W-EDIT: A Wavelet-Based Frequency-Aware Framework for Text-Driven Image Editing
Authors: Jiahui Sun
Weining Wang
Mingzhen Sun
Peiyao Wang
Xinxin Zhu
Jing Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion Transformers
Text-driven Image Editing
Training-free Method
Primary area: generative models

Abstract: While recent advances in Diffusion Transformers (DiTs) have significantly advanced text-to-image generation, text-driven image editing remains challenging. Existing approaches either struggle to balance structural preservation with flexible modifications or require costly fine-tuning of large models. To address this, We introduce W-Edit, a training-free framework for text-driven image editing based on wavelet-based frequency-aware feature decomposition. W-Edit employs wavelet transforms to decompose diffusion features into multi-scale frequency bands, disentangling structural anchors from editable details. A lightweight replacement module selectively injects these components into pretrained models, while an inversion-based frequency modulation strategy refines sampling trajectories using structural cues from attention features. Extensive experiments demonstrate that W-Edit achieves high-quality results across a wide range of editing scenarios, outperforming previous training-free approaches. Our method establishes frequency-based modulation as both a sound and efficient solution for controllable image editing.

Published as a conference paper at ICLR 2026
W-EDIT: A WAVELET-BASED FREQUENCY-AWARE FRAMEWORK FOR TEXT-DRIVEN IMAGE EDITING
Jiahui Sun Weining Wang?, Mingzhen Sun?, Peiyao Wang" Xinxin Zhu?, Jing Liu
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences
2 Innttitute of Automation, Chinese Academy of Sciences 2023aaauu2220iaaa. weining wwngfnlppr.ia.cc...
ABSTRACT
While recent advances in Diffusion Transformers (DiTs) have significantly ad- vanced text-to-image generation, text-driven image editing remains challenging. Existing approaches either struggle to balance structural preservation with flexible modifications or require costly fine-tuning of large models. To address this, We in- troduce W-Edit, a training-free framework for text-driven image editing based on wavelet-based frequency-wware feature decomposition. W-Edit employs wavelet transforms to decompose diffusion features into multi-scale frequency bands, dis- entangling structural anchors from editable details. A lightweight replacement module selectively injects these components into pretrained models, while an inversion-banne based frequency modulation strategy refines sampling trajectories using structural cues from attention features. Extensive experiments demonstrate that W-Edit achieves high-quality results across wide range of editing scenarios, out- performing previous training-free approaches. Our method establishes frequency- based modulation both a sound and efficient solution for controllable image editing.
INTRODUCTION
In recent years, diffusion models have become the dominant paradigm for image generation (Ho ett al. [2200] Sohl-
al. 2013 Song al. 2020 [Song & Ermon 2019), achieving remark- able progress in large-scale text-to-image (T2I) generation. Early designs such as Stable Diffusion (Rombach et al. 2022) employ U-Net (Ronneberger al. (0155) backbones, whereas recent frame- works including FLUX (Labs] 2024) and SD3 (Esser et al. (024)) replace U-Net with Diffusion
Transformers (DiT) (Peebles & Xie (203) and integrate flow matching (Albergo & Ganden-Eijjdde 2022 Lipman et al. 2022; Liu et al. 2022) to improve fidelity and efficiency. Despite these advances in generation, controllable text-driven image editing remains a significant challenge.
Text-driven image editing aims to modify images according to natural language instructions, sup- porting operations such object replacement, attribute modification, style transfer, and background editing, as shown in Fig. [ The central difficulty lies in achieving precise and flexible modifications while preserving the global layout and contextual coherence of the reference image. Existing meth- ods fall into groups. Training-based -based methods (Zhang al. 2023b Kawar et al., 2023 Zhang cct al., (023a) adapt diffuusin models to reconstruct reference images before applying edits. While effective, this process is computationally expensive and prone catastrophic forgetting. In contrast, training-fres approaches achieve editing by modifying the sampling trajectory of pretrained models inference time, without additional training. Among them, Inversion-based methods (Brack al.) 2024 Cao et al. 2023 Deutch et al. 2024 Aberman et al., 2024) avoid retraining by mapping images into noise space and resampling with new prompts, but suffer from trajectory drift and lim- ited controllability. Attention-injectin
(Hertz et al., 2022) improve structure preservation but are highly sensitive to layer selection and fail under complex edits. More recently, Stable-Flow (Avrahami et al. (2255)) introduced the idea of vital layer in flow- based diffusion models and performs injection only at these selected layers. This strategy improves stability and enables the completion of certain edits, but it enforces overly rigid constraints, often
approaches such as Prompt-to-Prome
"ooreeponniigg author.

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Published as a conference paper at ICLR 2026
Adding an object
..stronnuuu holding flag... n.monmn wearing a red hat...
desert with camel..."
...ddee with an egg
Scene editing
f..fforsst and fog Object replacement
living bath
sunny day winter day...
the merning night...
lamp wood table lamp... Non-rigid editing
"..biee motorcycle...
... deer
plane air balloon...
alonding raising her orms...""
stasdlng Raapiing...
standing roaring....
"...iting running....
Figure I: Representative editing results produced by W-Edit. Our training-free editing method able perform various types of addition, object removal, and global scene editing. These different edits are accomplished using the same mechanism.
image editing operations, including non-rigid editing, object
(4) W-bdit Magic-Daah
(1) FLUX
(3) Stable Flowe
dlg
street"
dog wearing blue
weuring red
Figure 2: Comparison of editing consistency and modification fidelity across representative
methods. Using different textual prompts with the same initial seed, we observe the following:
(1) FLUX produces relatively stable outputs but often fails to accomplish the intended edits; (2)
MagicBrush struggles to generate precise edits that faithfully follow textual instructions; (3) Stable- Flow delivers more stable results and completes editing tasks, but its mechanism enforces overly rigid constraints on diversity and can still introduce inconsistencies (e.g.. the background remains unchanged despite text modifications); and (4) in contrast, W-Edit produces edits that align closely with textual descriptions while preserving the consistency of unrelated content. Fine details are best viewed when zoomed in.
preventing necessary changes such as scene-level modifications. As shown in Fig. 2 these methods either maintain structure while missing edits, realize edits at the cost of consistency.
We argue that this difficulty arises from the entanglement of global semantics (e.g., layout, object identity) and local signals (e.g., texture, color, fine attributes) in the spatial domain. This entan- glement makes it difficult to simultaneously preserve structure and introduce modifications. The frequency domain, in contrast, offers natural decomposition that aligns with editing objectives. Low-frequency components encode layouts and semantics and thus can serve as reliable anchors for consistency, while high-frequency components capture textures and variations and thus suitable
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for flexible modification. This natural separation aligns directly with editing objectives, enabling explicit control over which parts of the image should remain stable and which parts can be modified. Building this insight, we analyze the feature representations of Diffusion Transformers (DiTs) from a frequency-domain perspective and discover a block-wise frequency progression. We find that early blocks predominantly foeus low-frequency structures, while later blocks refine high- frequency details as shown in Fig. 4 Building on this insight, we reformulate text-driven editing as multi-level frequency control.
