Latent Wavelet Diffusion For Ultra High-Resolution Image Synthesis
Authors: Luigi Sigillo
Shengfeng He
Danilo Comminiello
Venue: ICLR 2026 Poster (2026)
Keywords: Generative Models
Diffusion Models
Wavelet
Ultra High-Resolution
Primary area: generative models
TL;DR: We enhance Ultra High-Resolution image generation by decomposing latent features into wavelet subbands, allowing the model to focus on frequency-specific refinement during diffusion.

Abstract: High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine-grained visual detail. We present $\textit{Latent Wavelet Diffusion (LWD)}$, a lightweight training framework that significantly improves detail and texture fidelity in ultra-high-resolution (2K-4K) image synthesis. LWD introduces a novel, frequency-aware masking strategy derived from wavelet energy maps, which dynamically focuses the training process on detail-rich regions of the latent space. This is complemented by a scale-consistent VAE objective to ensure high spectral fidelity. The primary advantage of our approach is its efficiency: LWD requires no architectural modifications and adds zero additional cost during inference, making it a practical solution for scaling existing models. Across multiple strong baselines, LWD consistently improves perceptual quality and FID scores, demonstrating the power of signal-driven supervision as a principled and efficient path toward high-resolution generative modeling.

Published as a conference paper at ICLR 2026
LATENT WAVELET DIFFUSION FOR -LTRA-HIGH-RESOLLUION IMAGE SYNTHESIS
Luigi Sigillo Shengfeng He², Danilo Comminiello'
Sapienza University of Rome, SSingapore Management University, SEMBL
luigi. wwgilliiiiiiiooooooooooooo it
LVD
Figure 1:
We propose Latent Wavelet Diffusion, achieving 4K image synthesis without architectural changes or additional inference cost existing latent diffusion models.
ABSTRACT
High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine- grained visual detail. We present Latent Wavelet Diffusion (LWD), lightweight training framework that significantly improves detail and texture fidelity in ultra- high-resolution (2K-4K) image synthesis. LWD introduces novel, frequency- aware masking strategy derived from wavelet energy maps, which dynamically focuses the training process detail-rich regions of the latent space. This is complemented by a scale-consistent VAE objective to ensure high spectral fi- delity. The primary advantage of our approach is its efficiency: LWD requires no architectural modifications and adds zero additional cost during inference, mak- ing a practical solution for scaling existing models. Across multiple strong baselines, LWD consistently improves perceptual quality and FID scores, demon- strating the power of signal-driven supervision as principled and efficient path toward high-resolution generative modeling. The code is available at https: com //tpah/ww.cooooaaaaaaaaaaaaaaaaa
INTRODUCTION
Diffusion models have become dominant paradigm in generative modeling, achieving faat--of-the
art results in tasks such as image synthesis (Podell et al. 2024 Sauer et al. 2024 Lopez et al. 2025
Wang et al.) 2025), super-resolution (Saharia al. 2022; Sigillo et al. 2204 Wang et al. 2024).
and image editing (Brooks et al. 2023 Meng al. 2022). Foundational methods like Denoising Diffusion Probabilistic Models (DDPM) Ho al. (2020) and Denoising Diffusion Implicit Models
Work done during visiting period at SMU. Now European Molecular Biology Laboratory.

---

Published as a conference paper at ICLR 2026
(DDIM) Song et al. (2021a) have enabled increasingly powerful variants. Latent Diffusion Models
(LDMs) Rombach et al. (2022) improve efficiency by operating in a learned latent space, while recent architectures such Diffusion Transformers (DiTs) (Peebles & Xie 2023 Esser et al. 2024) further
enhance scalability and modeling eapacity.
Despite recent advances, generating Altra-high-resoluttinn (UHR) images at resolutions of 2K 4K and beyond remains significant challenge (Tragakis et al. 2024). Simply scaling models trained on lower-resolution data often fails to generalize, leading to repeated structures, blurred textures, and spatial inconsistencies (Bar- Tal et al. 2023 He et al. 2023). Naive alternatives, such as hierarchical generation pipelines and post-hoe super-resolution techniques (Du al. 2024a He et al.. [023)) typically produce oversmoothed outputs. To overcome these limitations, several strategies have been explored. One direction involves direct UHR training or fine-tuning (Ren et al., 2024] Chen et al.. 2024), although this typically demands extensive computational resources, access to proprietary high- resolution datasets (Liu et al. 2024b), and substantial GPU memory for large model backbones (Labs) 2024. Esser et al. (024). Other efforts focus on architectural modifications to improve long-range dependency modeling (Liu et al. 2024a), or optimization techniques that enhance the quality of latent representations (Hahm et al. 2024).
Across these varied approaches, a common limitation persists: most methods treat all spatial regions uniformly during generation, applying the same refinement process to areas with vastly different visual complexity. This uniformity disregards local frequency variation, failing to distinguish between smooth regions and areas rich in textures, edges, or semantic structure. The consequences are twofold. First, computation is wasted on low-detail areas that require minimal refinement. Second, high-detail regions are not sufficiently supervised, leading to artifacts loss of fidelity. These issues stem from both architectural and algorithmic constraints: latent representations often lack the structural granularity required for UHR synthesis, and current diffusion training objectives do not incorporate spatial adaptivity into the denoising process. Together, these limitations present a core bottleneck for perceptually accurate hith--high- image generation.
In this work, we propose Latent Wavelet Diffusion (LWD), a general and modular framework that introduces frequency-sensitiy spatial supervision into the latent denoising process of diffusion models. LWD is motivated by the observation that different regions of an image exhibit varying levels of structural complexity and perceptual importance. While some areas contain intricate textures or sharp edges, others are homogeneous or low in detail. Our goal is to exploit this spatial heterogeneity by allocating greater learning signal to regions with high visual complexity, and reducing supervision in low-detail areas. Importantly, LWD achieves this adaptivity without modifying the underlying architecture of the diffusion model, making it broadly applicable across model families.
The LWD framework consists of three key components:
1. A spectrally-aware VAE fine-tuning objective that improves the structure and diffusability of latent representations. By encouraging semantic consistency and frequency regularity, this objective enhances the suitability of latent spaces for high-resolution generation. It serves as the foundation for the subsequent components of LWD.
wavelet-derived spatial saliency map, computed via a discrete wavelet transform (DWT) applied to the latent representation. This map aggregates the local energy of high- frequecyyy subbands (LH, HL, HH) and is normalized to highlight spatial regions with strong structural detail. These saliency maps interpretable, data-driven, and require additional training, providing principled measure of spatial importance directly from the signal.
3. A time-dependent masking strategy that leverages the frequency-basee saliency maps to modu- late the training loss. At each spatial location and timestep, a binary mask determines whether the denoising loss is applied. Regions with high wavelet energy receive supervision across more timesteps, while simpler areas are updated less frequently. This mechanism introduces spatial adaptivity into the learning process and improves the fidelity of fine-scale detail.
LWD is compatible with a broad range of latent diffusion models, including both classical diffusion and flow-matching formulations. Because it operates solely through the training objective, LWD can be seamlessly integrated into existing pipelines. While ultra-high-reste high-resouttoonn generation is inherently computationally demanding due to the backbone's 's scaling properties, LWD ineurs zero marginal cost relative to the baseline. It requires no architectural changes or cascaded upsamplers, making it practical solution for improving the generation quality of existing models. We demonstrate its
2

---

Published as a conference paper at ICLR 2026
flexibility and effectiveness by applying it to several state-of-the-ar latent diffusion models and evaluating performance on tiira-higg-resolution image synthesis (2K to 4K). Experimental results show that LWD consistently enhances perceptual quality, improves FID scores, and better preserves fine-grained detail, all without increasing inference complexity.
RELATED WORK
Diffusion Models and Latent Generation. Diffusion models have become foundational in generative
modeling, particularly for image synthesis (Shen al., 2025 Zhan al., 2025), by progressively denoising Gaussian noise using a learned score function. Variants based on stochastic differential
equations (Song et al. 2021b); probability flow ordinary differential equations (Lipman et al. 2023).
and reinforcement-teee trained objectives (Black et al. 2024) have expanded the design space with improved stability and sampling efficiency.
To reduce the cost of high-resolution generation, Latent Diffusion Models (LDMs) (Rombach et al. 2022) perform synthesis in a compressed space learned via variational autoencoders (VAEs). However, generation quality is closely tied to the spectral fidelity and structure of these latent representations. Prior work has sought to improve this through enhanced VAE architectures (Esser et al., 2021), hierarchical compression (Takida et al., 2024), and frequency-aware regularization (Skorokhodov et al. 2025; Kouzelis et al.) 2025). We build on this direction by integrating frequency-sensie sensitive supervision both during encoding and throughout the denoising process.
Flow Matching and HlighRResolutinn Diffusion. Flow matching Lipman et al. (2023) (2024) offers an alternative to classical diffusion by learning a continuous velocity field that maps noise to data in latent space, eliminating the need for fixed noise schedules. This formulation underlies models such as Flux Labs (2024), which, paired with DiT backbones, has demonstrated strong performance in ulcra-high-resolution pipelines (Zhang et al.) 2025 Yu et al. 2025). Our method extends this family by introducing frequency-bie objective. Through wavelet decomposition of the latent space, LWD computes spatial saliency maps that guide targeted supervision toward detail-rich regions, enhancing fine structure without modifying model architecture
) Esser et al.
spatial masking into the flow-matching
Variational Autoencoder Optimization. The performance of latent diffusion models at high resolu- tions depends critically on the expressiveness and spectral consistency of the VAE. Improvements in- clude multi-scale encoders (Vahdat & Kautz 20200] Takida et al. (2224), spectral loss functions (Björk)
et al. 2022), and scale-consistency constraints (Skorokhodov et al. 2025 Kouzelis et al. 2025).
Wavelet- methods (Esteves al., 2025) Lin et al.) 2024 Agarwal et al., (025)) enrich latent expressiveness by isolating frequency components, while compression orientted approaches (Xic al. 2025 Tang et al. 2024) (HCohee et al. 2024) aim to reduce token count for improved sampling efficiency. Our LWD fine-tunes a pretrained VAE with scale-consistent spectral loss that sup- presses spurious high-frequency noise. This regularized latent space facilitates downstream wavelet decomposition and supports our spatially adaptive denoising objective.
Ultra High- Righ-Reesouttinn Image Synthesis. Maintaining global structure and fine detail in ultra- resolution generation is persistent challenge. Standard diffusion models tend to produce repetitive
patterns or distortions (He et al. (203)). Existing solutions include cascaded generation (Ho ct al.
2022). progressive upsampling (Gu et al. 2024), domain-specific pipelines (Ren al. 20224). and
latent-space super-resolution (Jcong et al. 2025). However, training-based methods (Xie ct al. 2023
Zheng et al. 2024 Guo et al.] 2024 Chen al.. 2024) are resource-intensir and training- free
approaches Bar- Tal et al. 2023 Lee et al. 2023 often yield local artifacts.
Methods such as ScalcCrafter He et al. (2023) mitigate repetition through dilated convolutions but may distort structure, while ResMaster Shi et al. (2025) uses low-resolution references for guided refinement. HiDiffusion Zhang et al. (0023 introduces architectural changes that risk performance trade-offs,, and progressive strategies like DemoFusion
Du Du al. (2024a) suffer from slow inference and irregular patterns. Diffusion-4K Zhang et al. (2025) and URAE Yu et al. (2025) advance latent modeling and parameter-efficieet adaptation at 4K resolution. Our LWD complements these approaches by introducing driven, spatially adaptive supervision, which improves structural and perceptual fidelity at additional cost.
Generative Modeling in the Frequency Domain, Frequency structure plays an increasingly important role in generative modeling (Yang et al. 2023). Wavelet-based diffusion methods, such
3

