Eliminating VAE for Fast and High-Resolution Generative Detail Restoration
Authors: Yan Wang
Shijie Zhao
Junlin Li
Li zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion
Super-Resolution
Adversarial distillation
Model Compression
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Diffusion models have attained remarkable breakthroughs in the real-world super-resolution (SR) task, albeit at slow inference and high demand on devices. To accelerate inference, recent works like GenDR adopt step distillation to minimize the step number to one. However, the memory boundary still restricts the maximum processing size, necessitating tile-by-tile restoration of high-resolution images. Through profiling the pipeline, we pinpoint that the variational auto-encoder (VAE) is the bottleneck of latency and memory. To completely solve the problem, we leverage pixel-(un)shuffle operations to eliminate the VAE, reversing the latent-based GenDR to pixel-space GenDR-Pix. However, upscale with 
$\times$8 pixelshuffle may induce artifacts of repeated patterns. To alleviate the distortion, we propose a multi-stage adversarial distillation to progressively remove the encoder and decoder. Specifically, we utilize generative features from the previous stage models to guide adversarial discrimination. Moreover, we propose random padding to augment generative features and avoid discriminator collapse. We also introduce a masked Fourier space loss to penalize the outliers of amplitude. To improve inference performance, we empirically integrate a padding-based self-ensemble with classifier-free guidance to improve inference scaling. Experimental results show that GenDR-Pix performs 2.8$\times$  acceleration and 60% memory-saving compared to GenDR with negligible visual degradation, surpassing other one-step diffusion SR. Against all odds, GenDR-Pix can restore 4K image in only 1 second and 6 GB.

Published as a conference paper at ICLR 2026
ELIMINATING VAE FOR FAST AND HIGH-RESOLUTION GENERATIVE DETAIL RESTORATION
Yan Wang, Shijie Zhao", Junlin Li, Li Zhang ByteDance (wangyan.my my, zhaoshi jie. 0526) @bytedance.com
ABSTRACT
Diffusion models have attained remarkable breakthroughs in the real-world super- resolution (SR) task, albeit at slow inference and high demand on devices. To accelerate inference, recent works like GenDR adopt step distillation to mini- mize the step number to one. However, the memory boundary still restricts the maximum processing size, necessitating tile-by-tile restoration of high-resolution images. Through profiling the pipeline, we pinpoint that the variational auto- encoder (VAE) is the bottleneck of latency and memory. To completely solve the problem, we leverage pixcl-(un)shuffe operations to eliminate the VAE, reversing the latent-based GenDR to pixel-spaae exxe-space GenDR-Pit --xx. However, upscale with x8 pixelshuffle may induce artifacts of repeated patterns. To alleviate the distortion, we propose a multi-stage adversarial distillation to progressively remove the encoder and decoder. Specifically, utilize generative features from the previous stage models to guide ad versarial discrimination. Moreover, we propose random padding augment generative features and avoid discriminator collapse. We also introduce a masked Fourier space loss to penalize the outliers of amplitude. To improve in- ference performance, we empirically integrate a padding- based self-ensemble with classifier free guidance to improve inference scaling. Experimental results show that GenDR-Pix performs 2.8x acceleration and 60% memory-ssving compared to GenDR with negligible visual degradation, surpassing other one-step diffusion SR. Against all odds, GenDR -ix can restore 4K image in only second and GB.
INTRODUCTION
Super-resolution (SR) is widely exploited to reconstruct missing high-quality (HQ) details from the degraded low-quality (LQ) image for better visual experiences. Existing studies (Dong et al., 2016; Lim al., 2017: Wang al., 2022a: Liang al., 2021: Wang et al., 2024c) have gained comprehen- sive understanding of synthesized degradation, e,g., bicubic downscaling. To develop generality and visual quality under real-world scenarios, prevalent researches (Ledig al., 2017: Zhang et al., 2021; Wang et al., 2024b) exploit generative models to recover structural information and replenish details. Recently, diffusion models have become new paradigm for text-to-image (Rombach et al., 2022b; Esser et al., 2024) and image-to-image tasks (Brooks et al., 2023; Wang et al., 2024b), bringing new trend for the SR task. Nevertheless, diffusion- based SR methods struggle with slow inference and intensive memory consumption, restricting their usage in real-world scenes.
To accelerate inference of diffusion SR, recent work (Xie et al., 2024; Wu et al., 2024b; Zhang al., 2024; Yue al., 2024a) has discovered one-step diffusion via step distillation minimize the overhead of diffusion inversion processing. Despite achieving the theoretically lowest complexity, existing one-step diffusion SRs easily exceed the memory upper bound for high-resolution input, requiring to crop into tiles, which executed serial and results in an unavoidable increase in elapsed time. Thus, redueing both runtime and memory footprint is of equal importance for practical employment. More recent attempts (Hu et al., 2024; Chen et al., 2025a) explore structural pruning or distillation simplify or remove complex modules. They have put lot of effort into simplifying the variational auto-encodes (VAE) since it costs more memory and even longer time in a few- step diffusion inference. However, these trials still execute the reconstruction from latent to pixel,
"Corresponding Author.

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Published as a conference paper at ICLR 2026
simplifying VAE and intensifying the dilemma between generation and reconstruction, particularly in the SR task. For instance, climinating the attention of the decoder carries a harmful impact on high- frequency details, leading to texture loss and sharpness decrease. Hence, the core problem of further slimming diffusion-based SR is to replace the VAE with simpler iidelit-guarrnnted operations.
We revisit LDM (Rombach et al., 2022a) employing latent space rather than pixel space (Ho et al., 2020), ie.., to reduce training and inference consumption by representing images with smaller features. However, there is similar operation in the pixel space, i.e. pixel-shuflle. Existing SR models (Shi et al., 2016; Wang et al., 2022b) use pixel-shuffle operation to upsample in the last stage, which allows most computations be performed under the smaller shape. Inspired by Chen al. (2025a:b) and the similar role between VAE and pixel(un)shuffle we unveil new path to lighten the diffusion SR model, which reverses the latent-based diffusion SR to pixel-based by replacing VAE with pixel-unshuffle and pixel-shuffle operation.
Despite its intuitiveness, eliminating VAE faces several problems. Firstly, VAE conducts large- scale representation compression for input images. Nevertheless, the model with the same factor pixelshuffle hardly converges and will cause visual artifacts, such as checkerboard and repeated pattern artifacts. The pixel-space model can barely learn from latent-based generative priors without VAE. To overcome these drawbacks, this work introduces both training and inference innovations.
To eliminate the VAE without heavy performance drops, we introduce multi-stage adversarial distillation that gradually replaces the encoder and decoder with pixel-unshuffle and pixel-shuffle operations. In the first stage, we remove the encoder by utilizing latent matching and adversarial learning on features extracted by GenDR. In the second stage, we remove the decoder by using the stage-I model as the discriminator. Moreover, we propose random padding (RandPad) augmentation to avoid discriminator collapse and improve diversity by randomly padding SR and HQ images.
To alleviate artifacts arising from large-scale pixel(un)shuffle, we propose masked Fourier space loss to impose a penalty for anomalous spike amplitudes.
To improve inference performance, we propose padding-based classifier- free guidance (PadCFG), which empirically integrates the self-ensemble strategy and classifier-free guidance.