Building on this progression, we propose W-Edit, a training-free framework for text-guided image editing from a frequency- wavelet-guided decomposition to provide multi-scale frequency bands with spatial locality, allowing separate treatment of structure and detail. Second, we design a lightweight frequency-band replace- ment module that selectively injects these components into pretrained diffusion models, avoiding the need for costly retraining. Third, we develop an inversion- based frequency modulation strategy that refines the sampling trajectory using structural cues from attention features, minimizing structural drift and improving controllability.
perspective. We introduce three key innovations. First, we employ
Extensive experiments show that W-Edit achieves high-quality results across diverse editing sce- narios. Compared with prior training- free approaches, W-Edit delivers more faithful, consistent, and visually coherent edits, establishing frequency- modulation principled and efficient solution for controllable diffusion editing.
2
RELATED WORK
2.1 TEXT-GUIDED IMAGE EDITING METHODS
Text-driven image editing usually builds upon pretrained T2I diffusion models (Huberman Spiegelglas et al. 2024, (Samuel et al. 2023 Tumanyan et al., 2023 Wallace et al.) | Wu & De la Torre 2023). ranging from early U-Net based backbones such as Stable Diffusion to recent Transformer based models like FLUX (Labs] 2024) and SD3 (Esser et al.) 2024). Existing ap- proaches can be divided into training- basedd--baeddd-e and training-free frree methods. Training-basel methods, such as InstructPix2Pix (Brooks et al. 2023) and MagicBrush (Zhang et al. (202a), construct large- scale instruction-image triplets to learn direct mappings for editing. IMagic (Kawar et al., (023) and SINE (Zhang et al., 2023b) reconstruct reference images by adapting pretrained models, but this is costly and prone to catastrophic forgetting. While effective, these methods demand heavy annota- tion and training costs and often generalize poorly to unseen domains such as video or fine-grained edits. Training-frre methods (Brack et al.) 2024] Cao al., 2023 Deutch et al., 2024 Aberman iet al. 2024). which dominate recent research, avoid dataset construction and leverage pretrained priors instead. Among them, inversion-based approaches (Song et al. 2020 Wu & De Torre 2023) reconstruct a reference image in the diffusion latent space before applying edits, but suffer from trajectory drift or reconstruction errors. Later improvements, such as optimization-bant
cal-
ibration and structural regularization (Brack al., [2024] Cao et al.) [2023] Aberman et al. [024]] Parmar et al., 2023; Tumanyan el al. 2023), enhance stability and control. Autention-modulaate techniques (Hertz et al., 2022] Tumanyan et al. 2023) improve spatial control by injecting cross- and self seff-attention features, yet they remain sensitive to block selection, computationally expensive, and prone to semantic inconsistency. While these methods achieve promising results, they still struggle to preserve global structure while enabling flexible local modifications without retraining.
2.2 FREQUENCY PERSPECTIVE IN DEEP LEARNING
Beyond spatial-domain manipulation. freueencydoomii analysis has proven useful in a variety of vision tasks. Early CNN-based studies integrated Discrete Cosine Transform (DCT) to acceler- ate convergence (Ghosh & Chellappa 2016) or introduced frequency-aware dynamic networks for super-resolution (Xie et 2021). Though not intended for editing, these studies demonstrated the potential of frequency representations in vision. More recently, diffusion-basee works further lever- age frequency cues for generation and editing: FDDiff (Wang et al. 2024) restores sighffrequency details in low-resolution images, FDG-Diff (Zhang al. (2225) enforces
consis- tency, WaveDiff (Phung et al. 2023) decomposes latent features into sub- bands for improved texture preservation, FreeU] (Si et al.
2024 regulates frequency responses to improve the generation qual-
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ity, ConsisID Yuan al. 2025) maintains identity in video generation via frequency control, and FCDiffusion Gao et al. 2024) designs multi-branch frequency pathways for diverse translations. However, these methods typically require additional objectives architectural modifications, limit- ing their plug-and-play applicability. This line of inquiry has been extended to image editing, where methods like FlexiEdit (Koo et al., 2024) refine the DDIM latent space by selectively attenuating high-frequency components to enhance fidelity in non-rigid edits, and FDS (Ren et al. 2025) pro- poses frequency-avee
denoising score improve the stability and quality of text-guided edits. However, these methods typically require additional objectives architectural modifications, lim- iting their plug-and-play and-play applicability. In contrast, W-Edit introduces a training-free wavelet-based frequency modulation framework for text-driven image editing. Instead of relying handcrafted attention injections or heavy retraining. decomposes intermediate diffusion features into multi- scale wavelet bands and selectively injects them to guide the sampling process. This lightweight design enables controllable and structurally consistent editing across diverse tasks, bridging the gap between requency-domail analysis and practical editing applications.
METHOD
Our goal is to enable controllable text-driven image editing without retraining preserves consistency while producing faithful modifications. To this end, we introduce W-Edit, wavelet-based frame- work that disentangles global structure and local details in the frequency domain. As illustrated in Fig. 3
we first extract an inversion trajectory from the reference image, then applies wavelet-based frequency decomposition and fusion at selected blocks, and finally performs frequency-guin ing with structural cues. Specifically, Sec. [3.] introduces wavelet-based frequency decomposition, Sec. 3.2 analyzes block-level frequency characteristics that underpin structural and detail preserva-
edit-
tion, and Sec. 3.3 details our wavelet-based editing framework and modulation strategy.
Insersion
Wavelet Based Frequency Guide
SingleStreamBlooks
Feature
3
N-2
Black
Block
Bleek
Block
Block
Hood
2;
Source Prempi
oostronout
WBFG
WBFG
Inject
Quac
Aticationn
holding flog"
Tanget Prompt
DaaibeStremDDaukk Doock
Frssion Block
Block N-2
N-1
Cuncar Fenture
Block
Black
Block
Block
Atlantion
Kest
Von
BingleStremmBlokk
Target Image
Block
2x
Downtinng
Transffomm
Figure 3: Overall Framework of W-Edit. Given an input image and a corresponding text in- struction, we first perform inversion on the input image to obtain the initial noise and the inversion trajectory. Next, starting from both the inverted noise and new random noise, we sample to gener- ate the reference image and the edited image simultaneously. Within the pretrained T2I model, we selectively choose different Transformer blocks and decompose the reference image's keys (K) and values (V) into multi-scale frequency bands using wavelet transforms. An energy-aware adaptive frequency fusion mechanism then applied to inject these frequency-domat features from the ref- erence image into the sampling process of the edited image. This procedure guides the execution of the editing task, resulting in high-quality images that preserve structural consistency while accu- rately reflecting the desired semantic modifications.
3.1 WAVELET-BANE BASED FREQUENCY DECOMPOSITION
A central challenge in editing is that global semantics (e.g., layout, object identity) and local details (e.g., texture, color, fine attributes) tend to be entangled in the spatial domain. This entanglement makes it difficult to preserve unchanged components while selectively modifying target regions. To
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address this issue, seek project diffusion features into the frequency domain, where struc- tural anchors and fine details can be explicitly disentangled. Traditional transforms like the Fourier Transform provide global frequency decomposition but discard spatial localization, making them unsuitable for tasks that require local or multi-scale control. To overcome this limitation, we adopt the wavelet transform, which introduces hasis functions localized in both space and frequency, way.
abling to simultancously capture global structures and fine details in principled
Suppose that represents independent variable. mother wavelet (b(() generates scaled and shifted versions (t), where is the scale parameter controlling dilation (larger a corresponds lower frequencies), and b the translation parameter controlling position along the t-axis. The continuous wavelet transform (CWT) provides a joint time- uee-ffreuency representation W(a,b)
((a.b) b) / (())a,b(1)dd (1)
(t)
a> O, bE IR,
Va
The discrete wavelet transform (DWT), contrast, yields a compact hierarchical decomposition. For 2D images, one-level DWT produces four sub-bands:
F DWT FA. F.. Fv.
where F is the low-frequenc approximation, and F H. Fv. FD capture horizontal, vertical, and diagonal high-freegg figh-frrequeccy details. Recursive application of DWT to F yields multi-level decompo- sition that represents both global structures and linengrained textures. This decomposition naturally separates global and local signals: low- freeuereycncy components encode layout and semantics, serv- ing as reliable anchors for structural consistency. while figh-frequency components capture textures and variations, enabling flexible and precise modifications. Such multi-scale, spatially localized frequency representation forms the foundation of our W-Edit framework.