---

Published as a conference paper at ICLR 2026
Target
fm
masked
Thresholding
Noiay
T t
18 77,
Diflusion Model
Mask Ad;
wavelet
Wavelet Energy Maps
J.H.A,
Normalioo
(B. H. W)
(B. 1B.8/2 2//3 W/2)
AeLLANie Roy
C
LLH
RHL
Upasmpledd
ZHH
Agavelst
High Freguenccs Enwrgy
(a)
(b)
Figure 2: (a) Temporal evolution of latent 2t, wavelet energy maps A wavelets and attention map Mt across diffusion timesteps. (6) Our wavelet-masked flow matching objective at timestep t. The model computes a wavelet attention map M, from latent 2s to modulate the prediction error between target velocity field (c - 20) and predicted velocity (2 t, y)- This focuses optimization on high-frequency regions with greater perceptual importance. While operations occur in latent space, decoded visualizations
shown for interpretability.
as Wife Phung et al. (2023), and spectral decomposition approaches have proven useful for efficient sampling (Qian et al. (024)), super-resolution (Aloisi et al. 2026) Sigillo et al. (025), and
restoration (Huang al.) 2024b) Zhao al., 2024 Diang al.] 2023). FouriScale Huang al.
(2024a) demonstrated that frequency filtering enhances coherence, and DiffuseHigh Kim et al. (2025)
leveraged low-frequency DWT guidance improve global structure UHR synthesis.
Diffusion-4K (Zhang et al. 2025) incorporated wavelet losses in the latent space to balance frequency bands, but applied them uniformly across all spatial locations. In contrast, LWD introduces wavelet- based spatial conditioning through üime-dependent masking. Rather than treating frequency passive loss signal, our LWD actively uses local frequency energy to modulate supervision across space and time, concentrating on learning where detail matters most. This enables sharper, more coherent synthesis without modifying the underlying model increasing inference cost.
METHODOLOGY
Latent Wavelet Diffusion (LWD) introduces frequency-aware supervision into latent diffusiin models by coupling signal-driven saliency analysis with adaptive training. Our key insight that structural complexity in images is unevenly distributed in space, yet most denoising models refine all positions equally. LWD addresses this by modulating the supervision schedule based on local frequency content, improving detail fidelity without increasing computational cost.
LWD operates in two sequential stages. The first stage fine-tur tane-tnns- a variational autoeneoder (VAE) us- ing a scale-consistency objective. This independent step yields spectrally stable latent representations, preparing the space for the subsequent frequency-based modulation.
In the second stage, we fine-tune latent diffusion model (e.g., Flux) using modified flow-matching objective that integrates rreqeency-guidee supervision. This stage incorporates three tightly coupled components: (1) extraction of wavelet-based spatial saliency maps from latent codes; (2) construction of a dependennndnt mask that adapts the training signal based on local frequency energy; and (3) application of this mask modulate the training loss dynamically across spatial positions and timesteps. Together, these mechanisms introduce spatial adaptivity into the denoising process, directing learning resources toward detail-rich regions. All components are model-agnostic and can be integrated into standard latent diffusion pipelines without architectural changes.
3.1 VAE SONE-TUNIG WITH SCALE- CONSISTENCY Loss
High- reshuutssoltion generation places unique demands on the latent space: must retain both semantic structure and spectral coherence across scales. To ensure this, we fine-tune the variational autoencoder
4

---

Published as a conference paper at ICLR 2026
(VAE) using multi-resolution reconstruction objective that regularizes frequency content while preserving perceptual fidelity.
Formally, let E(x) be the latent encoding of image H, #down downsampled version of and Zdown the downsampled version of the latent 2. Our loss combines four components:
LVAR DD(=) - 2 +a ([(coo)))) Jdown + DKI. (g(2 (x))(((())) +X CIPIPS (D())
Reconstruution
Scale Consistency
Latent Regulatization
Perceptual Loss
Here, wc incorporate a scale-consisteney term (Skorokhodov et al.) 2025 Kouzelis et al., 2025) that encourages the VAE to maintain struc- tural coherence across resolution scales. While originally proposed for general reconstruction, we identify critical for wavelet-guided UHR synthesis. Without this regularization, standard VAEs exhibit spurious high- fighffreuency noise that confounds downstream wavelet masking. This preprocessing naturally suppresses compression artifacts while preserving essential structural in- formation in z, enabling our masking strategy to target meaningful details rather than noise.
Tusi Resd--5t
Sease50 Sana
Trequency bidex
Figure 3: Normalized Discrete Cosine Transform amplitudes over zigzag frequency indices. The 'RGB' curve represents the target spectrum of real images. Our tuning (+SE) suppresses high- frequency energy corresponding to artifacts, align- ing the latent spectrum with the RGB reference to ensure cleaner latent space.
Unlike recent approaches that inject frequency conditioning directly into the encoder (Aloisi et al. 2026 Zhang et al., 2025). our formulation decouples signal regularization and generation: we first sculpt the latent space to exhibit desirable frequency properties, and then this structure guide the training of the diffusion model. This preserves architectural modularity while enabling effective frequency-awart supervision.
3.2 WAVELET-DERIVEL FREQUENCY SALIENCY MAPS
To guide spatial supervision based on structural complexity, we extract saliency maps from latent representations using localized frequency analysis. Given latent tensor E xx we apply single-level Discrete Wavelet Transform (DWT), producing four subbands:
DWT(=) + {ELL ALH, ZHL,
(2)
EHL, zHH) encode directional high- where ELL captures low-frequency approximations and {ZLH, EHLH frequency detail.
We compute the localized high-frequency energy as:
=(i.j)= = 2 Σ
Σ (2 + (2 + (2
where (i.j) denotes spatial position and E (1,...,C) C) indexes feature channels. The resulting energy map E E IR 2/2xW/2 is bilinearly upsampled and min-max normalized per sample to obtain final saliency map Awaveler E [o, 1] H
This map serves proxy for local structural richness, highlighting regions of the latent space associated with high-frequency content (e.g., textures, contours, transitions). Unlike learned attention mechanisms based semantic similarity (e.g.. DINO (Caron et al. 2021)). approach is deter- ministic and directly derived from signal properties. While we refer to Awaveler as an "attention map" for interpretability. is best understood frequency-aware saliency measure.
Notably, our VAE fine-tuning objective (Eq. [ helps suppress sigh-frequeccy artifacts and stabilize spectral behavior. This preprocessing step ensures that high-frequenn activations captured by the DWT correspond to meaningful structure, rather than encoding noise, thereby improving the utility of A Aaeeler for guiding spatial supervision.
5

---

Published as a conference paper at ICLR 2026
3.3 ADAPTIVE FLOW MATCHING WITH BEEQUENCY-GUIDER MASKING
We adopt a continuous-time flow-matching formulation (Lipman et al. 2023 Esser et al. 2024) for training the latent diffusion model. Given target latent 2g and noise sample NO, 7), we define interpolated samples as:
2 (1 20 + tc, E [0,
(4)
and supervise the predicted velocity field ue ve (2,, t, y), conditioned on text y, via:
Lim (| z - Do zr. 6, y)1
(5)
To incorporate
into the training objective, we apply spatially adaptive masking based the wavelet saliency map Awavelet- Specifically, for each location (i,j), we define time- dependent binary mask:
{
if (Awaveled (1, +
M,(i,))
otherwise
where T is the total number of diffusion timesteps and & E (0, sets lower bound on refinement. This ensures that all regions receive at least ET steps of supervision, while high-frequency regions benefit from extended refinement.
While selective spatial supervision has been investigated using transformer attention (Moser et al. 2025), our wavelet-derived saliency offers fundamentally different perspective based on signal processing principles rather than learned semantic features, offering computational advantages and a more generalizable solution.
The final masked loss becomes:
Cmasked
| ② - [(=-zo) 1 t,),
where & denotes element-wise multiplication. This formulation focuses learning capacity on detail- rich regions, improves fidelity in high-frequency content, and does so without increasing inference complexity. Crucially, this mechanism operates purely at the objective level and compatible with any latent diffusion model using flow-based or score-based trajectory.
4
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We evaluate LWD on two ultra-resolution datasets, Aesthetic-4K Zhang et al. (2025) is curated 4K benchmark with GPT-4o-generatee captions and high visual quality. LAION-High-HE High--Rs is a filtered subset of LAION-5B Schuhmann et al. (2022). from which we sample 50K 2K 2K-resolution AJJO-55(Schuumann
and 20K 4K-resolution image-caption pairs. These two datasets differ in both visual and linguistic distributions, allowing us to assess both generation fidelity and generalization under caption variance.
Implementation Details. We implement LWD using PyTorch and pyt orch warrch-waeeee... (Cotter 2019) for the Haar- based DWT. For the masking strategy, we set the lower bound & = 0.3, selected Via ablation to ensure each spatial location receives at least 30% of supervision.
Evaluation Protocol. To evaluate LWD as holistic framework, all 'LWD Model' variants utilize the Scale-Consistent VAE fine-tuning described in Section 3.1 while baseline models are evaluated using their original. off-the-shelli VAE checkpoints. Notably, we observed that LWD significantly accelerates convergence; models required only 10-50% of the training iterations suggested in their original papers to reach convergence. Detailed hyperparameters, training costs, and other configurations are provided in Appendix
Evaluation Metrics. We evaluate ultra-resolution text-to-image generation across three key dimen- sions. For image quality, we use Fréchet Inception Distance (FID) and LPIPS (lower is better), alongside the Gray Level Co-occurrence Matrix (GLCM) Score for texture and JPEG Compression