Through applying these strategies to remove the VAE of the recent late-of-thhearr GenDR (Wang et al., 2025), we obtain an end-to-end model, named GenDR-Pix, Pix, which directly restores high-quality images in shuffled pixel space. Compared to the GenDR prototype, GenDR-Pix achieves about 2.8 acceleration and saves over 60% GPU memory.
2
RELATED WORK
2.1 GENERATIVE IMAGE SUPER- RESOLUTION
Since pioneer works (Lim et al., 2017: Zhang et al., 2018b; Wang et al., 2024c) have exhaustively explored the potential of deep-learning-bant based methods the classic SR task with synthesised LQ, prevailing research (Ledig et al., 2017: Cai et al., 2019; Zhang al., 2021) concentrates on wild images under real-world degradations. To produce rorl-world LQ-HQ image pairs, Real-ESRGAN (Wang et al., 2021) introduced practicable datapipe that mixed multiple degrada- tions and utilized adversarial learning to boost the model to recover images with more real details. APISR (Wang al., 2024a) further expanded the degradation types with more image and video compressions. Recently, diffusion models (Rombach et al., 2022a;b; Esser et al., 2024) have surpassed the GAN in both text-to- text-to-image and image-to-in to-gge- tasks. Following the trend, diffusion- based SR models (Wang et al., 2024b; Yu et al., 2024; Ai et al., 2025) proposed using controllable modules, e.g., ControlNet Zhang al. (2023a), to guide the diffusion model regenerating HQ images according to L.Q condition. Then, (Wu et al., 2024b; Zhang et al., 2024; Yue et al., 2024a; Wang et al., 2025) distill the diffusion steps to improve throughout by examining the predicted latent with additional score networks (Wang et al., 2024e; Zhou et al., 2024) discriminators (Sauer et al., 2024).
2.2 DIFFUSION DISTILLATION
To accelerate the diffusion model, existing distillation approaches reduce steps (Poole et al., 2022; Wang al., 2024e; Zhou et al., 2024) and simplify the architecture (Kim al., 2024; Hu et al., 2024;
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3543
Paramer Memory
Size
UNet
Time (ms)
VAE
UNet
Mem (GB)
VAE
3.05
512-
66
46
2.0
2.5
720
66
2.8
C.73G3
S.7GG3
1024
73
191
2.7
3,5
1440a
187
419
3,0
5.1
2063
2048
534
1087
4.1
8.3
1503
2880
1710
3228
14.7
40962
6201
OOM
11.1
OOM
Functon
Evor
Encoier 555s
UNet 7.3ms
20.0%
Decoder 105ms
Table 1: Comparison of varied in- put sizes. The memory is the max- imum active memory after alloca- tion for UNet or VAE.
130
150
200
250
Figure 1: Profiling for GenDR with input size of 1024?p px.
Zhu et al., 2024). DreamFusion (Poole et al., 2022) introduced score distillation sampling (SDS), which leverages a pre-trained diffusion model to guide an arbitrary generator producing results satis- fying the pre-trained distribution. ProlificDrramer (Wang al., 2024e) and SiD (Zhou al., 2024) developed SDS with variational score distillation (VSD) and identity transformation, which further improve stability and robustness. In the SR task, OSEDiff (Wu al., 2024b) leveraged VSD and GenDR (Wang et al., 2025) developed CiD based on SiD to tailor a one-step SR model. For structural distillation, since disactivating parts of blocks or channels negligibly affects model performance, BK-SDM (Kim et al., 2024) and flux. 1-Itee-8B (Freepik, 2025) removed intermediate blocks of SD1.5 and FL.UX and used feature distilling loss to train lighter version models. Diffusion2GAN (Kang et al., 2024) conducted knowledge distillation from diffusion to GAN via adversarial learning and LaPSntI.PIPS. In the SR task, to lighten one-step diffusion, AdeSR (Chen et al., 2025a) performed knowledge distillation for Shtannel-prunce model.
3 MOTIVATION: REPLACING VAE WITH PUXEL
Efficiency bottleneck. For the one-step diffusion model like GenDR (Wang et al., 2025), the diffusion process conducted in the latent space, thereby necessitating a VAE encode or decode latents. As shown in Fig. 1 and Tab. 1, we profiled the whole process with torch. pro f lee and visualized allocated GPU memory and latencies of different functions. We found that for the one-step model, VAE is the bottleneck for latency and GPU memory, which limits throughput, especially for high-resolution input. In detail, for a 2880 px image (i.e., x20x720 LR input for x4 SR), VAE induces additional growths of 1.82 and 0.89x for memory and latency, respectively. In practice, due to VAE and UNet trained under fixed small resolution (e.g.. 512x512) and GPU memory boundary, existing diffuuson--brn based SR has to split the input image into multiple tiles, further stalling reasoning speed. To this end, modifying VAE is more profitable than modifying UNet.
Fidelity bottleneck. The existing VAEs conduct lossy compression that loses fighfrreqeency details for input images. Even for the 16-channel VAE, the reconstruction results are far from reliable (Esser al., 2024; Wang et al., 2025) (e.g., PSNR is below 35 dB). For the SR task that chases pixel-wise fidelity, the VAE is the bottleneck restricting the reconstruction.
Key and problem. Based the above findings, we pinpoint VAE the bottleneck for both efficiency and fidelity, but can we replace VAE with more efficient and loss-free functions? The answer is Yes! We substitute the encoder with pixel-unshuffle and decoder with pixel-shuflle to reverse the latent-space diffusion processing into pixel-space. Despite intuitionistic, removing VAE faces two tricky problems:
Pixel(un)shuffle with a large scale factor induces repeated pattern artifacts. Compared to Ad- cSR (Chen et al., 2025a) merely employing x2 pixel-unshuffle to replace the encoder, we remove both encoder and decoder with x8 (isuffe to chase the ultimate efficiency in terms of both latency and memory occupancy. However, using pixelshuffle for x8 upscaling challenging since one inappropriate value of weight/bias in tail layers results in artifacts for all repeated 8x8 patches.
Lack of a suitable discriminator for the pixelfuffled feature. Existing works (Chen et al., 2025a; Sauer al., 2024) utilize pretrained diffusion models (UNet/DiT) as discriminators to adversarially
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GenDR
70.56
GenDR
Encoddr
Decodar
GeDDR-ADC
Remove Encoder
70.44
Remove Decoder
0.4d
GFG
RandPad
MFFS
SDENDC
GDDDR-ADC
-rrembie
PadCFF
Larger
Finklled
65377
Add Bandomn Pad
0530
Add CFG
Enwemble
07:3
Larger Stale
62
Hk Module
GenDR-Pu Pix
28
MU.53
We remove the entire VAE for GenDR for higher efficiency. The bars model MUSIQ score on RealLQ250. The hatched bar means the modification has not been adopted. The dotted curve shows run- time with 2560 1440 output.
Figure 2:
Figure 3: We visualize the effect of our modifications step by step. The left-top corner shows the operation types corresponding to Fig. 2, and the left-down num- bers the operation order. The proposed strategies can effectively remove most artifacts.
distill the step/architccture by examining the generated latents. However, for pixel-unshuffled images, there exists proper large model to guide distillation, further increasing the difficulty of reverse latent to pixel space.
