQ. For negative prompts, the proposed N3 outperforms other settings in both attributes of ClipIQA and MUSIQ.
Comparison of varied CFG. Referring to InvSR, we implement CFG with a relatively low coefficient, i.e., 1.0 2.0. In Tab. we progressively add the CFD factor 1.7. With increasing CFG, the FR-IQA metrics continue to drop while the NR-IQA first increases and then drops.
Extensive comparison of varied distillation strategies and loss. We include more perceptual quality comparisons for the proposed CiDA. As illustrated in Tab. 8, we compare GenDR training under various distillation loss with OSEDiff (Wu et al., 2024b) and ablate each loss term separately. Generally, the proposed CiD achieves better fidelity than VSD (Wang et al., 2024d) and SiD. Com- pared to OSEDiff, GenDR with CiD maintains similar NF-IQA scores but higher LPIPS score, illustrating the effectiveness of CiD in consistency preserving. We also conduct ablation study to comprehensively figure out the effectivenss of each loss term. Generally, adding adversarial loss will introduce perceptual performance (LPIPS) drop but also bring huge no-reference improvement.
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A.4.2 TRAINING AND INFERENCE PERFORMANCE
Training cost. To comprehensively evaluate the practical cost for GenDR training with CiD/CiDA, we provide the theoretical/practical training cost (memory/time) in the same setting in Tab. 9.
Memory cost: In vanilla VSD, the fake score network fully trained, which requires updating two UNets (one-step generator and fake score network) and saving three UNets (with an additional real score network). CiD needs to update three UNets (plus extra discriminator headers for CiDA). We address this by sharing the base model and using switchable LoRA-this preserves only two UNets (one-step generator and shared score network) and optimizes fewer parameters, making CiD/CiDA feasible.
Time cost: In terms of UNet forward passes, VSDISiD executes 1 pass for generation, 2 passes for score prediction, and pass for fake score updating. CiD adds an extra pass for real score network optimization, while CiDA further adds more pass for GAN loss computation.
We evaluate real training performance as follows. By using ZeRO, LoRA, and sharing strategy, CiDICiDA requires GPU memory comparable to VSD and SiD, but slightly slows training steps by acceptable 34% and 42%.
Practical cost:
Table 9: Training cost comparison of varied distillation strategies. "f" represents using LoRA.
Method
Baseline
VSD
SiD
CiD
CIDT
CIDA
Forward times
4
4
6
Additional trainable parameters (M)
865
128
135
#Time per iteration (s, bs=8)
1.14
3.13
3.27
4.42
4.38
4.65
#Momory (GB, bs=8)
40.12
52.81
53.38
65.57
50.51
53.44
Inference cost. We transferred the PyTorch weight of GenDR to TensorKT fp16/int8/fp8 and deployed it A10 and L20 for practical real-world deployment of server-side enhancement. In the following Tab. 10, we exhibit the actual performance of the overall pipeline at 1024x 1024 and 1440x 1440. Specifically, for 1080p input images (1920x 1080 has similar pixels as 1440x 1440), GenDR can accomplish real-time restoration with throughputs of 2 1.5 images/s on A10 and 3x2.6 images/s on L20 (3x means one L20-48G can deploy 3 GenDR tasks).
Table 10: Inference cost comparison of GenDR deploying on varied GPUs.
Device-dtype
A100-fp16
A10-fp16
A10-int8
L20-fp16
L20-int8
L20-fp8
Runtime @ 1024 x 1024, #/7 (ms)
262
305
269
179
157
160
Runtime@ @ 1440x 1440, s:1080p (ms)
601
721
627
424
369
381
Memory @ 1920x 1440 (GB)
13.27
12.53
12.53
12.60
12.60
12.60
A.4.3 MLLM ARENA
In Fig. 13, we utilize DepictQA to make pairwise comparisons and calculate the average selected rate (in red). The GenDR receives about 78% votes, indicating its better quality than other methods.
A.4.4 VISUAL RESULTS
In Fig. 16, we provide more visual comparisons RealSet80 (Yue al., 2024b) between the proposed GenDR and other diffusion models. Generally, the GenDR can reasonably preserve the original information and generate more faithful details. In Fig. 17, compare our method with OSEDiff and RealESRGAN on the KwaiSR dataset for UGC (user-generated content) SR Li et al. (2025). The proposed GenDR provides faithful reconstruction and the highest fine-grained detail restoration.