This decomposition provides natural separation of consistency (low-frequency) and editability (high-frequency), forming the basis for frequency-aware analysis in Sec. 3.2
3.2 BLOCK- WISE FREQUENCY PROGRESSION IN DIFFUSION TRANSFORMERS
In U-Net based diffusion models, editing is often heuristically guided by selecting early blocks for structure and later blocks for details. This intuition stems from the encoder-decoder hierarchy of U-Nets, where spatial resolution decreases and then recovers. However, such heuristics do not apply to Diffusion Transform- ers (DiTs), whose blocks are architecturally ho- mogeneous and lack clear semantic stages. As result, it is unclear how information is orga- nized across blocks, and whether some blocks emphasize structural cues while others capture fine details.
Lesk deg standing."
T21 model Sampling X
Block N-2
Block N
Block
Block
Block
Blook
Taqus
2,
Atarntion frobur
PLUX
1-13
T-10
Singesmamm
Hlock3]
Block(1)
Plockss
To address this question, analyze interme- diate features of DiT's the frequency domain. Suppose that = is the output feature map the k-th block, where each block processes the in- termediate feature at specific stage of the dif- fusion process. We compute the Fourier trans- form of * as:
Figure 4: Frequency progression in T2I models. Attention features in DiT's progressively capture coarse layouts in early blocks and refined details in later blocks, with outputs progressively refined during sampling, providing strong image priors for generation.
(u, v) =[=*", =
(u, v) E IF2
where (u, v) denotes the frequency-domall representation of 2t, (u, v) are horizontal and vertical frequency indices and F(-} denotes the 2D discrete Fourier transform, and IF2 represents the 2D
frequency domain.
To quantify frequency contributions, we define mid-to-high frequency energy metric. We first define the Mid-Frequenee Radius, Fmid, which acts as the boundary separating the low fow-frequency and high- feehfrreuency regimes
T Fiidd Vmnxx/ //2
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where "max is the maximum possible radial distance in the frequency domain. This Tmid specifies the radius corresponding to the intermediate frequency band and serves as our division threshold. We then define the Mid-To-High Frequency Energy (E)TH) as the total energy accumulated from the radius mid up to the maximum frequency:
max-radius
MTH
Σ r=Tund
Pr(**)
(5)
Here, (2) is the radial power spectrum (the average energy at radius r). F is scalar value that directly measures the mid-to-high frequency components of the feature map.
For completeness, low- and high- high-frequency energies computed as:
Flow
Σ
(u,
Et
Σ
(6)
(u,o)ESuggh
Here, Slow and Snigh denote frequency index sets determined by radial thresholds. We compute the ERr for both SingleStreamBlocks and DoubleStreamBloeks of FLUX and visualize the results to- gether with the attention maps in Fig. 4 Since these blocks employ different fusion strategies, we visualize them separately to highlight distinct frequency behaviors. In the visualizations, darker gions correspond to low-frequency components, while brighter regions indicate higher frequencies. The contours in the attention maps show that early blocks mainly encode frequeeqcccy structural foundations, whereas later blocks exhibit sparser attention distributions that refine high-frequener details. Based on these observations, select blocks with either very high or very low mid-to-high frequency energy as the target blocks for frequency-domatt information fusion.
To validate this choice, compare selected blocks with the vital layers identified in prior work (Avrahami et al., 2025a). in which blocks importance was measured using DINOv2 (Oquab et al.) 2023). We find that these vital layers largely coincide with our selected fusion blocks, indicating strong correspondence between block importance and frequency response. Leveraging these func- tional characteristics.
implement selective frequency-domain injection across different modules. This strategy offers two main advantages. First, it allows each module to operate optimally within its functional domain. Second, enables frequency-awan guidance during sampling, significantly reducing computational overhead while maintaining precise control over both structure and fine- grained details.
3.3 WAVELET-BASER FREQUENCY-GUIDE EDITING
As illustrated in Fig. 3] method builds upon pretrained diffusion flow models. We employ wavelet-based frequency modulation to selectively replace specific frequency bands. This enables precise, flexible, and high-fidelity visual editing in pretrained diffusion models. Notably, our ap- proach is training-free and compatible with a wide range of mainstream diffusion architectures. The framework comprises two primary trajectories: the inversion trajectory and the editing trajectory. During inversion, intermediate noisy latents and key-value (K, V) attention pairs are recorded, capturing the evolutionary trajectory of the source image and serving as references for subsequent editing. The editing trajectory starts from pure noise and incorporates these stored key-value pairs while following new textual instructions. This ensures that latent representations are guided toward the original structure, preserving maximum fidelity and editing precision.
We first implement our framework on FLUX, a flow model. Flow models generate samples by transporting a prior distribution Po (Gaussian noise) toward the data distribution P1 (image man- ifold). In Rd we define time-dependent density Pe : [0, 1] x Rd > Rso and a vector field u. [0, 1] Rd + Rd. The flow Φ, is generated via the ODE:
dd)(x)) (x)() do(x)= =
dt
transporting samples from Po toward P1. To edit real images, we invert them into the latent space, ie., transport samples from pı back Po- Using the reverse Euler solver in FLUX, given the forward update and its reverse:
21-1 21 (aL-1 -1)) (c(2)))
21 21-1 + (a,
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where is the latent at timestep dy is the transport standard deviation, and un is the vector field, assuming (2()) se uy (2-1) for sufficiently small steps. This inversion maps images back to noise while storing key-value pairs for editing.
Frequency Decomposition via Wavelet Transform. To capture multi-scale visual information, apply discrete wavelet transform (DWT) intermediate model features. Given a feature tensor F E R Bxxxx x (or flattened attention F E R BxxD),, we apply DWT to decompose it into low-frequency approximation F and frequnrryy detail coefficients FD.::
F
DWT
(FA; FD, F2,......... FDn
where FA encodes global structure, and F capture edges and textures. This provides multi-scale representation for separate treatment of global structure and fine details.
Adaptive Frequency Fusion. We propose energy-awan adaptive frequency fusion mecha- nism. Let E, Fxt denote the energy the i-th sub-band. We select the minimal set of sub-bands whose cumulative energy reaches a threshold ry.
Fre F
if E 1 S S Ex.
F
(10)
otherwise.
Since visual energy in natural images is predominantly concentrated in low-frequency components representing global structure (Khayam 2003, Pimpalkhute et al. (021), this energy-based selection explicitly locks the reference image's layout. By preserving these high-energy bands and allowing the model to generate the remaining low-energy highffrequency bands, achieve balance where the scene structure remains consistent while fine-gra ane-grained details are free to adapt to text instructions. v balances guidance strength and feature preservation.