Relation to AdeSR. The AdeSR (Chen al., 2025a) has explored replacing the encoder with x2 pixel-unshuffle and established an end-to-end x4 SR model. However, there remain several limitations:
AdcSR utilizes fixed x2 pixel-unshuffle to encode images, which enforces the model generate more pixels. limiting its extension in other upscaling factors or low-level tasks. We utilize the x8 (unissffee substitute VAE for flexibility and practicality.
AdeSR only removes the encoder, while the decoder still costs memory and latency than UNet. Moreover, removing the decoder results in more performance drops. This work studies the path to eliminating both the encoder and decoder for ideal efficiency.
Compared AdcSR with complex pruning and distillation strategies, this work only removes VAE and can achieve a comparable or even higher acceleration ratio.
4 METHODOLOGY
To overcome the above-mentioned issues, we propose multi-stage adversarial distillation fun- damentally train a robust VAE-free model and a padding-based classifier-fre guidance for stable inference in pixel space. In practice, remove the VAE of GenDR to reverse the SR task to pixel space to propose GenDR-Pix. In Figs. 2 and 3, we present the roadmap from latent-basee GenDR to pixel-based GenDR-Pix, which eliminates the entire VAE with slight performance drops.
4.1 MULTI-STAGE ADVERSARIAL DISTILLATION
To remove VAE and avoid repeated pattern artifacts, propose multi-stage adversarial distillation schema. In Fig. 4, we exhibit an overview procedure for eliminating VAE. In Appendix A. we provide the detailed algorithm of the training schema.
Stage I: Remove encoder. We first replace the encoder with x8 pixel-shuffle layer and utilize UNet from GenDR the feature extractor of the discriminator. Given input image Xig. the teacher latents
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Teacher
GT
Teacher
GT
LG
Latent
LG
Distill
MFS
BB 110
GAN Loss
GAN Loss
Shuffeed UQ
Shufiled
Shuffled
Stage Remove Encoder
Stage Il: Remove Decoder
Figure 4: Illustration of training processes of GenDR-Pix. In Stage I, we replace the encoder with x8 pixel-unshuffle and utilize half of the teacher (gray) to generate discriminative features, and blue blocks are trainable. In Stage we further remove the decoder with pixel-shufie and utilize the Stage I model (bluc), and the purple blocks are trainable.
Zuaa HQ latents Zng. and discriminator D, the overall training target for generator 2um = (x(y)) is:
C. = Zie - Z1 11 + A coftplus(-D(z.j))
LD softplus--Dzz.. + softplus (D(zzu))
In this stage, we obtain GenDR-Ade that maps low-quality pixels directly to high-quality latents.
Stage II: Remove decoder. Based on GenDR-Adc, we further replace the decoder with pixel- shuffle layer. In practice, we find that using the Eq. (1) encounters severe performance drops due to appearance of repeated pattern artifacts and the inappropriate latent-based discriminator.
To investigate and alleviate artifacts, we visual- ize several typical examples in Fig. Specif- ically, observe that the patterns same repeated mode among all images with 8x8 patches. Moreover, we exhibit frequency- domain results where highlight points appear pe- riodically, aligning with the pixel-shuffle scale, Thus, we identify that the distortion is caused by upscale, similar to artifacts raised by 2x2 embedding in FluxSR (Li et al., 2025). As the frequency characteristic of the artifacts certain, we introduce a masked Fourier space (MFS) loss based on a band-rejection filter. In detail, given the SR/HQ image Ysu and Yiea we first calcu- late their amplitudes Fyy)uvl through FFT transformation. Then, we can compute the loss via the following formulation:
in the
Figure 5; Illustraiion of artifacts in reconstructed image and their magnitude spectrum. (Zoom in for best view.)
CF ||M ([F - IF fl ll1.
(2)
where M is the mask inistialized from zero and satisfies M(; i - 8 i s) 1 and Mi j + s, where E {0, [h/8].....h and j E {0, [w/8],... ......... w). 2s is band width.
As to the discriminator, we utilize the first-stage generator G1 to evaluate the generated shuffled image since Gi natively uses pixel-unshuffle to encode inputs. Despite Gi being qualified for representation, as shown in Fig. 3, the detail generation still suffers performance degradation since the discriminator focuses on the discrete distribution embedded by a fixed pixel-unshufft mode, which is susceptible to adversarial perturbations and easily induces model collapse. Inspired by E-LPIPS (Kettunen et al., 2019), we introduce random padding (RandPad) augmentation strategy to help the discriminator extract continuous representations for shuffled SR and HQ images. In detail, we randomly sample Ph. Pa E {0, 1..1. ,77 and execute padding for ysu and Ybq before sending them to discriminator:
randpad(y) pyd (y [pr. Ph-Pw, 8 -Pul).
where [ph, Ph: Pw. 8 Pvc] are padding element numbers for left, right, top, and bottom sides.

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Published as a conference paper at ICLR 2026
Following previous work in pixel space (Kang et al., 2023; 2024), add perceptual loss Lp (Ding et al.. 2020) to improve subjective fidelity. Given the input Xig. the teacher images Yieas the high- quality images Yhq. can optimize Ysu (2(xi)) by:
Lg Ie Ysullh + Å1 softplus(- (yau)) + X2hp + Xzha,
Lg softplus( 91 (randpad(ym)))) + softplus(S) ( (randpad(ysu)))
In this stage, reverse G2 in pixel space, which directly generates high-quality pixels.
4.2 PADDING CLASSIFIER- GUIDANCE
To enable classifier-free (CFG) guidance in pixel space as well as erasing artifacts, we propose padding-based classifier-free guidance (PadCFG) via incorporating self-ensemble and CFG strategies. Following Ho & Salimans (2022), we formulate the CFG prediction of GenDR-Pix G2 as:
y w x G2(X, Cpos) + (1-w) w) x G2(x, Cneg):
(5)
where w. denotes the guidance scale. Cpus and Caey are positive and negative condition, respectively. Since (2(x) is shuffled image, directly employing CFG in the pixel space will exacerbate artifacts, as shown in Fig. 3. A simple but effective solution is the self-ensemble strategy that combines restoration results from multiple augmented inputs, e,g., rotation and flipping. Hence, we implement self-ensembb by fusing results with various padding steps. Given the pairs padding setting {pr,i P. the final ensembles result y is calculated by:
Σ (2(ad(xx, [pn,c, 8 Ph,is Pon,,, 8 Pwr,i))) h Ph, Puo.i w + Por.):
(6)
While effective in reducing artifacts, self-ensemble increases the inference batches and induces perceptual performance drops. To solve the problem, we empirically integrate Eqs. (5) and (6) by:
x G2(pad( [4, 4, 4, 4)), Cpos) + (1 - w) x (pad [3, 5, 5)), Cneg).
To further improve visual quality in the inference phase, we try to enlarge the processing size and use extra post-fixing module consisting of several convolution layers. More details and results can be found in Appendix A.I.
EXPERIMENTS
5.1 EXPERIMENTAL. SETUP
Datasets.
Following Wang al. (2025), adopt LSDIR (Li et al., 2023), FFHQ (Karras et al., 2019), DiffusionDB (Wang et al., 2022c), and select high-quality data from Laion (Schuhmann et al., 2022) to construct training set. To generate HQ-LQ image pairs,
the Real-ESRGAN (Wang et al., 2021) and APISR (Wang et al., 2024a) degradation pipelines, We randomly crop 512x512 LQ/HQ images with dynamic scaling factor. During testing phase, we employ ImageNet-Tess (Yue et al., 2024b), RealSR (Cai et al., 2019), RealSer80 (Yue et al., 2024b), and RealL (Ai et al., 2025) to evaluate the effectiveness of the proposed GenDR- Pix under synthesised and real-world degradations.