A.5 PRIMARY RESULTS FOR GENDR DISTILLED FROM SD3.5
To demonstrate the generality and expansion of GenDR, we train an SDXL-based GenDR and employ SD3.5-large distillation teacher. Specifically, we change the 4-channel latent space and
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diffusion scheduler of pruned SDXL (1.3B) 16-channel VAE space of SD3.5 and flow matching
scheduler, and then use SD3.5 as fake/real prediction network CiD to conduct SR distillation for 3DXL-VAE16 The process and results of this stage summarized in Fig. 14: SDXL-VAE16 adopts SD3's 16-channel VAE, reduces parameters by half compared to the original SDXL, while achieving restoration quality comparable to the baseline.
"A smiling humanoid red panda hold poster says GenDR
"A shiba iru wearing a beret
"A snow mountain"
"A potrait of old man"
GENDR
Figure 14: Samples produced by SDXL (Rombach al., 2022) (Above, 2.6B Diffusion Model with 4-channel VAE) and SDXL-VAE16 (Bottom, VAE16, 1.3B Flow Model with 16-channel VAE).
Through using SDXL VAE16 generator Go and SD3.5-large initial fy and far flow- matching adapted Eq. (9) to train primary -based GenDR, termed GenDR-1.33 We provide the primary results of the 10k iteration training in Fig. 15.
LQ
GenDR
HYPIR-balance
GenDR-1.31
GenDR-1.3B 28 step
SD3..-large 28 step
Figure 15: Samples produced by GenDR, HYPIR (Lin et al., 2025), SDXL-based GenDR, and SD3.5-large with ControlNeXt (Peng al., 2024).
A.6 ADDITIONAL RESULTS FOR SD2.1-VAE16
Table 11: Comparison of T2I methods
A. QUANTITATIVE REUSLTS
To further verify the usability of
we pro- vide detailed results GenEval (Ghosh et al., 2023) and additional CLIP results in Tab. 4. Generally, the proposed SD2.1-VAE16 slightly lagged behind SD2.1 (similar CLIP but lower GenEval), which is aligned with pectation since increasing the latent space dimension will introduce generation difficulty (Esser al., 2024).
Model
#Params
GenEvalt
CLIPS
PixArt-a
0.6B
0.48
0.316
SD1.5
0.9B
0.43
0.322
SD2.1
0.9B
0.50
0.30
SD-Turbo
0.9B
SDXL
2.6B
0.55
0.343
SD2.
1-VAE16
0.9B
0.48
0.323
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A.6.2 QUALITATIVE RESULTS
In Fig. 18, show the images generated by the proposed SD2.I-VAE16 with the following prompts:
An astronaut riding horse in the forest.
A girl examining an ammonite fossil.
A chimpanzee sitting on a wooden bench.
penguin standing on sidewalk.
goat wearing headphones.
Dreamy puppy surrounded by floating bubbles.
An elephant walking on the Great Wall.
An oil painting of two rabbits in the style of American Gothic, wearing the same clothes as in the original.
A woman wearing red scarf.
Motion.
A squirrel driving toy car.
A giant gorilla at the top of the Empire State Building.
A bowl with rice, broccoli and a purple relish.
Stars, water, brilliantly, gorgeous large scale scene, little girl, in the style of dreamy realism.
A cat reading newspaper.
A woman wearing cowboy hat face to face with horse
A watermelon chair.
A photo of llama wearing sunglasses standing on the deck of a spaceship with the Earth in the background.
Pirate ship trapped in cosmic maelstrom nebula, rendered in cosmic beach whirlpool engine, volumetric lighting, spectacular, ambient lights, light pollution, cinematic atmosphere, art nouveau style, illustration art artwork by SenseiJaye, intricate detail.
B THE USE OF LARGE LANGUAGE MODELS (LLMS)
We use Large Language Models in this research project to assist with various aspects of the writing and research process, including text polishing and refinement, and grammar enhancement.