Integration with Attention. After fusion, coefficients are reconstructed via inverse DWT (IDWT) and then integrated into attention:
F'
FF
F
DI
F
Attn' Attn'(Q, (Q,K', V') Softmax
QK' va
V
(11)
with K' F'.. V F'Wy, and Q FWQ. Modulating key-value pairs in the frequency domain guides both global and local generation. Low- fwweffreuency bands control composition; high- frequency bands refine details. Energy-aware fusion allows blending multiple references edits dynamically. Compared with DCT-based or learned frequency-contre methods, our wavelet-based approach automatically selects informative frequency components according to energy distribution, offering superior spatial-frequency localization. Integration into attention enables precise, dynamic control while remaining training-free.
EXPERIMENTS
In Sec. 4.1 we benchmark W-Edit against baseline methods using both qualitative and quantitative evaluations. Sec. 4.2 presents a user study evaluating alignment, consistency, realism, and plausi- bility from human perspective. Sec. reports ablation studies that isolate the contribution of each component, while Sec. [4.4 demonsttrass W-Edit's generality and adaptability across different diffusion architectures.
4.1 QUALITATIVE AND QUANTITATIVE COMPARISON
In this section, compare the performance of E-Eiit with several state-of the---rtt--rttt text-driven image editing methods. Our framework is built upon the ILuX model, and we adopt the official public implementations of P2P (Hertz al. 2022), MagicBrush (Zhang al. 2023a). Flow-Edi Kulikov et al. 2024), and Stable-Flow (Avrahami et al. 2025b) for fair comparison.
Qualitative Comparison. As shown in Fig, 5 our proposed W-Edit achieves precise editing in the target regions while faithfully preserving the unedited areas. Specifically, existing methods of- ten struggle preserve the overall layout when applying local edits. For instance, Stable-Flow
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distorts the bicycle pedals and other baselines misplace the basket, leading to global inconsisten- cies. In contrast, our method faithfully maintains the bicycle's ' frame geometry and basket position, demonstrating superior structural consistency. Similarly, for snake coiled around tree, Stable- Flow captures the pose but produces a physically implausible floating body and an anomalous tail, whereas W-Edit generates natural configuration consistent with the scene's overall structure These examples demonstrate that W-Edit, by leveraging frequency-base modulation, can effectively bal- ance local edits with global structure preservation, producing more realistic and consistent results compared to other methods.
Quantitative Comparison. addition qualitative comparisons, we quantitatively evaluate the methods using the PIE-Bench dataset (Ju et al.] 2023), which is specifically constructed for the systematic validation of image editing methods. As shown Table [ W-Edit achieves the lowest FII of 65.44 and the highest CLIP score of 31.84 among all compared methods. This represents an 18.6% reduction FID and 4.5% improvement in CLIP score over Flow-Edit. These gains highlight its superiority in both visual realism and semantic alignment with the editing instructions, driven by advanced generative priors and our wavelet-based editing strategy that faithfully trans- lating textual modifications while avoiding artifacts. Moreover, W-Edit consistently outperforms baselines by achieving a PSNR of 24.06, which 14.5% higher than the second- best method, and LPIPS of 0. 1028, representing 32.5% improvement, underscoring its ability to maintain structural fidelity and perceptual quality. In particular, our method demonstrates a strong capability to pre- serve global composition and background details, while applying precise and localized edits in the target regions. Together, these results provide compelling evidence that W-Edit achieves a more favorable balance between fidelity, alignment, and controllability compared existing training- approaches. Moreover, compared with FLUX, our method only increases inference time by 10.8% and GPU memory usage by 1.6%. More efficiency comparisons are presented in Appendix A.2.
VLM Evaluation. In addition to conventional pixel or feature-level metrics, we employ vi- sion-language models (VI.ss) to provide a more semantic evaluation of editing quality. Following Stable-Flow, we use Phi-3.5-visson (Marah Abdin 2024) to evaluate two criteria: Text Following, which measures whether the edited image matches the prompt, and Minimal Modification, which assesses whether edits are restricted to the described regions. As shown in Table [] W-Edit achieves the best balance between semantic fidelity and structural preservation. Unlike MagicBrush, which tends to prioritize text alignment the expense of global consistency, W-Edit faithfully follows instructions while avoiding unnecessary alterations in unedited areas.
Table I: Quantitative Comparison of Different Methods
PeE-Bench
VLM Evaluation
User Study Scores
Method
CLIP
FID
PSNR
LPIPS
Text Fol.
Modify
Align.
Consist.
Real.
Plaus.
P2P
13
320.65
15.12
0.4736
31.5%
24.0%
2.7
3.0
2.9
2.6
MagicBrush
29.06
206.19
15.68
0.4615
84.5%
33.5%
3.5
2.3
2.4
2,2
Flow-Edit
30.48
80.35
18.33
0.2642
76.0%
54.5%
3.6
3.6
3.4
Stable-Flow
29.16
89.78
21.02
0.1522
77.5%
58.0%
3.7
3.7
4.1
3.4
W-Edit
31.84
65.44
24.06
0.1028
81.0%
63.0%
3.8
4.2
3.8
4.2
USER STUDY
We conducted a user study to evaluate our editing approach. Specifically, we used GPT-4 to generate 100 prompt sets, each consisting of four editing instructions covering object addition, removal, replacement, attribute modification, style transfer, and background change. Fifteen participants rated the editing results on a five-point Likert scale across four dimensions: (1) alignment with the target prompt, (2) consistency with the reference image, (3) realism, and (4) physical plausibility. As shown in Table I our method consistently outperforms all other methods across nearly all aspects, with particularly notable improvements in realism and physical plausibility.
4.3 ABLATION STUDY
To better understand the contribution of different components in W-Edit, we conduct an ablation study and report results in Tab. 2.
Among all configurations, selected-block injection achieves the best overall balance across the three CL.dP-based metrics, yielding the highest average score of 45.48 (see Appendix A.1 for metric def-
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Input
P2P
MagicBrush
Flow-Edit
Stablc-Flow
W-Edit(ours)
"A bicycle against wall with basket and flowers.
"A bridge over river with boats at night."
"A photo a cal in standing and wearing straw hat the street.
S
S
I
S
snake in the jungle tree.
"Ocean crashing on rocks." Qualitative comparison. Our method more faithfully adheres to target prompts while preserving the original image's ' structure.
Figure 5:
initions). contrast, all-block injection maximizes image similarity but almost collapses in direc- tional consistency, suggesting that overly aggressive injection enforces the reference too strongly and suppresses textual guidance. Removing SingleStreamBlocks or DualStreamBlocks also leads to performance degradation. The absence of SingleStreamBlocks harms similarity, while exclud- ing DualStreamBlocks reduces text alignment and consistency. Frequency components further play complementary roles. Without high-frequency signals, the model loses fine-grained consistency, whereas ablating low-frequency components damages structural preservation, resulting lower CLIPimg (See Appendix A.2 for visualization of frequency domain component decoupling.). These findings confirm that W-Edit benefits from both frequency bands and carefully chosen fusion blocks, which together enable favorable trade-off between fidelity, alignment, and editing controllability.