Metrics. Following existing work (Wang al., 2025; Yue et al., 2024a), we leverage several full-reference and non-reference IQA metrics to evaluate restoration quality. including PSNR, SSIM (Wang et al., 2004), LPIPS (Zhang et al., 2018a), NIQE (Mittal al., 2012), LIQE (Zhang al., 2023b), CLIPIQA (Wang al., 2023), MUSIQ (Ke et al., 2021), Q-Align (Quality) (Wu et al., 2024a), and DeQA (You et al., 2025).
Implementation details. GenDR-Pix is based on GenDR (Wang et al., 2025). which employs SD2.1 UNet (Rombach et al., 2022b) and vac-kl-f8-di6' To climinate VAE, we reinitialized the 16-channel head and tail convolutions of GenDR with 192-channel ones in stages and II, respectively. For the discriminator, we used frozen GenDR and GenDR-Ade as the feature extractor module and extra learnalle MLP heads LADD (Sauer et al., 2024). During the training procedure, the default

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Published as a conference paper at ICLR 2026
dger
BSRGAN
SlableRR-50
DimBIR-S0
SecSR: 50
GenDR-1 1
LQ
ESRGAN
AdeSR-1
OSEDiN
InvSR.1
GenDR.Pix
0014
BSRGAN
StaleeRR500
DiABIR-
SccSR-50
GenDR-1
LQ
ESRGAN
AdeSR-1
OSEDiTE1
InvSR-1
GenDR-Pix
Figure 6: Visual comparison of GenDR-Pix with other methods for x4 task on RealSet80 dataset.
AdamW (Loshchilov & Hutter, 2018) optimizer and a constant learning rate 1e-5 under BF loat 6 precision. The loss hyperparameters λ1,2,3 are 0.05, 1, 0.1, respectively. The batch and patch sizes are 512 and 512x512. All models are trained on 8 NVIDIA A100 GPUs and the PyTorch (Paszke et al., 2019) framework with the help deepspeed ZeRO2 (Rajbhandari et al., 2020). During the inference phase, we set PadCFG 1.8 for better restoration quality.
5.2 COMPARISION WITH STATE-OF-THE-ARTS
To examine the restoration and inference performance of the proposed GenDR-Pix, we compare it with multiple models, including 1) GAN: BSRGAN (Zhang et al., 2021), Real-ESRGAN (Wang et al., 2021), and RealESRHAT (Chen et al., 2023); 2) one-step diffusion: SinSR (Wang al., 2024d), OSEDiff (Wu et al., 2024b), InvSR (Yue al., 2024a), AdeSR (Chen et al., 2025a), and GenDR (Wang al., 2025); 3) multi-step diffusion: StableSR (Wang et al., 2024b), DiffBIR (Luo et al., 2023), SeeSR (Wu et al., 2024c), and DreamClear (Ai et al., 2025).
Quantitative comparison. In Tabs. and 3, compare the proposed GenDR-Pix with existing SOTA methods in terms of both restoration quality and inference performance. Generally, GenDR is the fastest model among all methods, even surpassing GAN-based models. Compared to OSEDiff and SeeSR, GenDR-Pix gains acceleration of 3.2x and 198.7x, respectively. Moreover, GenDR-Pix stands as the runner-up for both FR-IQA and NR-IQA metrics. In detail, GenDR-Pix advances OSEDiff by 0.6dB (PSNR) and 0.04 (CLIPIQA) on ImageNet-
Compared to the original GenDR, GenDR-PP -Px improves the PSNR/SSIM by a large margin while slightly decreasing perceptual quality. On RealSet80 benchmark, GenDR- -ix exhibits competitive performance with OSEDiff and InvSR. In Appendix A.2.2, we extend more results on real-world image datasets, including 4K benchmark.
Qualitative comparison. In Fig. 6, we depict the visual comparison of GenDR against other approaches. GenDR-Pix obtains comparable results GenDR and surpasses other approaches. Specifically, our model faithfully restores the tiger hair with more delicacy. For image 00/4, GenDR- Pix removes the heavy compression artifact and maintains the semantical structure.
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Table Quantitative comparison (average Parameters, MACs, and IQA metrics) on ImageNet-Test. The sampling step number is marked in the format of "Method name-Steps" for diffusion-basee methods. The efficiency metrics are calculated with 512x512 input on NVIDIA A100 GPUs. The best results for all methods are highlighted in bold and underlined. while the best one-step diffusion methods are reported red and blue.
Methods #Params.
#MACs
Metrics
PSNRT
SSIMT
LPIPS
NIQEI
CLIPIOAT
MUSIQU
Q-Alignf
BSRGAN
16.70M
293G
27.05
0.7453
0.2437
4.5345
0.5703
67.72
3.6057
Real-ESRGAN
16.70M
293G
26.62
0.7523
0.2303
4.4909
0.5090
64.81
3,4230
Real-HATGAAN
20.77M
417G
27.15
0.7690
0.2044
4.7834
0,4594
63.43
3.3244
StableSR-50
1410M
79940G
0.7317
0.2327
4,9378
0.5768
64.54
3,4388
DiffBIR-50
1717M
24234G
25.45
0.6651
0.2876
4,9289
0.7486
73.04
4.3228
SecSR-50
2524M
65857G
25.73
0.7072
0.2467
4.3530
0.6981
72.25
4,2412
*DreamClear550
2212M
24.76
0.6672
0,2463
5.3787
0.7646
70.08
4.0919
SinSR-1
119M
2649
26,98
0.7308
0,2288
5.2506
0.6607
67.70
3.8090
OSEDiff-1
1775M
2265G
24.82
0,7017
0,2431
4.2786
0.6778
71.74
4,0674
AdeSR-1
456M
496G
25.07
0,6982
0.2432
4.1947
0.7055
72.21
4.1382
InvSR-1
1298M
1820G
23.81
0.6777
0,2547
4,3935
0.7114
72.38
3.9867
GenDR-1
933M
1637G
24.14
0.6878
0.2652
4.1336
0.7395
74.68
4,3612
GenDR-Pix 866M
344G/744G
25.49
0.7286
0.2252
4.1892
0.7168
72.85
4,2082
inference using A A00 GPU to run an image.
Table 3: Quantitative comparison (average Inference time and IQA metrics) on RealSet80.