---

Published as a conference paper at ICLR 2026
Pixrt-SIgma-XL LWD PixAn--igma-XL
URAE
LWD
Sana-1.68
Sana-1.68
Figure 4: Visual comparison of 2K image generations. LWD demonstrates improved detail preserva- tion in complex areas while avoiding over-sharpening texture collapse.
Table 1:
Quantitative results on different metrics. The prompts are from the HPD (Wu et al.
2023
dataset. All images are at resolution of 2048 2048.
Model
FID
LPIPS
MAN-IQA
QualiCLIP
IIPSv2.1
PickScore
2022 SDEdit et al.
35.59
0.6456
0.3736
0.4480
30.92
22.86
I-Max Du el al. 2024b
36.28
0.6750
0.3641
0.4139
30.62
23.02
Diffusion 1ituusion-4K20 Zhhnn al. 2025
37.10
0.6920
0.3550
0.4815
30.55
22,80
Art Artt-Siga-XXie X hie el al 4024
36.58
0.6801
0.2949
0.4438
30.66
22.92
Sana- 1.6B Xic al. 2025
35.75
0.7169
0.3666
0.5796
30.42
Lumina-Image 2.0
al. 2025
54.96
0.6445
0.3663
0,4567
23.08
21.15
FLUX-1.de deve
20.4
37.58
0.6371
0,4110
0,5468
28.73
22,68
URAE Yu el al.
35.25
0.6717
0.4076
0,5423
31.15
22.41
LWD + URAH
32.88
0.6336
0.4099
0.5356
28.78
22.43
Ratio proxy for fine-grained detail. For semantic alignment, report CLIPScore (Hessel et 102T and QualiCLIP Agnoouucii et al. (2024). Finally, for perceptual quality, we use MAN-IQA Yang al. (022), HPSv2. Wu (2023), ) and PickScore Kirstain al. (2023). Higher values indicate better performance for all metrics except FID and LPIPS.
4.2
QUANTITATIVE RESULTS
2K Results. Table I and the top block of Table 2 demonstrate consistent improvements from integrating LWD across multiple backbone models. On the HPD prompt dataset (Wu al. 2023). LWD reduces FID by up to 7% and LPIPS up to 6% while also achieving comparable MAN-IQA and QualiCI.IP, indicating improved semantic alignment and perceptual quality. Moreover, on the Aesthetic dataset (Zhang et al. 2025), these gains are observed across diverse architectures, reinforcing the generality and model-agnostic nature of our approach.
4K Results. On the 4esthetic-4K (the bottom block of Table 2 and HPD prompt dataset (Table 7), LWD outperforms baselines such as URAE and FixArt-Sigma. particularly metrics like FID, especially pronounced in regions with fine structural detail, such as hair, foliage, or architectural elements, highlighting LWD's ' ability to seale effectively ultra-high resolutions. These results suggest that frequency-awe supervision provides
CLIPScore and Aesthetics. The improvements
7

---

Published as a conference paper at ICLR 2026
Table 2:
Quantitative benchmarks of latent diffusion models Aesthetic-Fyal 2048 2048 and
4096 x 4096. GLCM Score measures tigh-freueency texture richness using gray-level co-occurrence matrices, while Compression Ratio assesses visual complexity via JPEG file size heuristies, both introduced in Diffusion-4K Zhang et al. (2025)
Model
FTD
CLIPSeore
Aesthetics
GLCM Score
Compression Ratio
1033-166/Esee et al. 2024
43.82
31.50
5,91
0.75
11.23
SD2-Diff4K-FT6/Ziie ot 2025
40.18
34.04
5.96
0.79
11.73
LWD SD3-F16
38.74
34.94
6.17
0.74
11.99
PixAri-Sigma-Xi XL Chen ul.
2024
39.13
35.02
6.43
0.79
13.66
LWD PixAntSigma-Xi
36.14
35.21
6,27
0.87
6.05
SSaa- Xie al. 2025
32.06
35.28
6.15
0.93
24.01
L.WD Sana 1,68
34.30
35.58
6.23
0.78
27.34
SD3-FI6 Esser et 2024
33.12
5,97
0.73
11.97
SD3-Dit1=K-TTT) 2hinnh el 2025
33.41
5,97
0.70
11.90
LWD SD3-F16
34.08
6.03
0.77
12.27
Sana-116BB Xie al. 2025
34.40
6.14
0.39
48.36
LWD
34.59
6.21
0.60
32.62
meaningful guidance even in challenging struggle. LWD also achieves the highest GLCM score when paired with SD3-FI6, and substantially improves Sana's 's performance on both GLCM and compression ratio, indicating stronger fine-detail retention and texture fidelity, without compromising overall quality.
regimes where baseline methods often
4.3 QUALITATIVE RESULTS
Figures and 9 compare outputs from LWD and baseline models under identical prompts. LWD consistently renders sharper textures in gigh-freueency regions, such as fabric, skin, and hair, while preserving global structure.
Zoomed-in comparisons highlight improved reconstruction of fine details (e.g., hair strands, eyelashes, small objects) that are often blurred or omitted by baselines. These results suggest that frequency- aware masking not only enhanees local precision but does so in context-sensitive manner, avoiding over-sharpening or artifacts. This indicates that LWD effectively balances fine detail refinement with structural coherence. More full-resolution results can be found in Appendix
4.4
ABLATION STUDIES
4.4.1
EFFECT OF SCALF: CONSS--CONSSSYYYY LOSS ON RECONSTRUCTION QUALITY
Table quantitative reconstruction metrics for various VAEs, with and without the proposed Scale-Consistenge (SC) loss, evaluated on the Aesthetic-4K validation set. Across different architec- tures SI3-VAE. Flux- VAF, and Sana-AF, the addition of SC consistently improves performance, particularly in rFID and perceptual quality (LPIPS). For instance, Flux-VAE VAE-SC outperforms its baseline with significant reduction rFID (0.50 vs. 0.73) and an increase in PSNR and SSIM, indicating sharper and more faithful reconstructions. Notably, SD3-VAE-F16- stantial LPIPS improvement (0.18 vs. 0.30), suggesting better perceptual fidelity despite using a more aggressive compression factor (F16). These results confirm that scale-consistent regularization enhances latent representations, making them more robust and structurally aligned, critical properties for downstream diffusion tasks.
achieves sub-
4.4.2 IMPACT OF VAE REGULARIZATION AND WAVELET MASKING
To rigorously evaluate the contribution of cach component within the LWD framework. conducted a detailed ablation study. Our method comprises two main stages: (1) fine- fine-tuning a VAE with a scale- consistency (SC) loss and (2) fine-tuning the diffusion model with our proposed wavelet-masken loss (Lmasted). The study presented here isolates the impact of each component on the final rext-to-image generation quality.
The results, shown Table 4 confirm that both the SC loss and the wevellt-masked loss contribute meaningfully generation quality, with the full LWD framework yielding the strongest performance.
8

---

Published as a conference paper at ICLR 2026
SD3-F16
LWD
LWD URAB
LWD Sana-1.6B
Figure 5: 4K images generated LWD with different architectures.
Table Quantitative reconstruction results of VAEs with and without the Conls-Cossttency Loss 3.1
on Aesthetic-4K (Zhang et al. 2025) validation set.
Model
rFID
NMSE
PSNR
SSIM
LPIPS
SD3-VAE Esser et al. 2024
1.05
0.01
26.54
0.86
0.08
SD3-VAE- Zhang et 2025
0.70
0.07
19,82
0.63
0.30
SD3-VAE-FI6-SC
0.70
0.04
22.58
0.75
0.18
Flux-VAE LLbss
2024
0.73
0.01
27.18
0.89
0.07
Flux-VAE-SC
0.50
0.01
28.14
0.90
0.06
Sana-AE (Xie et al. 2025)
0.74
0.04
22.16
0.70
0.159
Sana-AE-SC
0.55
0.02
23.64
0.73
0.163
The results in Table show that the GLCM score slightly decreases with the full LWD framework. This reflects a known limitation of classical texture metrics like GLCM, which do not always correlate with perceptual coherence. Our method trades statistical complexity for more realistic details, positive trade-off validated by significant improvements in perceptual metrics like FID and Aesthetics.

---

Published as a conference paper at ICLR 2026
Table Ablation Study on the Contributions of LWD Components. We evaluate each component's impact on final generation quality using the Diffusion4k backbone on the Aesthetic dataset at 2048x2048 resolution.
Configuration
FID
CLIPScore f
Aesthetics 1
GL.CM Score t
Baseline FSD)-Dif4k--I6)
40.18
34.04
5.96
0.79
VAE Scale-Conse Scale-Consistency
39.50
34.10
6.05
0.78
+ Wavelet Masking
39.20
34.50
6.10
0.75
Full LWD (Ours)
38.74
34.94
6.17
0.74
4.5 DISCUSSION
Across quantitative and qualitative benchmarks, LWD enhances ultra-resolution image synthesis by integrating signal-derived saliency into the training loss. Compared to both conventional models and prior wavelet-based methods (e.g.. Diffusion-4KK,, it improves perceptual fidelity, semantic alignment, and spectral regularity, without increasing inference cost or modifying the underlying architecture. Its plug-and-ple and--pay nature makes it broadly compatible with modern latent diffusion pipelines.
LWD improves perceptual fidelity while maintaining comparable alignment scores. This reflects an intentional design choice: LWD prioritizes high-frequency detail recovery to prevent texture collapse UHR scales, complementing the base model's 's semantic capabilities rather than replacing them.
Beyond quality gains, LWD represents a shift toward more interpretable and structure-aware supervi- sion. Unlike attention mechanisms that rely on semantic priors, LWD leverages wavelet energy as transparent, self-supervised signal to prioritize detail-rich regions.
This frequency-guided supervision introduces a form spatial curriculum learning, where complex regions receive more focused updates. Such adaptive loss weighting opens avenues for dynamic training strategies, such as frequency-aware learning rates or hybrid spatial-frequency schedules. These mechanisms may be especially valuable in domains where structural detail is critical but semantic guidance weak, such as scientific visualization, material design, or multimodal generation.
Limitations and Future Work. While LWD improves generation quality without architectural changes or inference overhead, it inherits limitations common to latent diffusion models. In particular, VAE compression cause the loss of fine-grained semantic detail, potentially limiting performance in tasks requiring precise spatial alignment or photorealistic accuracy.
Future work could address this by incorporating higher-fidelity latent spaces hybrid approaches that combine latent and pixel-space supervision. Extending LWD to domains such as video generation, depth-aware synthesis, multimodal conditioning also represents a promising direction. The frequency-awer -wre masking mechanism is general and could be adapted to guide temporal attention, cross-modal alignment, or resolution-specife sampling in broader generative contexts.
CONCLUSION
We introduced Latent Wavelet Diffusion (LWD), general and modular framework that integrates frequency-based supervision into latent diffusion models for ultra-bigh-resolutinn image synthesis. By computing wavelet energy maps in the latent space and applying spatially and temporally adaptive masking, LWD selectively emphasizes high-detail regions during training. Without requiring archi- tectural modifications incurring additional inference cost, LWD consistently improves perceptual fidelity and semantic alignment across models such as Flux and SD3. It preserves high gigh-frequency detail and structural coherence more effectively than prior methods, demonstrating the value of signal-aware supervision in guiding the generative process. By unifying principles from signal processing and diffusion modeling, LWD offers scalable and interpretable approach applicable to a wide range of generative architectures.
Broader Impact. LWD promotes efficient and interpretable generation by aligning supervision with signal-leve detail. This may benefit applications requiring controllable high-resoln reghuuesolution synthe- sis, while raising familiar concerns around synthetic media misuse. Incorporating safeguards and provenance tools remains important direction.
10