To determine the optimal value for 1/, we per- formed a quantitative sensitivity analysis on the PIE-Bench datasetl. We evaluated perfor- mance using three complementary CL.IP-based metrics. As shown in the Tab r functions as critical slider balancing structural preservation (CLIPimg) and editability (CLIPixU/LLPPttr))
Table 2: Ablation study
Method
CLiPing
CLIPut
CLIPdir
Average
Selected-blrek injection
0.9749
0.3068
0.0826
0.4548
All block injection
0.9988
0.2839
0.0013
0.4280
SingleStreamBloeks
0.9184
0.3162
wio DualStramBBooks
0.9458
0.3089
0.0871
0.4473
high-frrqueeny
0.3092
0.821
0.4301
low- tw-frrquency
0.9249
0.3125
0.0954
0.444B
Lower values (1) < 0.4) favor text alignment at the cost of structural integrity, while higher values (n 0.8) over-preserve the reference, sup-
pressing the intended edit. Among the tested parameters, 0.6 achieved the highest CLIPdir score indicating the most accurate semantic transformation while maintaining high degree of structural

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fidelity. These quantitative findings align with the qualitative visualizations presented in Fig Con- sequently, we fixed 7 = 0.6 to maintain robust performance across diverse tasks while ensuring framework simplicity and efficiency.
Reference
17 = 0.2
1)
17
77
17 1.0
Figure 6: Visualization of the impact of different 7 values the generated results
Cat
Belenacee
Dog
Goocann bauge
Refrrmee
Reveple
Caloraruu
Prisephhl
Girl
Sefnnwe
Brad
Lasoois
Else Mark
Trunys
Figure 7: Qualitative editing results achieved on StableDiffussio..l...
Figure 8: Qualitative editing results achieved on Cog CogVideoX-1.0.
4.4 GENERALIZATION ANALYSIS
To evaluate the generalization ability of our method, we apply W-Edit to StableDiffusion- v1.5 and Cog VodeoXX-1110 (Yang et al. 2024). achieving effective style transfer and subject replacement (see Fig. 7 and Fig. { On
Table 3: Sensitivity Analysis of (1)
CLIPimg (1)
CLIPtxt (1)
CLIPdir (t)
Avg. (t)
0.2
0.8920
0.3210
0.0540
0.4223
StableDiffusion-v...
transforms girl into
0.4
0.9350
0.3140
0.0780
0.4423
multiple identities, generates diverse subjects
0.6
0.9749
0.3068
0.0826
0.4548
under consistent style, and converts images into architectural styles like London landmarks and the Are de Triomphe.
0.8
0.9910
0.2760
0.0610
0.4427
1.0
0,9990
0.2620
0.0050
0.4220
Cog VideoX-1.0, 1.0, performs coherent video edits, such as cat-to-dog and girl-to-boy transformations, maintaining back- ground consistency and preserving identity cues.
5 CONCLUSIONS
We present W-Edit, training-free image editing framework leveraging wavelet decomposition and frequency-guided modulation. By exploiting multi-scale frequency analysis, it ensures consistency and stability across editing operations. Extensive experiments show competitive performance and strong generality in tasks such as object addition, removal, and replacement. Our (requencydomman perspective offers a novel avenue for integrating spectral priors editing and generative frame- works, paving the way for more controllable and versatile content creation.
ACKNOWLEDGEMENT
This research is supported by Artificial Inteliggene-NNationl Science and Technology Major Project (2023/D0121200) the National Natural Science Foundation of China (62531026. 62437001, 62436001), the Natural Science Foundation of Jiangsu Province under Grant BK2024305 lnd the Strategic Priority Research Program of Chinese Academy of Sciences under Grant XDA04080400.
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A APPENDIX
A.1 EXPERIMENTAL EVALUATION SETUP
Experimental Details. Given the advanced generative capabilities of FLUX, we first conduct ex- periments on the FLUX framework, where we select blocks [0. 1, 2. 17, 18, 28, 53, 54, 56] for trequency-domain fusion. Haar wavelets are used as the basis for multi-level decomposition, with the maximum frequency energy weight set to 0.6. For Cog VogieooX--10,, we experiment with blocks [0, 1, 2, 3], employing the bior3.3 wavelet basis for decomposition. On Stable Diffusion v1.5, we operate on the overall features between UNet blocks, using Haar as the wavelet basis. To dispel concerns that the frequency variations shown in Fig 4 might be isolated cases and to verify the generalizability of our observations, we conducted the following extended experiments: We con- structed dataset containing k = 64 diverse text cues (generated by ChatGPT, covering different environments, object sets, and subjects) and calculated the average frequency energy distribution of these samples. The results strongly confirm that the transition from low-frequency structure dom- inance in early blocks to high-frequency detail dominance in later blocks is a consistent feature of the FLUX DiT architecture, rather than an artifact specific to any particular image. This statistical result strongly supports the robustness of our frequency- based block selection strategy.
Datasets. PIE-Bench (Prompt-based Image Editing Benchmark) is benchmark dataset specifi- cally constructed for the systematic validation of image editing methods. It aims to evaluate the editing strategy proposed in this study and compare it with existing inversion methods, while ad- dressing the lack standardized evaluation criteria for current image inversion and editing tech- niques. The dataset comprises 700 images covering both natural scenes and artificial scenes (such as paintings), which are divided into ten editing types: random editing (written by volunteers), object modification, object addition, object deletion, object content alteration, object pose change, object color adjustment, object material modification, background replacement, and image style transfor- mation. Within each editing type, the images are evenly distributed between natural and artificial scenes and balanced across four categories: animals, humans, indoor environments, and outdoor environments. Each image sample includes five key annotations: source image prompt, a target image prompt, an editing instruction, a description of the editing subject, and editing mask. The editing mask is particularly critical for accurately defining the expected editing region and essen- tial for precision evaluation. The dataset was constructed using combination of automated and manual methods-random random editing types were directly written by volunteers, while the other types utilized BLIP-2 to generate source image prompts and GPT-4 to produce target prompts and editing instructions. The results were then manually corrected to ensure accuracy, with two annotators and one reviewer collaboratively completing the annotation of editing subjects and masks. This process provides a standardized, high-quality evaluation benchmark for image editing research.
Phi-3.5-vision is highly capable VLM that demonstrates strong proficiency in both visual perception and linguistic reasoning. Its architecture designed to enable joint understanding and reasoning about the complex interplay between visual content and textual descriptions. The utilization of the VLM for quantitative evaluation is motivated by sev- eral key factors. Firstly, traditional automated metrics, such as LPIPS or PSNR, primarily focus on low-level pixel-wise or structural similarity, which often fail to capture high-level semantic align- ment between images and text prompts. In contrast, VLMs like Phi-3.5-vision are inherently suited for this task, as they can directly assess whether the visual content semantically conforms to the provided textual instruction. Secondly, evaluating "Minimal Modification" requires model to not only understand the intended edit but also to recognize and preserve the unaltered regions of the source image. The joint understanding capability of VLMs allows them to perform this nuanced comparison more effectively than metrics that operate on image pairs without semantic guidance. By leveraging the reasoning capabilities of m.i-3.5-vision our evaluation framework delivers more semantically meaningful and reliable performance measure, particularly for instruction- image editing tasks where faithfulness to both the text and the original image structure is paramount.
VLMs Experiment(Phi-355vviiiii
CLIP Metrics To comprehensively evaluate the performance of our image editing framework, we employ three Claed-based metrics that measure different aspects of editing quality as follows.
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(1) CLIPimg (Image Similarity) quantifies the visual fidelity between the edited image and the orig- inal input by measuring their feature-space cosine similarity in the CLIP image encoder. Higher values (closer to 1.0) indicate better preservation of the original image's content and structure, en- suring that edits maintain the essential characteristics of the source material.