Metrics CLIPIQAT
Methods
#Runtime
NIQEL
PI
LIQR
MUSIQT
Q-Alignt
DeQAt
BSRGAN
36ms
4,4428
4.0276
3.8449
0.6262
66.63
4.1207
3.8825
Real-ESRGAN
36ms
4.1517
3.8843
3,7392
0.6190
64.49
4.1696
3,8906
Real-HATGAN
116ms
4,4705
3.8843
3.4927
0.5502
63.21
4.1077
3.8444
StableSR-50
ms
3,3999
3.0314
3.8516
0.7399
67.58
4.0870
3.8787
DiftBIR-50
6213ms
5.1389
3.9544
4.0472
0.7404
68.72
4.3206
4.0668
SeeSR-50
6359ms
4.3749
3.7454
4.2797
0.7124
69.74
4.3056
4.0728
DreamCCear-500
6892ms
3.7257
3.4157
3,9628
0.7242
67.22
4.1206
3,9429
SinSR-1
120ms
5.6103
4,2697
3.5957
0.6634
63.79
4.0954
3,9713
OSEDiff-1
103ms
3.9763
3.6894
4.1298
0.7037
69.19
4.3057
4.0916
A
33ms
4.0005
3.5688
4,2313
0.7079
69.73
4.2298
4,0530
InvSR-1
115ms
4.0266
3.4524
4,2906
0.7271
69.79
4.3014
4,0045
GenDR-Pix
32ms
4.1010
3.6709
4.3078
0.7190
69.92
4.2891
4.0747
Image Quality
User study. We conducted user study evaluate our model's restoration quality. Users were asked to select the higher-quality image from two images, one restored by GenDR-Pit and the other restored by AdcSR, OSED- iff, or GenDR. As shown in Fig. 7, our model received comparable votes as the original GenDR and OSEDiff, and led AdeSR by quarter, demonstrating the effective- ness of the proposed distillation strategy to preserve the ability to produce high-quality images.
GinDR.Pix
Ad:SR
GenDR.Pix
OSEDIff
GexDR.Pix
GenDR
100
Preference (16
Figure 7: User studies image quality.
5.3 ABLATION STUDIES
Effeet of removing VAE. In Tab. 6, we compare the original GenDR (Wang et al., 2025), GenDR- Adc (Chen al., 2025a) (Removing Encoder), and GenDR-Pix (Removing VAE) in terms both quality and runtime performance. Compared to the GenDR baseline, the proposed GenDR-Pin maintains the restoration quality but saves approximately 65% time, 61 % GPU memory, and 55% computations. Moreover, if we allow for slight performance drop, the runtime for 4K image can be reduced to less than second by disabling CFG. The Fig. 9 shows the visual comparison in which GenDR-Pi -Px can maintain detail and sharpness as the baseline. We also provide extended performance comparison in Fig. 8, where GenDR-PP Pix remarkably improves the throughput and lowers the maximum memory employment. Moreover, GenDR is the only model supporting SR to SK, dispensing with the cropping strategy. The results demonstrate the effectiveness of removing VAE improve efficiency.
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Published as a conference paper at ICLR 2026
Table 6: Quantitative comparison between GenDR, GedDR-Ade, and GenDR-Pix on Rea1LR250. The inference performances are tested with 4K (3840x 2160) output on A100 GPU, except for #MACs tested with 512x512 output. "x" represents model without PadCFG strategy.
Model
VAB
#Runtime
#Memory
#MACs
#Params
MUSIQ
CLIPIQA
GenDR
4,92s
20.75GB
1637G
933M
70.96
0.6891
GenDR-Ade
Remove Encoder 2.69s (-45.3%)
17.75GB (+14.5%)
1097G (-33.0%)
894M
70.44
0.6714
GenDR-Pix
Remove VAE
1.75s (-64.5%)
8.01GB (-614%%)
744G (-54.6%)
866M
70.23
0.6843
GenDR-Piix
Remove VAE
0.87s (-82.3%)
5.03GB (-75.8%)
344G (-78.9%)
866M
68.64
0.6615
25
GenDR
GenDR-ADC
20
Re
Runtime (s)
10-
0
1080p
2K
4K
8K
VAE
Rm. Encoder
Rm. VAE
Figure 8: Performance evaluation (1080p 8K).
Figure 9:
Visual comparison of varied VAE set.
Method
Fomulation
#Runtime
TPIPS
MUSIQ
CLIPIQA
w/o CFG
0.87s
0.2391
58.23
0,4924
CFG
xxpp+tt - w) X
1.73s
0.2425
58.67
0,5005
CFG Ensemble
1 E P(paaPa) ) (1 [1) [(p)
3.54s
0.2273
56.31
0,4800
PadCFG
U0PP(4.4) 1
n(12))
1.75s
0,2575
58.13
0.5119
PadCFG
m(2.2)
1.75s
0,2474
58.85
0.5161
PadCFG
w P(4,)) + (1--) n(3,3)
1.758
0.2413
59.12
0.5066
Table Ablation study of PadCFG with varied pad settings. {p, n} represents results with positive and negative prompts and the subscripts (ph, Pw) are padding hyparameters for each side.
Effect of Multi-Stage Adversarial Distillation, Since training GenDR-Pis within one stage is un- available and AdcSR has discussed similar results of Stage I, we conduct ablation studies for Stage II. In detail, we validate the effectiveness discrim- inator, RandPad augmentation, and MFS loss. We first examine the discriminator in Tab. 4, in which the disablement of adversarial learning degrades the
Table 4: Ablation study discriminator.
Discriminator RandPad NIQE
MUSIQ
CLIPIQA
w/o Dis.
4.8343
60.95
0.4924
perceptual quality by 0.12 and 0.0533 in MUSIQ and CLIPIQA Compared to schema using GenDR-Adc for adversarial feature, directly us- ing GenDR will induce 2.20 (MUSIQ) and 0.0307 (CLIPIQA) drops. Employing RandPand improves CLIPIQA by 0.0191 and decreases NIQE by 0.17. Tab. 5 discusses the impact of MFC loss, where omitting frequency loss leads to huge performance drops among all IQA metrics.
GenDR
6.0660
61.07
0.5457
GenDR-Ade
X
4.8380
63.87
0,5764
GenDR-Ade
4.6648
63.89
0.5937
Table 5: Ablation study on MFC Loss.
Loss
NIQE
LIQE
MCSIQ
CLIPIQA
w/o Freq loss
4,8380
2.9055
63.87
0.5764
MFC loss
4.1121
3,2062
65.77
0.5700
Efffct of PadCFG.
To evaluate PadCFG, we compare several variants and different settings in Tab. 7,
disabling other optimizations for faimesss. The proposed Eq. (7) maintains latency and fidelity as vanilla CFG but advances 0.45 and 0.0061 MUSIQ and CLIPIQ, achieving better trade-offs between fidelity and perceptual quality. We also validate the guidance scales in Appendix A.2.1.
CONCLUSION
This work introduces guideline to remove the entire VAE for diffusion-based SR. In detail, replace the encoder and decoder with x8 pixel-unshuffle and pixel-shuffle, respectively. To alleviate the artifacts brought by pixel(un)shuffle, and PadCFG for inference. Based on GenDR and proposed strategies, we obtain GenDR-Pix which accelerates by 2.8 and saves 60% GPU memory with negligible quality degradation.
introduce multi-stage adversarial distillation for training

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Published as a conference paper at ICLR 2026
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A
APPENDIX
A.1 ALOGRORITHM DETAILS
Overall scheme. In Algorithm 1, we summarize the training schema of the proposed GenDR-Pix. We first remove the encoder and then remove the decoder via adversarial learning. The C is calculated according Eq. (2), and utilize DISTS (Ding et al., 2020) as Lp estimate the perceptual similarity between the reference teacher results and student results.