---

Published as a conference paper at ICLR 2026
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we have included the core Python script detailing our wavelet-based masking algorithm in the supplementary material. Further implementation details, including key hyperparameters and computational requirements, are provided in Appendix section D We release our public GitHub repository_ which contains the complete implementation, training scripts, evaluation code, and the final pre-trained model checkpoints.
ETHICAL STATEMENT
Our work is built upon publicly available datasets commonly used in the field of generative modeling. We acknowledge that these large-scale datasets may contain inherent societal biases, which our model could potentially learn and reproduce. We have used these datasets in accordance with their original licenses. Below, we discuss the potential societal impacts of our work.
POSITIVE IMPACT
The primary goal of our research is to advance the state of high-resolution image generation, which has significant positive applications. These include empowering artists, designers, and content creators with more powerful creative tools; enhancing visual effects for entertainment and media; and potentially aiding in scientific visualization and data augmentation. By developing a method that improves quality with zero inference overhead, we aim to make high-fidelity generative Al more accessible and practical for a wider range of beneficial uses.
POTENTIAL RISKS AND MITIGATION
We recognize that generative models can be misused for malicious purposes, such as creating misinformation ("deepfakes"), generating harmful or explicit content, and perpetuating societal biases. To mitigate these risks, we are committed to the following measures:
1. Responsible Release:
We release our code and models under a responsible AI license (e.g.. a variant of the CreativeML Open RAIL-M license) that explicitly prohibits use for malicious, harmful, or unethical purposes.
2. Acknowledging Limitations:
We are transparent about the limitations of our model and its potential to generate biased or factually incorrect content, as discussed in the main paper.
3. Encouraging Safe Deployment: We strongly encourage all downstream users our models implement their own safety filters, content moderation systems, and ethical guidelines before deploying any applications.
LLM USAGE STATEMENT
A large language model (LLM) was used as a writing assistance tool during the preparation of this manuscript. The LLM's role was limited to improving grammar, clarity, and conciseness. All content was conceived and written by the authors, who take full responsibility for the paper's scientific integrity.
LCKNOWLEDGEMENT
The work of Sigillo was partially supported by "Ricerca e innovazione nel Lazio incentivi per dottorati di innovazione per le imprese per la PA L.R. 13/2008" of Regione Lazio. Project "Deep Learning Generativo nel Dominio Ipercomplesso per Applicazioni di Intelligenza Artificiale Alta Efficienza Energetica", under grant number 21027NP000001366 The work of D. Comminiello was partly supported by Progetti di Ateneo of Sapienza University of Rome under grant numbers RM123188F755F877 and RM1241910FCCBERT and by the European Union under the Italian
https: httpp:://githubb.com///// wtt.://www..iiiiiio.oooooooo
ffusion Ffusiii

---

Published as a conference paper at ICLR 2026
National Recovery and Resilience Plan (NRRP) of NextGenerationEU, "Rome Technopole" (CUP BSSC2000882006 Project 5: "Digital Transition Through AESA Radar Technology. Quantum Cryptography and Quantum Communications".
REFERENCES
Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chat- topadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al. Cosmos world foundation model platform for physical ai. arXiv preprint arXiv:2501 03357, 2025.
Lorenzo Agnolucei, Leonardo Galteri, and Marco Bertini. Quality-aware image-text alignment for opinion-unaware image quality assessment. arXiv preprint arXiv:2403.11176, 2024.
Lorenzo Aloisi, Luigi Sigillo, Aurelio Uncini, and Danilo Comminiello.
Wavelet Diffu-
sion GAN for Image Super-Resolution. Springer Nature Singapore, 2026. ISBN 978-981- 95-4072-3. doi: (0.1007/978-981-0 -81-998-44----3333
URL https: http:/:////ooooo/..1100000 https: /ttpiv//aaxiv..o wttpg//g............. org/10.1007/
978 978-981-1 10-8888----8-0 95-2 36
Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. MultiDiffusion: Fusing diffusion paths for controlled image generation. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.), Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 1737-1752. PMLR, 23 29 Jul 2023. URL httttttts///:cceeeeeee http:/in.........e..e...... pre.s/p2es///222/ far-tal23a. html
ml
Sara Björk, Jonas Nordhaug Myhre, and Thomas Haugland Johansen. Simpler is better: Spectral regularization and up- up-sampling techniques for variational autoencoders. In ICASSP 2022 2022 IEEE International Conference Acoustics, Speech and Signal Processing (ICASSP), pp. 3778- 3782, 2022.
doi: 10. N01100/CCASPP433222222....
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine. Training diffusion models with reinforcement learning. In The Twelfthh International Conference on Learning Representations, 2024. URL https:
/ http///pppneeeeeee/e// net http://eww....eeoorrreoooooooooa
Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18392-18402, 2023.
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 9650-9660, 2021.
Junsong Chen, Chongjian Ge, Enze Xie, Yue Wu, Lewei Yao, Xiaozhe Ren, Zhongdao Wang, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart-o: Weak-to-strone training of diffusion transformer for 4k text-to-image generation. In European Conference on Computer Vision, pp. 74-91. Springer, 2024.
Fergal Cotter. Uses of Complex Wavelets Deep Convolutional Neural Networks. PhD thesis, Apollo University of Cambridge Repository, 2019, URL https: wwww//wwwww/m///////////w/ww.../ repository. cam. ac
uk/hanaale/1811/30666
Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, and Zhanyu Ma. Demofusion:
Democratising high-resolution image generation with no $$S. In Proceedings the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 1189,6168, 2024a.
Ruoyi Du Dongyang Liu, Le Zhuo, Qin Qi, Hongsheng Li, Zhanyu Ma, and Peng Gao. I-max: Maximize the resolution potential of pre-trained rectified flow transformers with projected flow, 2024b. URL htttts://///ttttt
orgg abs /2100 0. 07536
Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEÉE/CVF conference on computer vision and pattern recognition, pp. 12873-1 2883, 2021.
12

---

Published as a conference paper at ICLR 2026
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller. Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference machine learning, 2024.
Carlos Esteves, Mohammed Suhail, and Ameesh Makadia. Spectral image tokenizer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 17181-17190, 2025.
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Joshua M. Susskind, and Navdeep Jaitly. Matryoshka diffusion models. In The Twelfth International Conference Learning Representations, 2024.
URL http:tttts::/ppeeee/eee www.eewwweeeeeeeeeeeeeeeeeeee wtw.://eoooooorneettoteooooo
Lanqing Guo, Yingqing He, Haoxin Chen, Menghan Xia, Xiaodong Cun, Yufei Wang, Siyu Huang, Yong Zhang, Xintao Wang, Qifeng Chen, et al. Make cheap scaling: A self-cascade diffusion model for higher-resolution adaptation. In European Conference on Computer Vision, pp. 39-55.
Springer, 2024.
Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, et al. Lix-video: Realtime video latent diffusion. arXiv preprint wtx::2501100000.. 2024.
Jaehoon Hahm, Junho Lee, Sunghyun Kim, and Joonseok Lee. Isometric representation learning for disentangled latent space of diffusion models. In Forty-forst International Conference on Machine Learning, 2024. URL https: httpt:://ppeeeevieeeeeeee http//pppreviieeeeeeeeeeeee net wtw.:/uueee/oouuteeaaaam www.oorruuuuuuuutunnaeoanaaeaae-
Yingqing He, Shaoshu Yang, Haoxin Chen, Xiaodong Cun, Menghan Xia, Yong Zhang, Xintao Wang, Ran He, Qifeng Chen, and Ying Shan. Scalecrafter: Tuning-free higher-resolution visual generation with diffusion models. The Twelfth International Conference on Learning Representations. 2023.
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference- free evaluation metric for image captioning. EMNLP (1), 2021.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-665 6851, 2020.
Jonathan Ho, Chitwan Saharia, William Chan, David Fleet, Mohammad Norouzi, and Tim Salimans.
Cascaded diffusion models for high fidelity image generation. Journal of Machine Learning Research, 23(47):1-33, 2022.
Linjiang Huang, Rongyao Fang, Aiping Zhang, Guanglu Song, Si Liu, Yu Liu, and Hongsheng Li. Fouriscale: frequency perspective on training-free high-resolution image synthesis. In European Conference Computer Vision, pp. 196-212. Springer, 2024a.
Yi Huang, Jiancheng Huang, Jianzhuang Liu, Mingfu Yan, Yu Dong, Jiaxi Lv, Chaogi Chen, and Shifeng Chen. Wavedm: Wavelet-basee diffusion models for image restoration. IEEE Transactions on Multimedia, 26:7058-7073, 2024b. doi: 10. 10211090MM..222.333555
Jinho Jeong, Sangmin Han, Jinwoo Kim, and Seon Joo Kim. Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion Models. 2025 IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 2355-2365, Los Alamitos, CA, USA.
June 2025. IEEE Computer Society. doi: 10. 10.1009/CVPR3527442250000
URL https
http:// 2025. 00225
Hai Jiang, Ao Luo, Haoqiang Fan, Songchen Han, and Shuaicheng Liu. Low-light image enhancement with wavelet-based diffuuiio models. ACM Trans. Graph, 42(6), December 2023, ISSN 0730-0301.
doi: 10.1145/3618373 URL htt.ttts:220 //ooi. org/ 10. 111453611833
Younghyun Kim, Geummin Hwang, Junyu Zhang, and Eunbyung Park. Diffusehigh: Training free progressive high-resolution image synthesis through structure guidance. Proceedings of the AAAI conference artificial intelligence, volume 39, pp. 4338 4348-4346, 2025.
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Pick- a-pic: An open dataset of user preferences for text-to-image generation. Advances in Neural Information Processing Systemx, 36:36652-36665 2023.
13