(2) CLIPtxt (Text Alignment) assesses how well the edited image corresponds to the target textual instruction by computing the cosine similarity between CLIP image features of the output and CLIP text features of the prompt. This metric captures the semantic alignment between visual content and textual description, with higher values indicating more accurate interpretation of editing commands.
(3) CLIPdir (Directional Consistency) measures the precision of attribute manipulation by calculat- ing the cosine similarity between the vector from original to edited image in CLIP space and the vector from source target text. This metric specifically evaluates whether the editing direction follows the intended semantic pathway, with positive values indicating consistent transformations and negative values suggesting deviation from the desired editing trajectory.
Together, these three metrics provide a multi multi-dimensional assessment of image editing systems, balancing the trade-off's between content preservation (CLIPimg), instruction faithfulness (CLIPtxt), and editing precision (CLIPdir).
A.2 QUALITATIVE AND QUANTITATIVE EXPERIMENTS.
To comprehensively demonstrate W-Edit's versatility across diverse editing scenarios, we present qualitative results for six core editing tasks: non-rigid editing, object addition, object removal, object replacement, attribute modification, and background editing. Each task is designed to evaluate distinct aspects of the framework's 's capabilities, from precise local manipulations to global semantic transformations, while maintaining structural and semantic consistency with the reference image. The non-rigid editing task handles deformable transformations including pose changes and facial expressions. Object addition tests spatial reasoning when introducing new elements. Object removal assesses background reconstruction proficiency. Object replacement evaluates semantic understanding when swapping objects. Attribute modification validates fine-grained control over object characteristics. Background editing examines global scene transformation capacity. These tasks span from localized object manipulations to comprehensive scene transformations, providing thorough evaluation across various editing complexities.
Quantitative results. Based on the quanti- tative results in Table our method demon- strates complementary strengths across edit- ing tasks. The Add operation excels in im- age preservation (CLIPimg: 0.9740), while Re- move achieves the best text alignment (CLIP- txt: 0.2943). Scene editing shows superior di- rectional control (CLIPdir: 0.1195), and Re- place maintains balanced performance across metrics. These specialized capabilities provide users with flexible editing strategies tailored to different sequirementt-wwethee prioritizing visual fidelity, semantic accuracy, or edit controllabil- ity. The consistent performance validates our method's robustness across diverse editing scenarios.
Table 4: CLIP metrics comparison across differ- ent tasks
Task
CLIPing
CLIPixi
CLIPdir
Add
0,9740
0,2869
0.0679
Attr
0.9224
0.2857
0.0990
Replace
0.9073
0.2885
0.1184
Remove
0.9558
0.2943
0.0728
Scene
0.9139
0.2811
0.95
Non-rigid
0,9291
0,2837
0.0493
Ablation Study In addition to quantitative metrics, we also qualitatively analyze the ablation re- sults, shown in Fig. g When all blocks are used for injection, the editing signal becomes overly strong, causing the output to almost replicate the reference image and significantly degrading text similarity. Using only single-stream or dual-stream blocks similarly reduces text alignment and in- troduces inconsistencies with the reference layout. In fact, injecting only at dual-stream blocks even leads to erroneous generation, such as altering the appearance of the crocodile. This occurs because dual-stream blocks lie at lower depths and thus primarily capture oow-frequeccy structure- -whil preserving global layout, they lack high-frequency information, resulting in distorted object details. Conversely, ablating high-frequency components causes loss of fine details (e.g., the crocodile's teeth), whereas ablating low-frequency components disrupts structural integrity, leading to issues such as inconsistent background regions (c.g., mismatched trees).
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Reference image
Fusion Layer
Single-Stream Only
Ablate low-frequency
All-layer processing
Double
Double-Stream Stream Only
Ablate fiquefreyeency
Figure 9: Visualization results ablation experiments
Table 5: Memory and time usage
Memory and time usage Table compares the computa- tional efficiency of different methods, revealing notable per- formance differences. MagicBrush, built on the U-Net archi- tecture, requires only 16.75 GB of memory with a processing time of 20.38 seconds. In contrast, the other methods are based on the FLUX framework. consuming similar memory (around 35.5-36.1 GB) but with longer processing times ranging from 46 50 seconds. Our method achieves superior editing perfor- mance while incurring only a 10.8% increase in runtime and a 1.6% increase in memory usage com- pared to FLUX. While W-Edit delivers competitive editing quality, this efficiency analysis provides practical guidance for selecting methods under given computational resource and time constraints.
Method
Memory (GB)
Time (s)
MaigeBrush
16.75
20.38
FLUX Flow-Edit
35.58
46.00
36.09
Stable-Flow
35.59
47.00
W-Edit
35.59
50.00
Qualitative results. Figures showcase results for object-centric edits. For object addition (Fig. (3). W-Edit exhibits strong spatial reasoning by integrating new objects (e.g., ceramic vase on wooden table, hardcover books on shelf, and lit candles on birthday cake) into the existing scene geometry without occluding unrelated elements or introducing visual artifacts. The added objects inherit lighting conditions and texture consistency from the reference, ensuring perceptual coherence. In object removal (Fig. [4]. the framework not only eliminates target elements (c.g., scattered rocks, folding chair, park benches) but also inpaints the resulting regions with contex- tually appropriate content- such extending grassy terrain matching wall textures avoiding the blurred edges or semantic inconsistencies common in baseline methods. For object replace- ment (Fig. (5), W-Edit preserves fine-grained spatial relationships (e.g., the position of cake on a plate, bread on a cutting board) while substituting objects with semantically distinct alternatives, demonstrating its ability to disentangle object identity from scene layout. For non-rigid editing (Fig. [66.. W-Edit demonstrates remarkable capability in handling complex deformable transforma- tions, including facial expression changes, animal pose adjustments, and body posture modifications, while maintaining structural integrity and avoiding unrealistic distortions. In attribute modification (Fig. (7). the framework achieves precise control over object characteristics such as color, mate- rial, and texture, enabling fine-graines edits like altering car colors or modifying book appearances without affecting unrelated regions. Finally, for background editing, W-Edit seamlessly transforms entire environments while preserving consistency of foreground objects, showcasing its capacity for comprehensive scene-level modifications that maintain both semantic coherence and visual plausi- bility.
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As demonstrated in Fig. we comprehensively visualize the editing capabilities of the proposed W-Edit model across the diverse PIE-Bende dataset. Specifically, W-Edit successfully executes vari- ous complex operations, including but not limited to: adding or removing objects in scene, altering the subject's 's content, pose, background, material, and color, as well as applying overall style trans- fer. 'hese examples encompass diverse subjects such as animals, humans, and everyday objects, qualitatively validating the model's robustness and precision in understanding and executing com- plex editing instructions, thereby confirming its powerful and versatile image editing performance.
We also compared W-Edit with the current large-scale pre-trained model FLUX.1 Kontext in Fig 19 As the qualitative comparison in the provided images shows, W-Edit's 's structural fidelity not infe- rior to FLUX.1 Kontext when performing specific edits. For example, in the pose transformations of eagles and dogs, although our method's 's background consistency is slightly worse than FLUUX.1 Kontext, the subject consistency of the edited image is worse than our method. For example, the sub- jects of the dog and eagle change, and the ability to preserve the subject structure is not as good as our frequency domain method. However, in the cat example, it can be seen that FLUX.1 Kontext's generation is more reasonable and of better quality. But in the bridge example, FLUX.1 Kontext cannot even edit correctly. Therefore, in terms of structural fidelity, W-Edit is not inferior to some current resource-intensing pre-trained editing models, based on the qualitative results.