Algorithm 1: Training Scheme of GenDR-Pix
Hyper-patameters: d 0.05, d2 A 0.1, 499
B. Stage Remove Encoder
Input:
Pretrained GenDR network 9p. AddDR-Ade Go. discriminative head he
Initialization 0 <- initialize Ø
repeat
Sample HR and LR image pairs (Xi, x:) and prompt from batch B; calculate the latent and let
2 Go [x; t, e) and reference model generate z,- G,.(2:: c): then calculate adversarial features
d, = Gp (Zg 1, c) and di, = Ga(2n: t, c); sg[:] and spl]] are stop gradient and softplus operations
Update with @ = v/ Lig, where
L ap(- ho (sg(dd)) sp(hosgg(d,))
Update Gj with 0 = - CoC where
Le
||, 2 Ai hs (2,)]
until achieving convergence or processing 20M image Output: Go
b Stage II: Remove Decoder
Input: Pretrained GenDR network Gp. Add Go, GenDR-Pix Ge.dicrriminative head he Initialization / initialize
repeat
Sample HR and LR image pairs (x..x.). and prompt e from batch B; calculate the latent z; let
Xg Gp c) and reference model generate Gu e); decode z, to image then calculate adversarial features d, Ga(x;i (, c) and d,, = Go(xa; RandPad augument d,, and d, Update with o 1/ Lg. where
Ls = spl (sg((d))) sp(ha(ss((pa))))
Update Go with v = vb 1/ V L.p. where
Ls Xx, l + A spl- (x,) (x,,,,) + (x,x(xx,),))
until achieving convergence or processing 20M image Output: Go.
RandPad. As we also employ RandPad to augment the discriminative features, we provide its pseudocode in Fig. 10. The padding dimensions pad h and pad_M are randomly chosen. We use the 'reflect' mode to perform padding on input images to avoid boundary misalignment of image_neg and image_pos.
PadCFG. In Fig. 11, we present the pseudocode of PadCFG. Based on Eq. (7), we implement the PadCFG in the same manner as the vanilla CFG, we introduce extra padding operations before concatenation. Since the computational complexity of the padding operation is almost negligible, our method can earn "free lunch" for qualitative improvement.
FixMod. We realize a simple fist-fix module (FixMod) to avoid the aberration and further reduce the influence of artifacts. Precisely, construct it by combining the wavelet color fix and small pretrained UNet. We also conduct jointly training of FixMod and GenDR- - to improve robustness and generatlity.
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# RandPad for GT image pad_h random, randint (0, 8) pad_w random. randint (0, 8)
with n)
gt st_image pad (gt image, (pad 8-pad pad w, 8-pad mode=' mode='reflectt") encode with pixel-unshuffle
gt_latents
8)
Figure 10: Pseudocode for RandPad.
image_nee _ee pad (image, (4, 4, 4, 4) mode= lodd='Izete refflett
image_pos pad (image, (3, 5, 3, 5) mode- reflect
image_input concat (Limage_neg, image_pos], dim=0)
# encode with pixel-unshuffle
latents = pixelunshuffle (image 8)
pred unet (latents, t, prompt) neg, pred_ = pred. chunk (2, dim=3)
# PadCFG
latents pred_neg guidence_sca sev (pred_pos pred_reg)
# encode with pixel-shuffle
= pixelshuffle (latents, 8) [: [E 4:-4,
Figure 11: Pseudocode for PadCFG.
A.2 ADDITIONAL. RESULTS FOR GENDR-PIX
A.2.1 ABLATION STUDIES
Effects of guidance scale. Similar to vanilla CFG, the proposed PadCFG can balance the detail diversity and fidelity generated samples by adjusting the guidance scale. In Fig. 13, we test varied guidance scales from 1.0 to 2.0 and compute PSNR and MUSIQ. Specifically, as PadCFG guidance increases, PSNR decreases while MUSIQ (perceptual quality) improves, indicating an inverse relationship where higher guidance enhances perceptual quality (as measured by MUSIQ) the cost of PSNR reduction. In Fig. 12, we compare visual results under different guidance scales. Increasing the guidance scale leads to enhanced sharpness and more intricate details.
Effects FixMod. Tab. 8, we conduct an ablation study on the effects of FixMod. Over- all, FixMod significantly improves perceptual scores by 3.15 and 0.0097 MUSIQ and CLIP- IQA, respectively, outperforming processed im- ages at larger resolutions. Additionally, FixMod is a lightweight module, introducing only 0.02 seconds of latency increase, as demonstrated in Fig. 2. In Fig. 14, we visualize the results of varied post-processing configurations. Notably, while
Table 8: Ablation study on FixMod.
Method
Color Fix
ARNIQA
MUSIQ
CLIPIQA
Baseline
0.6700
67.13
0.6013
Large Res.
0.6854
69.81
0.6843
FixMod
0.6935
70.23
0.6975
FixMod
0.7033
70.28
0.7010
post-processing introduces metric improvement, it provides no additional details but alleviates certain artifacts in the outputs.
Effects of dataset scale and training period. During the training process, we utilize approximately 20 million LQ-HQ image pairs under longer training scheduler to optimize GenDR-pix, which surpasses most existing one-step models. In summary, we employ the data and scheduler strategy for two reasons: 1) GenDR-Pix depends full-parameter training transfer from latent space to pixel
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1.
1.2
24.00
23.75
64.5
PSNR
23.50
MUSIQ
64.0
23.25
23.00
63.5
22.75
MUSIQ
1.0
2.0
PadCFG guidance
2.0
18
Figure 13: Impact guidance scale.
Figure 12: Visual comparison of varied PacCFG guid- ance scales on "Nikon_005_aa LR4" from RealSR.
Baseline
Larger Resolution
FixMod w/o Color Fix
FixMod Color Fix
Figure 14: Visual comparison between baseline, larger resolution processing, and FixMod.
space, which requires more data and time stabilize the training process and avoid overfitting: 2) GenDR-Pix is trained under more complex degradation, which integrates multiple upscale factors using both the RealESRGAN pipeline and the APISR pipeline, requiring more data for comprehensive distillation.
To directly quantify the influence of dataset scale and training steps. we visualize performance trajectories in Fig. 15. All three curves show non-linear "phase-transition behavior. Specifically, ClipIQA experiences a sharp improvement around 50k-100k iterations. However, more importantly, the absolute performance consistently increases across all regimes, even before these transition points. This indicates that the observed gains are not accidental artifacts of data scaling, but stable consequence of the proposed distillation strategy. Moreover, the performance gap between IM and 20M images is only 1.15 on MUSIQ, which is significantly smaller than the influence of our distillation strategy.
64.0
Cerrenatal
ato imagua
65
81.5
MUSIO
MUSIO
82.
Generated Images
62.0
100 200
500
2000
200
500
3000
10C
1M
3M
Number of Training iteration (K)
Number of Training Iteration (k)
Number Training Images
Figure 15: Impact of training iteration and data.