---

Published as a conference paper at ICLR 2026
Theodoros Kouzelis, Ioannis Kakogeorgiou, Spyros Gidaris, and Nikos Komodakis. EQ-VAE. Equivariance regularized latent space for improved generative image modeling. In Forty-second International Conference Machine Learning, 2025. URL httpps://pennreeee..ee.
forum? d=UNhW5YY1.06.
Black Forest Labs. Flux. Stttss///ktaab..comm//oo--ttssss
2024.
Yuseung Lee, Kunho Kim, Hyunjin Kim, and Minhyuk Sung. Syncdiffusion: Coherent montage via synchronized joint diffusions. Advances in Neural Information Processing Systems, 36:50648- 50660, 2023.
Bin Lin, Yunyang Ge, Xinhua Cheng, Zongjian Li, Bin Zhu, Shaodong Wang, Xianyi He, Yang Ye, Shenghai Yuan, Liuhan Chen, et al. Open-sora plan: Open-source large video generation model. arXiv preprint arXiv:2412.0013
2024.
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In The Eleventh International Conference on Learning Repre- sentations, 2023. URL https::/ net.
Guangyi Liu, Yu Wang, Zeyu Feng, Qiyu Wu, Liping Tang, Yuan Gao, Zhen Li, Shuguang Cui, Julian McAuley, Zichao Yang. Eric P. Xing, and Zhiting Hu. Unified generation, reconstruction. and representation: Generalized diffusion with adaptive latent encoding-decoodigg In Forty-first International Conference on Machine Learning. 2024a. URL /pwnre/eeeeeeeeeeeeeeeee net/ forum?id=ighh 1iiiuui---iiiggc222222
Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, and Lichao Sun. Sora: A review on background, technology, limitations, and opportunities of large vision models, 2024b.
Elconora Lopez, Luigi Sigillo, Federica Colonnese, Massimo Panella, and Danilo Comminiello. Guess what think: Streamlined ceg-to-image generation with latent diffusion models. In ICASSP 2025 2025 IEEE International Conference Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, 2025. doi: 10.1 D0.1090/CAASSP99600000000 10890005.
Stephane Mallat and Wen Liang Hwang. Singularity detection and processing with wavelets. 1EEE transactions on information theory, 38(2):617-643, 1992.
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun- Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations, 2022. URL ht httpt::://///. http:///pppeevieeeee net. forum?
id aBsCjcPu t3
Brian B. Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio, and Andreas Dengel. Dynamic Attention-Guiddd Diffusion for Image Super-Resolution. In 2025 JEEF/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 451-460, Los Alamitos, CA, USA, March 2025. IEEE Computer Society. doi: 10. 10.11099WACV61000 w00.l...22200000 seeecomptersocceey.oorg
URL https://doi
110. WWw.W1WWV6611100 .t0.:0.1........22 2025. 00054
William Peebles and Saining Xie. Scalable diffusion models with transformers. Proceedings of the IEEE/CVF international conference computer vision, pp. 4195-4205, 2023.
Hao Phung, Quan Dao, and Anh Tran. Wavelet diffusion models are fast and scalable image generators. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pp. 10199- 10208, 2023.
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann. Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: Improving latent diffusion models for high-resolution image synthesis. The Twelfth International Conference on Learning Representations, 2024. URI. https: //tp:t//ppeeevvveeeee ew net forumo id 522221212x111121ff
Yurui Qian, Qi Cai, Yingwei Pan, Yehao Li, Ting Yao, Qibin Sun, and Tao Mei. Boosting diffusion models with moving average sampling in frequency domain. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 891 1-8920, 2024.
14

---

Published as a conference paper at ICLR 2026
Qi Qin, Le Zhuo, Yi Xin, Ruoyi Du, Zhen Li, Bin Fu, Yiting Lu, Xinyue Li, Dongyang Liu, Xiangyang Zhu, Will Beddow, Erwann Millon, Victor Perez, Wenhai Wang, Yu Qiao, Bo Zhang, Xiaohong Liu, Hongsheng Li, Chang Xu, and Peng Gao. Lumina-image 2.0: A unified and efficient image generative framework. In Proceedings of the IEEE/CVE International Conference on Computer Vision (ICCV), pp. 20031-20044 October 2025.
Jingjing Ren, Wenbo Li, Haoyu Chen, Renjing Pei, Bin Shao, Yong Guo, Long Peng, Fenglong Song, and Lei Zhu. Ultrapixel: Advancing ultra high-resolution image synthesis to new peaks. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL
fictps://operret
ew net. htte:/oormmiiivoooo.m d=wooIIdddddddddo Ww3]
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi. Image super-resolution via iterative refinement. IEEE transactions on pattern analysis and machine intelligence, (774)):713-4726 2022.
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion distillation. In European Conference on Computer Vision, pp. 87-103. Springer, 2024.
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski. Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. Laion-Sb: an open large-scal dataset for training next generation image-text models. In Proceed- ings of the 36th International Conference on Neural Information Processing Systems, NIPS '22, Red Hook, NY, USA, 2022. Curran Associates Inc. ISBN 9781713 11771318888.
Hui Shen, Jingxuan Zhang, Boning Xiong, Rui Hu, Shoufa Chen, Zhongwei Wan, Xin Wang, Yu Zhang, Zixuan Gong, Guangyin Bao, Chaofan Tao, Yongfeng Huang, Ye Yuan, and Mi Zhang. Efficient diffusion models: A survey. Transactions on Machine Learning Research, 2025. URL
https://openrew/e net f
Shuwei Shi, Wenbo Li, Yuechen Zhang, Jingwen He, Biao Gong, and Yinqiang Zheng, Resmas- ter: Mastering high-resolution image generation via structural and fine-grained guidance. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 6887-6895, 2025.
Luigi Sigillo, Riccardo Fosco Gramaccioni, Alessandro Nicolosi, and Danilo Comminiello. Ship in sight: Diffusion models for ship-image super resolution. In 2024 International Joint Conference on Neural Networks (IJCNN) pp. 1-8, 2024. doi: 10. 119/11CNN609999.1O1 210..9..2222.0.000000 10650042.
Luigi Sigillo, Christian Bianchi, Aurelio Uncini, and Danilo Comminiello. Quaternion wavelet- conditioned diffusion models for image super-resolution. In 2025 International Joint Conference on Neural Networks (IJCNN, pp. 1-8, 2025. doi: 10. 10//1///NNN69688..... 1.208. 1228578.
Ivan Skorokhodov, Sharath Girish, Benran Hu, Willi Menapace, Yanyu Li, Rameen Abdal, Sergey Tulyakov, and Aliaksandr Siarohin. Improving the diffusability of autoencoders. In Forty-second International Conference on Machine Learning, 2025. URL htt.h:tts//// httttp::///penneeieee n
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Interna- tional Conference on Learning Representations, 2021a. URL
Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations, 2021b. htLhttps:: http:////peeeeeeeeeemm net/ forum?
URI/attps://oppeevviees
1ddPxITG12RRHS
Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada, Woosung Choi, Chich-Hsin Hsin Lai, Naoki Murata, Toshimitsu Uesaka, Kengo Uchida, Wei-Hsiang Liao, and Yuki Mitsufuji. HQ- VAE: Hierarchical discrete representation learning with variational bayes. Transactions Machine
15

---

Published as a conference paper at ICLR 2026
Learning Research. 2024. ISSN 2835-8856. URL http:pss//ooeneeeeeewwet///////s id=xgAVkqrLjx
Anni Tang, Tianyu He, Junliang Guo, Xinle Cheng, Li Song, and Jiang Bian. Vidtok: A versatile and open-source video tokenizer. arXiv preprini arXiv:2412. 2024.
Athanasios Tragakis, Marco Aversa, Chaitanya Kaul, Roderick Murray-Smith, and Daniele Faccio. Is one gpu enough? pushing image generation at higheroresolutions with foundation models. In A. Globerson, Mackey, D. Belgrave, A. Fan, Paquet, J. Tomezak, and C. Zhang (eds.). Advances in Neural Information Processing Sys- tems, volume 37, pp. 41242-41273. Curran Associates, Inc., 2024. doi: 10.52202/ files/paper pdf
079017-1305. URL https: httpe::/prooeedinss..err....... 222.1111088440055557500000033232
Arash Vahdat and Jan Kautz. Nvae: A deep hierarchical variational autoencoder. Advances in neural information processing systems, 33:19667-19679, 2020.
Yufei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C Kot, and Bihan Wen. Sinsr: diffusion- based image super-resolution in a single step. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 25796-25805, 2024.
Z. Wang, E.P. Simoncelli, and A.C. Bovik. Multiscale structural similarity for image quality assessment. In The Thrity-eevntth Asilomar Conference Signals, Systems & Computers, 2003, volume 2, pp. 1398-1402 Vol.2, 2003. doi: 10.1109/ACSSC2203..22222
Zhendong Wang, Jianmin Bao, Shuyang Gu, Dong Chen, Wengang Zhou, and Houqiang Li. Design- diffusion: High-quality text-to-design image generation with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20906-20915, June 2025.
Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023, URL https::tt//. //arivaaaivv////aa///aeeaaaiiiia oroo 2306. 09341
Enze Xie, Lewei Yao, Han Shi, Zhili Liu, Daquan Zhou, Zhaoqiang Liu, Jiawei Li, and Zhenguo Li. Difffit: Unlocking transferability large diffusion models via simple parameter-efficiin fine-tuning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4230-4239 2023.
Enze Xie, Junsong Chen, Junyu Chen, Han Cai. Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li. Ligeng Zhu, Yao Lu, and Song Han. SANA: Efficient high-resolution text-to-image synthesis with linear diffusion transformers. The Thirteenth International Conference on Learning Representations, 2025.
net.
Sidi Yang, Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujiu Yang. Maniqa: Multi-dimension attention network for no-reference image quality assessment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1191 1200, 2022.
Xingyi Yang, Daquan Zhou, Jiashi Feng, and Xinchao Wang. Diffusion probabilistic model made slim. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22552 22562, 2023. doi: 10.1 101119//CVPPS322222222.00000
Ruonan Yu, Songhua Liu, Zhenxiong Tan, and Xinchao Wang. Ultra-resolution adaptation with ease. International Conference Machine Learning, 2025.
Zheyuan Zhan, Defang Chen, Jian-Ping Mei, Zhenghe Zhao, Jiawei Chen, Chun Chen, Siwei Lyu, and Can Wang. Conditional image synthesis with diffusion models: A survey. Transactions Machine Learning Research, 2025. ISSN 2835-8856. URL https: httt:::://peereview.oo httpp//peeeee..eeeeeeee net/ forum? www.::wwwwwwwwwhwhhh.. Survey Certification.
16