The impact of the value of 1 on the results. To investigate the impact of the hyperparameter 1) on the editing results, we conducted detailed qualitative analysis, shown in Fig. 6 In the W-Edit framework, 7) plays a critical role by essentially controlling the amount of frequency sub-band energy retained from the reference image, thereby striking balance between structural fidelity and editing flexibility. Our analysis indicates that there exists an optimal range for the value of 1j. When is set too low (e.g.. 7 i 0.4), the model retains too little structural information from the reference image. This leads to issues such as structural collapse or loss of subject identity in the edited results, as illustrated in the first column of Fig. This effect similar to the "low-frequency removal" scenario in the ablation study in Section 4.3, where the basic composition of the original image cannot be maintained. Conversely, when r is set too high (e.g.. 17 ? 0.8), an excessive amount of frequency information from the reference image is preserved, which overly suppresses the text- text-guided editing capability. As shown in the last column of Figure X, the editing effect becomes minimal, with the image almost unchanged, resembling the results of the "full-block injection" baseline. As shown in the Tab 3 we found that setting 7 to 0.6 achieves the best balance in most scenarios. Under this configuration. W-Edit is able to firmly preserve the overall structure and subject identity of the source image while effectively responding to textual instructions to produce clear and natural editing effects. The choice of this optimal value is further supported by the quantitative evaluation in Section 4.2, where our method achieved the highest CLIP Score under this setting, demonstrating its superiority in semantic consistency and editing quality. In conclusion, 00666 is established as our default parameter, providing W-Edit with stable and adaptive performance.
Wavelet Transform Feature Decomposition To validate the frequency- mechanism of the W-Edit framework, we performed Wavelet Transform decomposition and frequency domain analysis on features from various blocks of the FLUX architecture. The W decomposes the feature F into a Low-Frequency Approximation Component (Fa) (structure information) and High-Frequency De- tail Component (Fp) (texture information). Visualization reveals non-uniform "Low-Frequeng" Foundation High- -requeery Refinement" evolution trend: early blocks are dominated by FA (es- tablishing structure), while later blocks exhibit significantly increased FD energy (generating fine- grained texture). Quantitative analysis of the mid-frequency energy further confirms this by showing the feature energy centroid shifting from the low-frequency region to the high-frequency region with increasing layer depth. The strong consistency between the visualization and quantification validates the frequency evolution in the FLUX architecture. Ultimately, this provides solid theoretical ba- sis for W-Edit's 's adaptive block selection based on energy metrics, enabling balanced control over structure preservation and detail editing by aligning F FA/FD coefficients in the wavelet domain.
Decomposition Level. To determine the optimal recursive level for Wavelet decomposition, we systematically visualized the impact of different levels on key features and calculated the resulting energy retention rate, as shown in Fig. II Qualitative analysis indicated that the decomposition level significantly affects feature representation. For instance, at Level 1, the low- fwe.ueeyy com- ponent effectively captures the image's 's overall structure, and the ggghffrry component retains
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clear details. However, as the decomposition level increases (e.g., Level 6), the wow-frequecyy in- formation becomes overly coarse due to repeated downsampling. failing to carry meaningful visual patterns; concurrentyy, the resolution of the high-frequee frequenry components decreases significantly, de- grading their ability to represent fine details. Using Parseval's theorem, we performed a quantitative assessment via the energy retention rate, which demonstrated that Level 2 decomposition achieves the optimal balance between structural and detailed information while preserving approximately 85% of the original feature energy. In contrast, Level decomposition might introduce noise due to information redundancy, while excessive levels (such as 2 4) compromise editing quality due to excessive information loss. Based on these qualitative observations and quantitative calculations, we ultimately selected Level 2 decomposition as the core parameter for the W-Edit framework. This choice ensures that the model can fully utilize the low-frequency components to maintain structural consistency and leverage high-quality high-frequency components for detail synthesis in subsequent editing, thus balancing visual fidelity with computational efficiency.
Layer Comparsion. Tab. 6 compares the layer selections and performance metrics between Stable-Flow (based on DINOV2 structural saliency) and our frequency energy-based method. The layers [0. 1, 2, 17, 18, 28, 53, 54, 56] were evaluated using two key metrics: Jaccard Similarity Coef- ficient (0.9) indicates high overlap in layer set composition, demonstrating that our frequency-driver selection aligns closely with Stable-Flow's structural importance criteria. Spearman Rank Correla- tion Coefficient (= 0.925) quantifies the agreement in importance rankings derived from ablation studies on edit quality (CLIPimg Score). This strong correlation validates that frequency energy response effectively captures structural significance, supporting our method as a robust alternative to DINOv2- approaches. The reduction percentages further illustrate comparable performance in stability metrics across methods.
Block-Wiss Frequency Progression. We utilized a statistically significant subset of 64 unique, high-diversity text prompts (covering concepts like structure, texture, and complex scenes) as input. For each block and its feature map Fi. we calculated the following normalized energy ratios via Fourier Transform. Represents the percentage of total spectral energy contained within the low- frequency radius (rlow 0.25 Rmaz), directly quantifying the block's 's anchoring of structural information.Rppea
the percentage of total spectral energy contained within the siggffrruynncy radius (the outermost ring, ranging from 0.75 x Rmaz Rmmaz), directly quantifying the block's refinement of details and texture.
We initially calculated the total energy (Etotat) of the feature map and obtained Ejow and Enigh by summing the spectral energy values within their respective frequency bands. We then calculated the Le norm for all 128 generated samples and averaged the Enow and Enigh ratios relative to Ftotal for each block. Based on this quantitative data, we designed two core visualization charts to illustrate the analysis results intuitively. The Energy Progression 3D Plot uses the Training Step Index and Network Block Index as the horizontal axes and Energy Value as the vertical axis to clearly demonstrate the 3D distribution of Enow and Enigh energy across different training phases and network depths. This highlights the shift in energy dominance from low to high frequencies. Furthermore, the Macro Energy Ratio Pie Chart presents the average proportion of low- fwwffreeenccy and high-frequency energy within the total feature representation across the entire diffusion process. These quantitative results strongly confirm a highly significant shift in spectral energy dominance from early to late blocks. We have included a new progression curve figure in the Appendix, shown as Fig 12 that clearly visualizes the evolution trend the Ejow and Ehigh curves across all blocks. This systematic quantitative and statistical analysis provides the necessary and rigorous theoretical foundation for W-Edit's frequency- aware block-wise injection strategy.
Decoupling of high and low frequencies. To further validate this conclusion, we conduct a vi- sualization experiment where only specific frequency bands are injected. As shown in Fig. [8] injecting only wwwfrreuenccy components alters global structure and layout but fails to refine local details. Injecting only high-frequency components preserves the overall structure while modifying textures and fine-graine -reiner Htrrbutes.Combinii ously maintaining global consistency and enabling precise modifications, This evidence confirms that multi-level frequency control is key to balancing stability and flexibility in editing.
both yields faithful and coherent edits simultane-
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Table Comparison of selected layer.