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Iniage 50
Real-ESRGAN
StableSR-Nh
DifiBIR-50
SecSR-50
DreanClea-50
LQ
Real-HATGAN
SinSR-1
OSEDiIT-1
InvSR-1
GenDR-1
Image bears
Real-ESRGAN
StableSR-50
DifiBIR-50
SccSR-50
DreamClear-50
LQ
Reai-HATGANN
SinSR-1
OSEDaT--
InvSR-1
GeaDR-1
Image 32
Real ESRGAN
StableSR
DirBIR 50
SecSR
DreamClear 50
L
Reui-HATGAN
SinSR-1
OSEDifT-
InvSR-1
GenDR-i I
Image 0014
Real-ESRGAN
StableSR-50
DifiBIR-50
SecSR-50
DreamClear-50
LQ
Reai-HATGAN
SinSR-1
OSEDifF-
InvSR-1
GenDR-1
Figure 16: Visual comparison of GenDR with other methods for x4 task. (Zoom-in for best view.)
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LQ
RealESRGAN
OSEDiff
GenDR
Figure 17: Visual comparison of GenDR with other methods for UGCSR (Zoom in for best view.)
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Figure 18: px and 5122px samples produced by SD2.1-VAEL
24


Reviews:
[{"review_id": "h4OKnl2PsI", "reviewer": "Reviewer_8VTQ", "summary": "The paper introduces GenDR, a framework designed to convert a pre-trained text-to-image diffusion model (such as Stable Diffusion) into a compact model for real-world image super-resolution. The approach uses a 16-channel VAE to retain more detailed image representations and applies CiDA  to align the training objective with the super-resolution task. GenDR produces results in a single inference step and shows improved performance over several baseline methods in terms of perceptual quality and runtime efficiency on benchmark datasets.", "questions": "I suggest the authors include a brief comparison with TVTSR [1] (ICCV 2025) in the main text. While the motivation is aligned, the two methods adopt different and complementary strategies: the proposed approach keeps the spatial compression ratio fixed but increases the channel dimension by 4×, whereas TVTSR maintains the channel size and compresses the spatial resolution by 2×. A short discussion of these orthogonal design choices would help clarify the novelty and positioning of the proposed method.\n\n[1] https://arxiv.org/pdf/2507.20291", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper addresses a practical limitation of the commonly used f8c4 VAE in SR tasks by expanding it to 16 channels, which helps preserve more details. \n\n- The proposed CiDA framework is a well-engineered solution that enables stable training and efficient inference even in a higher-dimensional latent space.\n\n- The experiments are thorough, covering model size, inference speed, ablations on CiDA, prompt simplification, and performance after SR-specific fine-tuning. These results support both the effectiveness and practical relevance of the method.", "weaknesses": "- In Table 2, the paper only reports changes in no-reference metrics, which do reflect perceptual quality improvements, but it lacks reference-based metrics like LPIPS. Including trends in LPIPS (even if degraded) would provide a more complete picture, especially considering the known trade-offs between no-reference and reference-based measures. Visualization comparisons under different loss functions would also strengthen the argument.\n\n- It remains unclear why the model, after being fine-tuned for SR on a 512-resolution UNet, can still generate coherent 1024-resolution T2I results without typical artifacts (e.g., extra limbs). Some clarification on how the SR tuning impacts generalization to higher-resolution T2I tasks would be helpful.", "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": "hENre89iZx", "reviewer": "Reviewer_Pt8i", "summary": "This paper proposes GenDR, a diffusion-based model for real-world image super-resolution (SR) that aims to address the long-standing trade-off between detail fidelity and inference efficiency. The authors identify that most text-to-image (T2I) diffusion models are suboptimal for SR tasks because (1) they use low-dimensional latent spaces (typically 4-channel VAEs), and (2) they require multi-step inference to synthesize images. To overcome this, the authors develop an SD2.1 model with a 16-channel VAE backbone and a consistent score identity distillation strategy.  Empirically, GenDR achieves competitive or superior performance over state-of-the-art (SOTA) models such as DiffBIR, OSEDiff, and DreamClear, with notable improvements in LIQE, MUSIQ, and Q-Align scores and a 77 ms runtime.", "questions": "I wonder about the effectiveness of RAPE regularization in SR task.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "I agree with the claim on the disadvantages of the 16-channel VAE, which probably limits the performance upper bound of diffusion-based SR methods. As illustrated in Table 2, such a modification indeed improves the SR results.", "weaknesses": "1. My main concern mainly focuses on the experimental performance. The authors claim that the proposed method is able to enhance the detail fidelity. The quantitative comparison results in Table 1 cannot support such a claim, in which GenDR does not show obvious improvements regarding the fidelity metric, such as PSNR, LPIPS. \n\n2. As for the visual results, I personally don't think GenDR is better than other SoTA methods, particularly in the first example in Fig. 1 and the second example in Fig. 5.