---

Published as a conference paper at ICLR 2026
Jinjin Zhang, Qiuyu Huang, Junjie Liu, Xiefan Guo, and Di Huang. Diffusion-4k: Ultra-high- resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
Lin Zhang, Lei Zhang, Xuanqin Mou, and David Zhang. Fsim: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing, 20(8):2378-2366, 2011. doi:
10.11099112200121109933
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference computer vision, pp. 3836-3847, 2023.
Chen Zhao, Weiling Cai, Chenyu Dong, and Chengwei Hu. Wavelet-based fourier information interaction with frequency diffusion adjustment for underwater image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattenn Recognition, pp. 8281-8291, 2024.
Qingping Zheng, Yuanfan Guo, Jiankang Deng, Jianhua Han, Ying Li, Songeen Xu, and Hang Xu. Any-size- dife-diifusion: Toward efficient text-driven synthesis for any-size hd images. Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 7571-7578, 2024.
17

---

Published as a conference paper at ICLR 2026
A THEORETICAL AND IMPLEMENTATION DETAILS OF WAVELET-GUIDED MASKING
This section provides detailed theoretical background the Discrete Wavelet Transform (DWT) and its application in our framework, discusses specific implementation choices and hyperparameters, and analyzes the properties and trade-off's of our proposed method.
A. THEORETICAL FOUNDATION: DWT AND RELEVANCE MAPS
Discrete Wavelet Transform (DWT). The Discrete Wavelet Transform (DWT) decomposes 2D signal, such as an image or a latent tensor, into orthogonal frequency subbands by applying separable cascades of low-pass and high-pass filters along both spatial dimensions. Given an input X E IR XXxXXWD xD single-level DWT produces four spatial subbands:
LL (Low-Low): Approximation coefficients capturing global structure and coarse semantics.
LH (Low-High): Horizontal detail coefficients, sensitive to vertical edges.
HL (High-Low): Vertical detail coefficients, highlighting horizontal edges.
HH (High-High):: Diagonal detail coefficients, encoding fine textures and high-frequency transitions.
Formally, the DWT of X given by:
LL X hoo $ 2 hlow 4
LH X how + Mnigh + 2
DWT(X)
HL hiigh 2 hoo $
HH = X hnigh 2 hnugh 4
where floo hnigh are orthogonal wavelet filters (e.g., Haar or Daubechies), * denotes convolution, and 2 indicates downsampling by factor of 2.
Relevance Map Construction. To identify spatial regions that require enhanced refinement dur- ing generation, we compute a wavelet-bases relevance map from the fighffreuueccy subbands. Specifically, we calculate the aggregated energy of the directional detail coefficients:
Mielevance = LH2 + HI.? + HH²
This yields saliency map that highlights localized frequency-rier -ich regions, such edges, textures, and fine details. The relevance map then resized match the original latent resolution via bilinear interpolation and normalized to the range [0, 1]:
Mrelevance min miinMMeeeesoon (rlevvncce
M
max MMelevance) min Myllevance)
where e is a small constant to avoid division by zero. The LL subband, which encodes coarse spatial content, offers limited information about local complexity and thus excluded. contrast, the aggregated energy of the LH, HL, and HH bands approximates the local gradient magnitude, similar spirit edge detectors like Sobel and Laplacian filters, and aligns with the intuition that visually salient regions often correspond to areas with rich high-frequency content. This approach theoretically supported by prior work in multiscale signal analysis, which demonstrates that the local maxima of wavelet detail coefficients correspond to structural singularities and perceptual boundaries Mallat & Hwang 1992).
A.2 IMPLEMENTATION AND HYPERPARAMETERS
Choice of Wavelet Basis. We deliberately selected the Haar wavelet for its ideal trade-off of properties for our framework:
Computational Efficiency: It is the most computationally efficient wavelet, minimizing training overhead.
18

---

Published as a conference paper at ICLR 2026
Preservation of Discontinuities: Its discontinuous nature makes it exceptionally effective at localizing and preserving sharp edges and contours.
Sparsity and Non-Redundamd Its orthogonality and compact support induce sparse, non-redundant representations, making our energy maps precise and ideal for our masking strategy.
Comparative Analysis with Other Transforms. To empirically validate the selection of Haar wavelets over other frequency analysis methods, we conducted rigorous ablation study comparing our approach against Daubechies wavelets (db2) and FFT-based High-Past filtering. The results are summarized in Table
Two key principles dictate the superior performance Haar in this context:
1. Spatial Localization:
LWD requires a spatially precise mask Myjj.) to target specific latent regions. While global transforms like FFT/DCT provide excellent frequency resolu- tion, they sacrifice spatial localization; high-frequency coefficient corresponds to periodic patterns across the entire image, not specific positions. Recovering spatial energy maps via inverse transformation introduces Gibbs ringing near sharp transitions. These artifacts cause signal leakage into neighboring latent positions, blurring the mask and degrading texture precision (GLCM 0.71 vs 0.74).
2. Compact Support:
Among wavelets, Haar has the most compact support (2 coefficients), minimizing cross cross-position interference. This is critical for generating sharp binary training masks. Smoother wavelets (e.g., Daubechies) introduce wider receptive fields, creating "gray areas" mask boundaries that dilute supervision without semantic benefit.
As shown in Ta while FFT is computationally faster due to hardware optimizations, the spatial artifacts degrade generation quality (FID 39.45). Haar achieves the optimal balance, outperforming Daubechies in both efficiency and final texture fidelity.
Table 5: Ablation of Frequency Decomposition Methods. Comparison on Diffusion4k backbone (Aesthetic dataset, 2048 2048).
Method
Cost (ms) |
FID +
Aesthetics f
GLCM 1
LWD (Haar)
1.136
38.74
6.17
0.74
LWD (Daubechies)
1.274
38.92
6.14
0.73
LWD (FFT High-Pass)
0.875
39.45
6.08
0.71
Wavelet Masking Lower Bound. The primary hyperparameter for our wavelet masking strategy ablation study (Table (6) This study revealed clear trade-off: very low value (e.g., 1 0.1) can cause smooth regions to be under-trained while very high value (e.g.. / 0.7) diminishes the benefit of targeted refinement, causing performance to regress towards the baseline. The value I = 0.3 was found to be robust sweet spot.
the lower bound I. The value 0.3 was chosen based
Table 6: Ablation on the Masking Lower Bound (t).
Masking Lower
FID 4
GLCM Score t CLIPScore
Bound ()
0.0
34.15
0.68
0,5411
0.1
33,21
0.72
0,5420
0.3
32,88
0.74
0.5423
0.5
33.46
0.71
0.5419
0.7
34.02
0.69
0.5407
Intuition for the Masking Strategy. The time-dependent masking schedule designed to allocate more training attention structurally rich regions of the image, which identified via higher
19

---

Published as a conference paper at ICLR 2026
wavelet energy. The schedule ensures that these areas refined over training steps, while still providing a minimum level supervision to all regions. This enhances high-freeg freeuurrecnccy details without overfitting them.
A.3 ANALYSIS OF METHON PROPERTIES AND TRADE-OFFS
Preservation of Global Structure. Our wavelet-masker loss is designed to preserve global coher- ence. The masking computation targets only the high-frequency subbands (I.H, HI., HH), which encode localized detail. The LL subband, which captures low-frequency, global structure, is not involved. This ensures that while local refinement is emphasized, the global scene layout and object structure remain intact.
Robustness and Potential Artifacts. Our wavelet-based masking strategy is agnostic the source of reguffrequency information and has proven robust across diverse scenes without introducing noticeable artifacts. The VAE fine-tuning stage key this stability, as it regularizes the latent space to ensure that the high-frequency energy used for masking corresponds to meaningful content rather than spurious signals. While we have not observed failure cases in our benchmarks, investigating domain-specific behavior is an important direction for future work.
On the Synergy of Frequency Suppression and Utilization. A natural question arises from the apparent tension between our use of a multi-scale VAE loss to suppress spurious high-frequency components, and later of high-frequency energy guide the adaptive masking. These two strategies serve complementary and sequential roles. The VAE loss does not eliminate all high- frequency content; rather, it penalizes frequency components inconsistent across scales, which often correspond to noise or artifacts. This regularization aligns the latent spectral distribution more closely with that of clean, natural images.
Crucially. it is precisely this filtered latent space that makes our frequency-based attention meaningful. Once the latent tensor is regularized, the remaining high-frequency energy (extracted via DWT) is more strongly correlated with visually salient features like edges and textures. In other words, by denoising the latent representation, the VAE enhances the signal-to-noise ratio of our wavelet attention mechanism. This sequential design, first purifying the latent space, then exploiting its structured frequency characteristics, ensures our method combines signal-domain regularization and content-adaptive computation in synergistic manner.
B ADDITIONAL RESULTS FOR 4K
To assess the efficacy of our proposed Latent Wavelet Diffusion (LWD), we conduct a detailed evaluation focusing on the challenging 4K resolution (4096x4096)
B.1 QUANTITATIVE RESULTS
presents the quantitative comparison on 4K image generation. We evaluate the generated images using several key metrics: MAN-IQA (Yang al. 2022) and QualiCLIP (Agnolueci et al. 2024), which assess perceptual image quality and alignment with textual prompts, respectively. Additionally, we compute the GLCM (Gray-Level Co-occurrence Matrix) score as a measure of texture complexity and detail richness in the generated high-resolution outputs. Finally, we report the Compression Ratio, indicating the compressibility of the generated images, which can indicative of redundancy or lack of fine details.
Table 7
Our LWD-enhanced URAE demonstrates competitive performance across all evaluated metrics. Notably, it achieves the highest MAN-IQA score and GLCM score, suggesting superior perceptual quality and richer textural details compared to the baselines. Furthermore. our LWD + URAE achieves favorable Compression Ratio (28.77). better than URAE and PixArt PixArt-Sigma-X. XL. suggesting good balance between detail and redundancy. These quantitative results underscore the effectiveness of our LWD approach in enhancing the visual quality and detail of images generated 4K resolution.
20