16

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Published as a conference paper at ICLR 2026
Table Quantitative comparison (average Parameters, time, and IQA metrics) on RealSR (x4),
Metries
Methods
#Runtimel
PSNRT
SSIM
LPIPS
NIQEJ
CLIPIQAT
MUSIQT
Q-Alignt
BSRGAN
36mss
26.51
0.7746
0.2685
4,6501
0.5439
63.59
3.8277
Rcal-ESRGAN
36ms
25.85
0.7734
0.2729
4.6788
0.4901
59.69
3,9185
Real- HATGAN
116ms
26.22
0.7894
0.2409
5.1189
0.4336
58.41
3.8353
StableSR-50
3731ms
24.52
0.6733
0.3658
3,4665
0.6897
66.87
3,9862
DiffBIR-50
6213ms
26.28
0.7251
0.3187
5,8009
0,6743
64.28
3,9182
SeeSR-50
6359ms
26.19
0.7555
0.2809
4.5366
0.6826
66.31
3,9862
DreammCear-50
6892ms
24.14
0.6963
0.3155
3,9661
0.6730
63.74
3,9705
SinSR-1
120ms
25.99
0,7072
0,4022
6,2412
0.6670
59.22
3 3,8800
OSEDifi-1
103ms
24.57
0.7202
0.3036
4,3408
0.6829
67.30
4,0664
1
33ms
25.69
0.7362
0.2825
4.1846
0.6695
67.47
4.0772
InvSR-1
115ms
24.50
0.7262
0.2872
4,2218
0.6919
67.47
4,2085
GenDR-1
77ms
23.18
0.7135
0.2859
4.1588
0.7014
68.36
4.2388
GenDR-Pis Pix
32ms
25.03
0.7513
0.2702
4.0751
0.6816
66,29
4,1815
*: inference using? 3 A100 GPU run an image.
Table 10: Quantitative comparison (average Parameters, time, and IQA metrics) on RealSR and Re50LQ250 with scal factorx2 and x3.
RealSR
RealLQ2250
Methods
Scale
PSNRT
LPIPS
ClipIQAT
MUSIQT
NIQEL
CLIPIQAT
MUSIQU
Real-ESRGAN
28.02
0.2062
0.6014
63.54
5,5589
0.6883
67.72
OSEDiII-1
x2
23.29
0.2774
0.7214
67.56
4.1813
0.7056
71.16
GenDR-1
x2
27.18
0.1753
0.6152
66.82
4.3286
0.6981
70.21
GenDR-Pix
x2
27.95
0.1660
0.6797
65.14
4.1320
0.7194
71.59
Real-ESRGAN
x3
26.69
0.2475
0.5762
62.14
4.4329
0.6232
65.90
OSEDiff-1
x3
20.35
0.3165
0.6183
66.97
3,9106
0.6969
70.64
GenDR-1
x3
21.90
0.2416
0,5841
64.61
3.8648
0.6781
71.39
GenDR-Pix
x3
26.80
0.2065
0.6128
60.96
3,8545
0.7067
71.43
A.2.2 QUANTITATIVE RESULTS
In Tab. 9, we present additional results for RealSR (Cai et al., 2019). Specifically, GenDR-Pix achieves higher SSIM and LPIPS values than any other one-step diffusion method, benefiting from lossless pixelshuffle and pixclunshufflc operations. Regarding NR-IQA, GenDR-Pix achieves a score of 4.18. surpassing OSEDiff by 0.1150. Specifically. GenDR-Pix can compete with some multi-step diffusion models like DifIBIR by accelerating by 194.2x. In Tab. 10, we compare the proposed GenDR-Pis Pix with RealESRGAN, OSEDiff, and GenDR under multiple scale factor. Unlike AdeSR tailored for x4 SR, the proposed method achieves remarkable performance for both x2 and x3 SR, showing effectiveness and robustness.
Moreover, we examine GenDR-Pix with the real-world SR for 4K images on RealDeg (Chen et al., 2024) benchmarks. Since most methods need to process 4K SR with a tiling strategy, which is ex- tremely slow, we only include GenDR as representative latent-based method during testing. Tab. 11 presents the efficiency and restoration comparison of proposed GenDR- Pix with BSRGAN (Zhang al., 2021), AdeSR (Chen et al., 2025a), and GenDR (Wang et al., 2025). Specifically, only BSR- GAN and GenDR-Pix can process 4K images without failure and tile strategy. In general, GenDR-Pix slightly behind GenDR but surpasses the other method by large margin.
Notably, we observe that the performance gap between GenDR and GenDR-Pix slightly widens, which may be attributed the overlap introduced by the tiling strategy. For efficiency performance, compared to AdeSR and GenDR, GenDR-Pix performs 4.29 and 12.99x acceleration, respectively. Compared to BSRGAN and AdcSR, GenDR saves 49, % and 64.8 44.8 memory consumption.
A.2.3 QUALITATIVE RESULTS
In Fig. 16, we depict the visual comparison of GenDR against other approaches. GenDR-Pix obtains comparable results to GenDR and surpasses other approaches. Specifically, GenDR-Pix faithfully
17

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Published as a conference paper at ICLR 2026
Table 11: Quantitative comparison (average Parameters, Inference time, and IQA metrics) on Social Media subset from RealDeg. The runtime and memory are measured on RealDeg with an A 100 GPU.
Metries
Methods
Status
#Runtimel
#Memory
NIQEJ
LIQET
ARNIQAT
MANIQAT
CLIPIQAT
BSRGAN
Pass
2.10s
29.65GB
4,1816
2,9022
0.5847
0,3649
0.5787
AdeSR-1 1
Partial
15.37s
42.91GB
4,0064
3,4083
0.6304
0,4001
0.6523
"GenDR-1
Pass
46.51s
8,47GB*
3.6740
3.8478
0.6968
0.4199
0.6968
GenDR-Pix
Pass
3.58s
15.10GB
3.5701
3.7172
0.6612
0.4092
0.6570
*: inference using tiling strategy for an image.
BJIC GUS MaazzA
FEMI EAE
BJ14 GUB
J CUE
BJ1 CUE
Image
StubleRR-5
DinBIR
SeeSR.55
GenDR
J14 GUE
BJIC DUE
BJI4 CUE
8.54 CUB
HUK CUE
BJ14 CUE
L.Q
Real-ESRGAN
AdeSR-1
OSEDifF-1
InvSR-1
GenDR-Pis Pix
fower2
StubleRR-S
DinBIR
LQ
Real-ESRGAN
AdeSR-1
OSEDifF-
InvSR-1
GenDR-Pix
Figure 16: Visual comparison of GenDR-Pix with other methods for x4 task on RealSet80 dataset.
restores the plate number "BJI4 CUE" and grille. For flower2, method presents more meticulous details for the stamen, despite suffering sequela of repeated patterns.
A.3 LIMITATIONS
Our method removes the entire VAE of one-step diffusion-based SR model to chase the best efficiency. In situations of the multiple-step model, while the MFS loss and RandPad still work, the encoder and decoder are inapplicable to be removed through multiple stages.
Another limitation lies in the native unet in SD2.1, which executes most computations under lossy downsampled resolution, ignoring the local high-frequency structure. This may intensify the repeated artifacts. In future work, we will consider structure adaptation based on both UNet and DIT.
This paper is explorative work to replace VAE with pixel (un)shuffle in the real-world SR task. However, considering our methods being theoretically competent in other low-level tasks, we believe there would be more following work simplify the diffusion model by removing VAE.
18

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Published as a conference paper at ICLR 2026
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.