\n\n3. As for the efficiency, GenDR shows slight advantages compared with InvSR and OSEDiff. I guess such improvement is due to the removal of the text encoder. However,  this trick is also suitable for other methods (e.g., InvSR) that do not rely on dynamic textual information.", "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": "QMuNK8QJhv", "reviewer": "Reviewer_5Lfg", "summary": "This paper presents GenDR, a method for efficient super-resolution (SR) that combines VAE-16 and small latent diffusion models (LDMs) using score distillation. The authors claim their approach enhances restoration quality while maintaining speed, achieving superior performance over existing methods. The paper’s main focus appears to be on improving the balance between fidelity and inference time in diffusion-based SR models.", "questions": "See above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "1. The combination of VAE-16 with latent diffusion models, coupled with score distillation, is an interesting approach for super-resolution.\n2. The experimental results demonstrate that GenDR outperforms existing models in terms of both quality and efficiency, with strong quantitative and qualitative performance across multiple benchmarks.\n3. The method is designed to be efficient, offering improved restoration speed without compromising visual fidelity, which is a significant advantage in real-world applications.", "weaknesses": "1. The paper struggles with clarity and coherence, especially in how it connects its contributions. While the abstract and introduction spend significant time discussing the advantages of using VAE-16 for SR tasks, the method section shifts focus entirely to the proposed CiDA loss. This abrupt shift makes it difficult to understand the relationship between the two contributions, as they don’t seem to be closely tied. \n2. While the paper introduces CiDA as a novel technique for distilling scores, its relevance to the task of detail restoration in SR is not sufficiently discussed. The paper does not clearly explain why this loss function is needed for SR or how it helps improve the restoration of fine details. The overall discussion of CiDA feels more aligned with general diffusion model training rather than specifically addressing the SR challenge.\n3. The methodology section is dense and difficult to follow. The authors jump between various components like VAE-16, CiD, and adversarial learning without fully explaining their relationships or how they work together to address the SR problem. Additionally, the notations used in the equations make the explanation harder to follow (e.g, $f(\\cdot)$ and $\\epsilon(\\cdot)$ seem to both refer to the scores).", "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": "T9M3SOzvyW", "reviewer": "Reviewer_kCw1", "summary": "This paper proposes GenDR for better tradeoff between detail enhancement and inference efficiency. By utilizing a pre-trained 16-channel VAE, it expands the restoration capacity of the proposed SR model with a 0.9B UNet backbone. To reduce computational overhead, it introduces consistent score identity distillation technique to effectively train a one-step model while preserving its ability to generate vivid details. Extensive experiments demonstrate its ability on diverse scenarios.", "questions": "* The proposed method utilizes a generator and two trainable score networks to perform the score distillation process. Thus, quantitative comparison of memory usage should be provided.\n* The authors should consider providing more image results as supplemental material.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* By utilizing a pre-trained 16-channel VAE, GenDR effectively expands the capacity of the proposed SR model.\n* It proposes a SR-tailored step distillation technique CiDA that restore vivid details and stabilize training.\n* Extensive experiments demonstrate its effectiveness on various benchmark.", "weaknesses": "* Since one of the main contributions of this paper is expanding SR capacity by a large channel VAE, more analysis about this component should be added, including the choice of the latent channel, training difficulty and reconstruction ability. \n* Results of multi-step GenDR model are absent. Although authors provide comparison between various distillation methods, comparing with the teacher model can further validate the performance of the proposed CiDA.\n* While replacing the text encoder by a fixed positive embedding reduces inference overhead with slight performance degradation, results on Tab. 7 shows that slightly increasing CFG scale leads to worse performance. I am concerned that the weak textual guidance might come from using a fixed embedding. The authors should consider testing on input images that require high semantic control, such as portraits or text-containing inputs.", "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)