---

Published as a conference paper at ICLR 2026
hoiislyle sungheees lining standing front blue
tree stands coln water reflcting starty night kilky Way orange
ustroniaut in lilts-ookored sui stands vibran Feld wilfoowrs, holding helnel and greting dowwmwndd with warm glowing sunset the bacigrmund.
Spider-dan out Host--L
Figure 6: Images generated 4K resolution with LWD+SANA.
21

---

Published as a conference paper at ICLR 2026
surreal landscape depicting an ethereal fusion natural beauty and fantastical archilecture, reminiscent Salvador Dall's dreamlike
colossal lover emerging from earth. its intricate visible it seamlessly paintings. From above the clouds, bathed otherwerdlly glow, casting lavender gold across sky, while cherry tranqui blossoms flutter gently in the foreground, adding sense of serenity to this breathlaking vision where lime and nature interlwine.
Steampunk airship flaating above misty Victorian cityscape, intricate brass and copper mechanical details, golden hour lighting, billewing douds, architectural palette, composition.
Figure Images generated at 4K resolution with LWD+URAE.
22

---

Published as a conference paper at ICLR 2026
LE
sleek black lunury sedan parked on rain-sosked city street at night, reflecting neon lights from neerby buildings. The wet pavement glietens. and the car's smooth highlighted by embient glow the environment.
Girl with pink hair, vaporwave style, ratra nestratic, eyberpunk, vibrant, neon colors, vintage 80s and 90s style, highly detailed.
picturesque alley historic center Naples, with laundry hanging dry flower-filed balconies inwitin aroma of freshly baked pizza the air.
Figure 8: Images generated at 4K resolution with LWD+URAE.
23

---

Published as a conference paper at ICLR 2026
URAE (FLUX-1.der) dev)
Figure 9: Visual comparison of 4K image generations from LWD and competing baselines.
B.2 QUALITATIVE ANALYSIS
To complement the quantitative evaluation, Figures of LWD against selected baselines, presenting side-by-sie comparisons of the generated images.
13 and provide qualitative comparison
The visual comparisons highlight the benefits of LWD enhancement. Our method demonstrates the generation of images with finer and more intricate details, particularly noticeable in complex textures and object boundaries. These qualitative observations align with our quantitative findings, reinforcing the effectiveness of the proposed LWD for nigh-resolution image generation.
Table Evaluation on ultra resolution (4096 x 4096) image generation task with (Wu et al. 2023 prompts.
Method
MAN-IQA (1)
QualiCLIP (t)
GLCM Score
Compression Ratio |
PixAst-Sigman Sigma-- (Chen et al.
2024
0,2935
0.2308
0.48
45.15
Sana- 1.6B (Xie et al. 2025
0.3288
0.4979
0.71
25.89
FLUX-1.de devvv (Labs 2024
0.3673
0.2564
URAE al. 2025
0.3850
0.3758
0.41
38, 86
LWD CRAE
0.4011
0.3855
0.74
28.77
All gualitative examples shown in the paper generated following strict, reproducible protocol to prevent cherry-picking. Prompts were sourced directly from the original papers of the baseline models (e.g., URAE, Diffusion-4K) each prompt, the displayed image is the first output generated using a fixed random seed, applied identically both the baseline and our LWD-enhanced model. We have included our code in the supplementary materials to ensure full transparency. While subjective preferences for certain images may vary, our method consistently improves objective indicators of texture and detail.
Selection Protocol
the HPD benchmark dataset. For
C FREQUENCY-AWARS EVALUATION
To rigorously assess the frequency characteristics of generated images, propose suite of frequency-sensitie metrics that extend beyond standard perceptual scores. These metrics are de- signed to quantify the degree which generated images preserve the natural frequency distribution
24

---

Published as a conference paper at ICLR 2026
URAE
LWD URAE
Figure 10: 4K generation of URAE vs LWW + URAF.
Upper caption: "Fiffel Tower was Made up of more than 2 million translucent straws look like cloud, with the bell tower at the top of the building, Michel installed huge foam-making machines in the forest blow huge amounts of unpredictable wet clouds in the building' classic architecture." Lower caption: "Barbarian woman riding red dragon, holding broadsword, in gold armour"
25

---

Published as a conference paper at ICLR 2026
observed in real images, with particular attention to the presence and quality of high-frrquency details.
High/Low Frequency Ratio (HLFR): We decompose each image using 2D discrete wavelet transform (DWT) and compute the total energy in the detail coefficients thighffreuuency subbands: LH, HI,, HH) and in the approximation coefficients how-freeqeene LL subband). The HLFR is defined as the ratio of high-frequency to low-frequency energy:
EI EuI. + ERи
HLFR
ELL
This ratio reflects the relative emphasis on fine-scale structures. A value similar to the reference (real images) indicates natural distribution of frequency content. Large deviations can signal oversmoothing or hallucinated detail.
Ratio Difference from Real (RDR): To quantify deviation from the natural HLFR, we compute the absolute difference between the HLFR of the generated image and the real reference:
RDR geLF gen HLFR
Lower values are better, indicating better aligmment with the natural frequency distribution.
Wavelet Quality Score (WQS): This metric evaluates the structural similarity between generated and real images in the wavelet domain, where frequency components are explicitly separated by scale and orientation. Given multi-level discrete wavelet transform (DWT) of both the generated image Ig and the reference image I,. we compute the SSIM and MSE for each corresponding subband s across all decomposition levels I. The final WQS aggregates the per-subband scores using frequency-awer weights:
L WQS Σ 1=1 HELL.
E
wh. SSIM 11 A MSE(I. I12),
where wt,, weights that can prioritize perceptually important subbands (e.g., low-frequency LL high-frequency HH), and À is scaling factor that penalizes distortion. The score normalized to [0. 1]. where indicates perfect structural alignment. Higher WQS values reflect better reconstruction fidelity across frequency scales, meaning the model preserves both coarse structure and fine texture.
High-Frequency Energy (HFE): This metric quantifies the total energy of the image's 's high figg-frequency components after wavelet decomposition. For a given decomposition level, we define:
HFE Σ
(1 +1 ||
- 112).
This value provides absolute measure of fine-scale activity in the image. While real images have characteristic HFE ranges, excessive HFE may indicate artifacts or noise, and too little HFE suggests oversmoothing. Alignment with the real HFE is typically ideal.
High-Frequenny Emphasis Index (HFEI): This metric evaluates how much the model over- or under-emphasizes high-frequency content relative the real distribution. We define as:
HFEgen
HFE
HFEI
TotalEnergy gon
TotalEnergy
where total energy is computed over all wavelet subbands. HFEI indicates the generated image places more emphasis high frequencies than real images (potentially hallucinated detail), while HFEI < 0 indicates a loss of fine detail. An HFEI near zero is ideal.
also report FSIM ZZhagg et al. 2011) and MS-SSIM (Wang et al, 2003), which capture visual similarity and structural coherence, respectively. Both metrics bounded between 0 and 1, with higher values indicating better perceptual quality.
Perceptual Metrics:
For completeness,
26

---

Published as a conference paper at ICLR 2026
PixArt-Sigma-XL
LWD PixArt-Sigma-XL
Figure 11: 2K generation PixArt-Sigma-XL. vs LWD + PixArt-Sigma-XI.
Upper caption: "An elderly man with prominent, bushy beard and deep-set eyes wears vibrant orange turban, his weathered face marked by lines of age and experience.". Lower caption: "A lone figure horse stands in misty forest, gazing up at tall, multi-fiered temple surrounded by towering trees and soft, diffused light. Steam rises from the rocks near a stream, creating an atmospheric scene of tranquility and mystery."
27

---

Published as a conference paper at ICLR 2026
SANA
LWD + SANA
Figure 12: 4K generation of Sana LWD Sana.
Upper caption: "A litter of golden retriever puppies playing in the snow. Their heads pop out of the snow, covered in." Lower caption: "A curvy timber house near a sea, designed by Zaha Hadid, represent the image of cold, modern architecture, at night, white lighting, highly detailed."
Results: Table summarizes the performance of diffusion backbones, along with their respective versions enhanced with LWD. LWD consistently improves frequency alignment: for instance, the LWD+Diff4K variant achieves a high/low frequency ratio of 0.0556, nearly identical to the real reference (0.0560), while also minimizing the ratio difference (0.0438). Wavelet Quality Scores improve in both cases, and the HF energy is more tightly controlled, preventing oversharpening. Importantly, LWD preserves or improves FSIM and MS- RS-SSI,, confirming that frequency fidelity does not come the expense of perceptual quality. These results demonstrate that LWD enhances the frequency realism of generated images measurable and interpretable way, offering a principled approach frequency-awe image synthesis.
D HYPERPARAMETERS DETAILS AND PERFORMANCES
Training Configurations and Computational Cost All models were trained NVIDIA A100 GPUs. VAE fine-tuning was performed for 60K steps with a batch size of 4 and a learning rate of 1 10-53. Table 9 details the specific training requirements for each backbone architecture.
28

---

Published as a conference paper at ICLR 2026
SD3-Dif4k-F16
LWD SD3-F16
Figure 13:
2K generation of SD3-Dif14k-16 vs LWD SD3-F16.
Upper caption: "A serene landscape features winding river, flanked by trees with autumn foliage, leading to a rustic wooden cabin with a corrugated roof, set against softly blurred background." Lower caption: "A grand interior featuring intricate stained glass windows, elaborate rose window, ornate frescoes depicting biblical scenes, and elegant chandeliers illuminating the richly decorated walls and arches."
29