19


Reviews:
[{"review_id": "VfrBx5ZuAW", "reviewer": "Reviewer_bRU9", "summary": "This paper presents GenDR-Pix, a one-step super-resolution model that removes the VAE encoder and decoder, enabling direct pixel-space generation. To stabilize training and reduce artifacts, the authors introduce a two-stage adversarial distillation framework along with MFS loss, RandPad, and PadCFG. Trained on 20M images, the model achieves faster inference and lower memory usage while maintaining high perceptual quality.", "questions": "Recent one-step RealISR methods such as (e.g., OSEDiff), commonly leverage pre-trained T2I diffusion models  as initialization. With such strong priors, these models require only 10k–20k training steps to adapt to the SR task. In contrast, the authors of this paper move away from this paradigm: they remove both the VAE encoder and decoder, adopt a GAN-based training scheme, and train from scratch on 20M images using adversarial and perceptual losses.\n\nThis shift raises a fundamental question: Are pre-trained T2I diffusion models truly necessary for high-quality one-step super-resolution, or can a GAN-based model trained with sufficient data achieve comparable results without them?\n\nMore specifically:\n\n- Has the author tried training the student model without any teacher guidance, relying solely on GAN and perceptual losses?\n- Has the author evaluated using a randomly initialized teacher, instead of one derived from a pre-trained T2I model?\n\nThese experiments would help clarify whether the benefits come primarily from the pre-trained semantic priors of T2I models or from the data scale and the adversarial learning strategy itself.\n\nAnother important aspect is the role of training data. The authors employ 20 million images for training, which is significantly larger than the datasets used in previous one-step SR works.\n\nTo what extent does the scale and quality of this dataset, as well as the number of training iterations, contribute to the final performance?\n\nFor example:\n\n- Has the model been evaluated under reduced data regimes (e.g., 1M or 5M images)?\n- How sensitive is the performance to the number of training steps?\n- Is the high perceptual quality primarily a result of the large-scale data, rather than architectural or training innovations?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear motivation and simplification of architecture: \nThe paper explores removing the VAE encoder and decoder in one-step super-resolution, which reduces system complexity and avoids reliance on latent-space operations.\n\n- Practical efficiency improvements: \nBy operating entirely in pixel space, the method achieves notable gains in inference speed and memory efficiency, as supported by quantitative results.\n\n- Targeted technical solutions: \nThe paper introduces specific techniques (e.g., MFS loss, RandPad, PadCFG) to address known challenges like checkerboard artifacts and unstable guidance in pixel-space generation.\n\n- Empirical validation: \nThe proposed method is evaluated on multiple benchmarks, with ablation studies that reasonably support the effectiveness of key components.", "weaknesses": "none", "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": "luuO1EczyC", "reviewer": "Reviewer_6W9z", "summary": "This work aims to eliminate the VAE bottleneck in one-step diffusion-based super-resolution models to enable efficient on-device image restoration. The authors focus on the problem of high latency and memory consumption caused by the VAE in latent-space diffusion models. The authors then try to address this limitation by refining the VAE encoding and decoding, which are often in low efficiency, especially for high-resolution inputs. The authors propose to use a pixel-space diffusion SR model that replaces the VAE encoder and decoder with pixel-level operations and model compression techniques. Evaluations based on some image super-resolution datasets show the effectiveness of the proposed method in terms of restoration quality, inference speedups and memory reduction.", "questions": "Please clarify my concerns in the weakness part. I'm not an expert in this field, but I think this paper may have some merits. I would like to check the authors' rebuttal to decide my final rating.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Overall, this paper focuses on a meaningful topic in improving the efficiency of the diffusion-based restoration process. The whole idea is easy to understand and seems to be effective. The authors identify the VAE as the primary bottleneck in both latency and memory usage of the diffusion-based SR models. The authors aim to eliminate the VAE to improve efficiency without significantly compromising visual quality. The experimental results show reductions in memory and time costs, making the proposed SR method accessible for real-world tasks.", "weaknesses": "I appreciate the authors’ efforts to design a more effective diffusion quantization method. Here, I summarize my major concerns and questions in three parts. \n\n1. The authors propose to replace the VAE with pixel-unshuffle and shuffle operations, so the diffusion procedure is from latent space to traditional pixel space. However, it is unclear about the effectiveness of this strategy. The authors could give a deeper theoretical justification for why latent-space diffusion can be replaced by pixel-space diffusion.\n2. The authors try to prevent discriminator collapse and improve generalization by randomly padding images before feeding them into the discriminator. Could you give some ablation studies on how the padding affects the data augmentation performance?\n3. In equation 2, the authors utilize the masked Fourier space (MFS) loss based on a band-rejection filter. Could you give more details on the mask initialization and how the mask collaborates with the random padding?", "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": "OE2YJpVNGV", "reviewer": "Reviewer_occV", "summary": "The paper presents GenDR-Pix, a pixel-space adaptation of the one-step diffusion-based super-resolution method GenDR, aimed at addressing the memory and latency limitations of VAE-based pipelines for high-resolution images. By leveraging pixel-(un)shuffle operations, the authors remove the VAE entirely, but note that high upscaling (×8) can introduce repeated-pattern artifacts. To mitigate this, they propose a multi-stage adversarial distillation strategy, using generative features from previous stages to guide discrimination, along with random padding to augment features and prevent discriminator collapse, and a masked Fourier space loss to penalize amplitude outliers. Additionally, a padding-based self-ensemble with classifier-free guidance is integrated to improve inference performance. Experiments show that GenDR-Pix achieves 2.8× faster inference and 60% memory reduction compared to GenDR, with negligible visual degradation, and is capable of restoring 4K images in 1 second using 6 GB of memory, outperforming other one-step diffusion SR methods.", "questions": "No", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The idea of achieving efficient high-resolution image restoration by removing the VAE is interesting and promising.\n\n2. The paper is well-written.", "weaknesses": "1.Baselines are VAE latent-space-based models, and forcing the removal of the VAE to operate in pixel space introduces certain risks. As shown in Tables 2 and 3, the proposed method does not achieve the best results. The VAE removal strategy requires more thorough analysis and theoretical justification, which is currently lacking in the paper. The authors do not explain the underlying motivation beyond efficiency, nor do they clarify why this approach works in practice.\n\n2. If the goal is to reduce GPU memory consumption, there are simpler and more effective alternatives, such as tiled VAE, which is already commonly used in high-resolution image generation models.\n\n3. The current experiments are conducted only on SD2.1. To fully validate the effectiveness of the proposed method, experiments should be performed on stronger models, such as SDXL or FLUX-based models. A broader range of evaluations is necessary to convincingly demonstrate its effectiveness.\n\n4. If removing the VAE can indeed improve efficiency, why not adopt a non-VAE-based generative model instead?", "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": "HyNoRdX9jh", "reviewer": "Reviewer_mmPH", "summary": "The paper identifies the VAE as the principal latency and memory bottleneck in one-step diffusion SR, then removes it by operating in pixel space with pixel-unshuffle/shuffle. It proposes a two-stage adversarial distillation, a masked Fourier loss to suppress periodic artifacts induced by large-factor shuffling, a random-padding augmentation for discriminator stability, and a padding-based classifier-free guidance that doubles as a lightweight self-ensemble. Experiments indicate about 2.8× speed-up and roughly 60% memory savings versus a GenDR baseline.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper provides convincing evidence that VAE dominates both time and memory.\n\n- The artifact analysis in frequency space is insightful, and the masked Fourier loss directly targets the shuffle-periodic spikes observed empirically.\n\n- Efficiency results are strong, including 4K processing without tiling in certain settings, and the ablations for discriminator choice, RandPad and MFS are helpful.", "weaknesses": "- My main concern is that the conceptual novelty is incremental relative to prior encoder-removal and pruning approaches although the authors emphasized the differences to AdcSR.\n- Its robustness across scale factors and severe degradation is under-explored.\n- The effect of RandPad to boundaries is not analysized and discussed.", "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)