---

Published as a conference paper at ICLR 2026
Table 8: Comparison of frequency-sensitive metrics across different methods Aesthetic-4K (Zhang et al. 2025) validation set.
Metric
HLFR
RDFR
WQS f
HFE
HFEI
FSIM f
MS-SSIM
Real
0.0560
0.0000
1.0000
0.0140
0.0000
1.0000
1.0000
Sana-1.6B (Xic et al. 2025]
0.0784
0.0558
0.4673
0.0196
0.6108
0.1324
LWD + 1.65
0.0610
0.0537
0.4701
0.0144
0.5227
0.6217
0.1324
SD3-Diff4k-F16 (Zhang et al.
2025
0.0691
0.0470
0.4624
0.0158
0.8064
0.6155
0.1296
LWD + SD3-F16
0.0555
0.0437
0.4735
0.0144
0.4826
0.6245
0.1521
PixArt-Sigman
0.0550
0.0409
0,4763
0.0119
0.6296
0.1354
0,4255
LWD + PixArt-Sigma-XL
0.0564
0.0500
0,4730
0.0150
0.6239
0.1478
0,5094
Table 9: Training configurations and efficiency gains for LWD across different backbones.
Backbone
Res.
Batch Size Iterations
Training Time
LWD URAE (Flux)
2048
2k
~44 hours
LWD + URAE (Flux)
4096
1
2k
24 hours
LWD Diff4K (SD3)
2048
8
10k
-48 hours
LWD SANA
2048/4096
2/1
33k
24 hours
LWD PixAnt-E
2048
2
1.5k
~4 hours
Hyperparameters All experiments were conducted system with 4 NVIDIA A100 GPUs. Our VAE fine fine-tuning objective (Equation[] [ balances four terms. The weights were adopted from prior work (Wu al. (003), which provides extensive validation for these values. Following established practice, we set the weights to a 0.25, 3 = 0.001, and 0.05.
Training Overhead LWD introduces a marginal overhead during training. The minor increase in peak GPU memory usage (Table IO) is due to the storage of intermediate tensors for the wavelet transform and energy masks. These tensors are small (the size of the latent map) and their memory footprint is insignificant compared to the large diffusion model backhone.
Table 10: Computational Overhead Analysis during training on a single A GPU (64GB).
Method
Mem. Usage (%)
Mem. Usage (GB) Time per 20 Steps (s)
Sana
90.5
57.9
47
Sana LWD
93.9
60.1
47
Another key advantage of LWD) is its efficiency. It is training-only strategy that requires zero architectural modifications. Consequently, LWD-enhanced model has the exact same number of parameters and identical inference time its baseline counterpart.
30


Reviews:
[{"review_id": "m3wUaWedUV", "reviewer": "Reviewer_9QM1", "summary": "This paper proposes to mask the diffusion loss to favor areas where details lie.\nA masking weight matrix $A_\\text{wavelet}$ is initially computed from the input image using a 2x2 wavelet decomposition (only Haar wavelets appears to be mentioned).\nA mask $M_t$ is computed at every training by setting $1$ at every location where $A_\\text{wavelet} + l \\geq t/T$, here $t$ is current training step out of a total of $T$.\nA masked loss is then computed by multiplying the score error with $M_t$, effectively zero-ing gradients in low-detail areas.\n\nIn addition the paper makes use of existing methods for scale-consistency VAE fine-tuning to further improve results and help the conditioning of the latents provided to their core method.", "questions": "1. From Fig.2 I understand the map gets normalized globally, can that impact areas of details with less contrast?\n2. The mask as described progressively vanishes, having more and more 0 as training progresses. What mechanism ensures that no (catastrophic) forgetting is happening for the masked-out points? Does it rely entirely on $l$ being large enough?\n3. In Eq.3 what's the purpose of $1/C$ if $E$ is min-max normalized after?\n4. Have you tried the RMS amplitude $\\sqrt{E(i,j)}$ instead of $E$, given your curriculum is a linear thresholding?\n5. In tables 1 and 2, when comparing to the baselines, do these baselines benefit from scale-consistency VAE fine-tuning?\n6. The scale-consistency VAE fine-tuning seems to originate from previous papers, so I assume it would make sense to make baselines benefit from it unless you consider the scale-consistency fine-tuning part to be your own contribution. Please clarify this.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The idea is simple and easy to implement, it's reminding me of past ideas for super-resolution where people used edge detection maps as an extra signal for learning. \n2. The paper is well written and easy to follow.\n3. The method does not incur any extra training costs or inference cost other than compute the wavelet matrix which is insignificant.", "weaknesses": "1. Notations can be confusing: in some settings $t$ is the current training step (eq.6) while in other settings $t$ is the noise level / diffusion time step conditioner (eq 4, 5). \n2. What happens with other wavelets, or even DCT or FFT which can fulfill similar roles as Haar wavelets? (you mention in the appendix that Haar are the best suited, yet I'd still want experimental confirmation).\n3. Your method has two component (1) scale-consistency VAE fine-tuning (from existing works) and (2) frequential-energy masked loss, what's the effect on the evaluation metrics of each component?\n4. The $l$ ablation and other ablations such as scale-consistency should be in the main paper, not in the appendix.\n5. Some big images such as Fig.1 could be moved to the appendix to make space for scientific content like the aforementioned the ablations and wavelets comparisons that are currently missing.\n6. Experimental results feel inconclusive: sometimes there is improvement, sometimes degradation. There are lot of metrics being reported in tables 1 and 2, it's unclear whether some are more important than others. Just to explain further my point of view: 2.5 FID points reduction from 35.25 to 32.88 does not feel significant because the FID is still very far from 0.", "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": "vpw3AFo85t", "reviewer": "Reviewer_QbLK", "summary": "This paper proposes Latent Wavelet Diffusion (LWD), a frequency-aware modification of diffusion training that enables existing architectures to generate high-resolution samples without architectural changes or excessive sampling cost. The authors use a multiscale VAE training objective together with a wavelet-based diffusion training loss, extending both the latent space and the diffusion process to better address the unique challenges of high-resolution generation. The VAE training ensures that the latent space exhibits well-structured frequency characteristics, while the frequency-aware loss encourages the model to allocate more training capacity to high-frequency regions of the input. Experiments demonstrate that LWD achieves higher-quality generation at high resolutions compared to baseline methods.", "questions": "1. Can you explain Figure 3? To me, SD3-Med has the closest spectrum to the RGB image as far as the plot shows. How does SE tuning help in this case?\n\n2. Can you report HPS scores for Table 2? It might be more reliable compared to other metrics such as FID for text-to-image generation\n\n3. Have you tried training the model only on low-frequencies first, and then shifting the attention more and more toward high-frequency details as the training progresses? The current version has high-frequency training for all steps.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper leverages existing architectures for high-resolution generation with minimal training overhead. Given the importance of high-resolution generation in many applications, the proposed method could have significant practical impact.\n\n- The use of wavelets to modulate the loss function during diffusion model training is, in my opinion, both novel and interesting.\n\n- The method also fine-tunes the VAE component, which appears crucial for maintaining a well-structured latent space at higher resolutions.\n\n- The experimental results demonstrate the benefits of the proposed approach, at least to some extent.\n\n- Overall, the paper is well written and easy to follow.", "weaknesses": "- The quantitative and qualitative results presented in the paper are somewhat confusing, in my opinion. While the qualitative results clearly show advantages of LWD + URAE, the quantitative metrics—particularly HPSv2, which I believe aligns better with human perception than FID—indicate worse performance. Conducting a user study and providing more qualitative comparisons between LWD and existing baselines would strengthen the paper’s contributions.\n\n- The claim regarding identical inference time is somewhat misleading. The inference cost of diffusion models also depends on the input resolution. As discussed in the HiDiffusion paper, generating high-resolution images with models like SDXL is increasingly challenging due to the computational demands at higher resolutions. The authors should clarify this point and perform a more balanced evaluation that demonstrates the inference-time limitations of running large models (e.g., Flux) at higher resolutions.\n\n- The proposed multiscale VAE loss is not entirely novel, as similar formulations have been introduced in EQ-VAE and other prior works. The authors should revise the claim suggesting this is their contribution.\n\n**Minor**:\n- There appears to be a typo in Equation (1).", "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": "4cgM83v1gb", "reviewer": "Reviewer_sQLN", "summary": "This paper introduces Latent Wavelet Diffusion (LWD), a method aimed at improving ultra-high-resolution image synthesis (2K-4K) using latent diffusion models. The authors propose a frequency-aware masking strategy based on wavelet energy maps, which helps focus the training process on detail-rich regions in the latent space. The approach also incorporates a scale-consistent VAE objective to ensure high spectral fidelity. The key advantage of LWD is that it enhances image quality and detail without requiring architectural modifications or additional inference costs. The paper provides experimental results across multiple datasets and compares LWD with several state-of-the-art models.", "questions": "See above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The method is notably efficient, both in training and inference, as it does not require any changes to the underlying model architecture or introduce additional computational cost during inference.\n2. The approach results in noticeable improvements in key image fidelity metrics such as FID and LPIPS, with a demonstrated enhancement in texture and detail preservation.\n3. The frequency-aware saliency map and time-dependent masking strategy offer a interpretable way to focus model attention on high-frequency regions.", "weaknesses": "1. While the method shows improvements in several fidelity metrics, there is a noticeable performance drop on certain tasks, such as PickScore and HPSv2.1. These drops suggest potential degradation in text-image alignment, which could impact the quality of the generated images, as seen in the generated Eiffel Tower in Figure 4. The seasonal inconsistency in the image (the LWD + URAE version of the Eiffel Tower showing a different season than the original) further indicates this issue.\n2.  The paper discusses the VAE fine-tuning with scale-consistency loss, but the contribution of this step to the overall performance is not adequately ablated or isolated. Without a clear comparison, it is difficult to assess how much this component adds to the method’s success.\n3. The paper introduces a saliency map generated using wavelet decomposition, but it does not provide an ablation study to verify whether this saliency map is actually contributing to the improvements in performance. An experiment comparing results with and without the saliency map would help clarify whether this component is functioning as intended and contributing meaningfully to the enhancement of fine details.", "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": "CA3SigW5hL", "reviewer": "Reviewer_Zh5f", "summary": "This paper introduces Latent Wavelet Diffusion (LWD), a novel training framework designed to enhance the quality of ultra-high-resolution (UHR) image synthesis (2K-4K) from latent diffusion models. The authors identify a key limitation in existing methods: a uniform training process that fails to distinguish between low-detail and high-detail regions, leading to wasted computation and a loss of fine-grained texture.\n\nLWD addresses this by introducing a signal-driven, frequency-aware supervision strategy. The framework consists of two main stages. First, a pre-trained Variational Autoencoder (VAE) is fine-tuned with a scale-consistent spectral objective to produce a more stable and spectrally regular latent space. Second, during the fine-tuning of a latent diffusion model, LWD computes wavelet energy maps from the latent codes at each step. These maps are used to create a time-dependent spatial mask, which dynamically modulates the training loss to focus more intensely on detail-rich (high-frequency) regions.", "questions": "See the weakness above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "LWD is a training-only strategy, requiring no architectural changes to the diffusion model and adding zero computational overhead during inference.", "weaknesses": "1 The paper called the proposed VAE is a spectrally-aware VAE, where no spectral objective is involved. In my opinion, it is somewhat overclaimed.\n\n2 Figure 3 is hard to read. For example, what does DCT mean?\n\n3 The title is Latent Wavelet Diffusion. However, in the paper, the wavelet only proposes a mask for RGB-based diffusion training. I do not \nagree that this model is a wavelet diffusion model, and worry about the contribution&novelty of this paper.\n\n4 The improvements in Table 1&2 are limited.", "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)