Layer
f
R
Rgf
Rour
d²
Stable-Flow Drop (%)
Ours Drop(
28
6
0
7.2
7.5
9
0
9.5
6.2
4
0
8.5
8.8
53
7
1
6.8
6.8
0
2
9.5
9
56
8
-1
6.8
7
5
2
7.8
9.3
17
1
O
O
12.1
12.5
3
6.7
8.5
Block(0)
Block[3]
Block[7]
Block[
Block 12)
Block 15]
Block(7]
Blockd 18)
Figure 10: Frequency domain evolution pattern with block.
To verify that the separation of low-frequency structure and high-frequency details in W-Edit enables precise and faithful editing, we conducted the following experiments by injecting low-frequency information and high-frequency information separately.
We designed rule where, when generating an image based on a text prompt, if we force the preser- vation of the low-frequency components of the reference image, the generated image is compelled to retain the overall composition and macroscopic appearance of the reference image. Under this strong constraint, the text prompt can only perform subtle semantic replacements on local content, without changing the overall structure style.
Table Our method yields results for different edit types on the PIE-Bench dataset.
Categories
Structure Distance
PSNR
LPIPS
MSE
SSIM
CLIPsim
CLIP
random
0.043
20.627
0.170
0.014
0.804
24.891
31.563
obj
0.041
19.101
0.181
0.015
0.780
24.729
32.123
add
0.014
24.612
0.082
0.004
0.918
24.499
31.587
delete
0,015
24.614
0,081
0.005
0.887
24.499
30.997
content
0.014
25.772
0.084
0.004
0.917
24.397
31.665
pose
0.024
24.310
0.104
0.007
0.881
26.297
32.823
change color
0.020
23.956
0.096
0.007
0.903
24,888
32.659
material
0.012
25.997
0.075
0.003
0.936
24.268
31.762
background
0.014
25,463
0.069
0.004
0.927
23,888
31.742
style
0.013
26.167
0.085
0.011
0.913
23,955
31.480
19

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Published as a conference paper at ICLR 2026
(ook Low Freeh
Ielt Fich Droe)
loA Law Deee
ei High Proq
fol Low Feer
di Fich Froel
fok Low Feres)
(ei High Ficep)
(VV HighTFesp Freg
(cD Pagg
(dV Foes
(cD High Paog)
(cV Tigh
(cD High Prrgg
(eV Hagh
(cD High
Level:
Level:
Leval:
Level: 6
Figure 11: Results features at different decomposition levels.
Spectral Energy Analysts: Block Levell Energy Distribution and Ratie
Prcsemno High
Obintaticn tluc Fittao,
Ovaell Enery tuto Diseitutionn
(a) SingleStreamBlolks
Block-Lavel Distribution Overall Ratlo
Lown wwo--reeenyyyyo Trequnccy Energy Dientbution Each roslam
Overall Energy Dansbbriin
(a) DoubleStreamBloks
Figure 12: Block-Wiss Frequency Progression.
The high-frequency components encode the fine details, textures, outlines, and edges of the objects in the image. By preserving the nigh-frequency components of the reference image, we are able to precisely lock the geometric shape and contour structure of the object.
Therefore, shown in Fig. 20 In the upper row, text prompts such as "castle" or "factory" suc- cessfully capture local features, like the style of the seaside house, but the image's style, overall background, and layout are still preserved and controlled by the reference image's ' low-frequenc, components. In the lower row, when injecting high-frequency information and using natural land- scape (mountain range) as the reference image, the generated image clearly retains the mountain's contour, while its style and texture be significantly changed according to the text prompt (e.g., "night." " "spring," or "desert"). This substantially improves the precision and creativity local modifications.
Consequently, this verifies that the low-frequency components capture the global structure, macro- scopic layout, main color tones, lighting, and artistic style of the image. The high-frequency compo- nents encode the fine details, textures, outlines, and edges the objects in the image. Furthermore, validates that our method successfully achieves the separation of low-frequency and high-frequency information, significantly enhancing the precision and flexibility of image editing and providing a foundation for achieving excellent editing results.
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Published as a conference paper at ICLR 2026
"...table with a vase of flowers
"...bookcase filled with books"
"...park with a duck pond"
....utset with silhouetted birds"
b...bed with stuffed animal"
"...cake with birthday candles"
s...street with people holding umbrellas"
river with bridge"
"...dor with wreath"
Figure 13:
Object addition results. W-Edit accurately adds objects specified in the text prompt while preserving the original image structure. For instance, it successfully adds vase on the table, books on the shelf, and candles to the birthday cake, demonstrating precise spatial understanding and minimal disruption to existing content.
"..fieeld with trees and-reeks"
"...room with a sofa and-chairs"
"...park with trees and-benches"
"Restaurant with lables "nd-waiters"
" "...park with pond and
be.ch with umbrella and towell
"Library with bookshelves andd-tabley"
"...forest
sigmpost"
"Museum exhibits and visiters
Figure 14: Object removal results. W-Edit effectively removes target objects while maintaining scene coherence. Examples include removing rocks from the field, eliminating the chair from the room, and clearing benches from the park. The edited regions show natural inpainting that seam- lessly blends with the background.
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Published as a conference paper at ICLR 2026
"Chocolate Vanilla cake
"Breguette Loaf sandwich bread"
"A Monder classical fountain" foonstcal.fuutairr
"A city school "M
"A pickap delivery truck" "
"A ballpoint fountain pen
"A Stamese Maine Coon cat "
"A plastic paper hag"
"A plastie glass bottle"
Figure 15: Object replacement results. W-Edit demonstrates strong capability in replacing objects with semantically different alternatives while preserving spatial relationships and background con- sistency. The method successfully replaces chocolate cake with vanilla cake, changes types bread, and transforms cat appearances, showcasing its flexibility in handling diverse replacement tasks.
"A tree in summer autum"
"A dog is walling running
"Al cat is slanding sitting" "
AA man standing hands pockets" T
"Cloth still moving in wind
cat with eyes spea half-closed
"A woman walking gracefully quickly"
"A ball-ff flattened dough"
smiling slightly laughing "
Figure 16: Non-rigid editing results. W-Edit demonstrates remarkable capability in handling com- plex non-rigid transformations while presenting structural integrity. Our method successfully mod- ifies facial expressions, adjusts body postures, and alters animal poses, showcasing its ability to manage deformable objects without introducing artifacts or compromising the original image com- position.
B REPRODUCIBILITY STATEMENT
Implementation details, evaluation protocols, and dataset descriptions are provided in the main text and appendix. Complete proofs are also included in the main text. The full source code will be released upon acceptance.
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new book
cotton silk shirt"
blue
wishaven
"A Naste girl elderly woman"
"Clean Dilapidated corridors
"An emptyyfflll parking lot"
"A town ruraal street"
"A river in daylight night"
"A mountain in winter"
"A living room kitchen
"Meadow wildflowers grass"
Attribute modification and scene editing. W-Edit does well in fine mhe-grained attribute control and comprehensive scene transformation. For attribute modification, it precisely adjusts ob- ject properties such as color, material, and texture. For background editing, our method seamlessly replaces entire environments while maintaining consistency of foreground objects.
Figure 17:
Real Image
Without injection
Low-frequency injection
More Low- frequency injection
High and Low frequency injection
Figure 18: The impact of injecting specific frequency band on the image.
C
THE USE OF LLM
Throughout the preparation of this paper, we employed a large language model (LLM) to improve the writing and correct grammatical errors. The LLM was also used as an experimental tool to generate certain prompts for testing purposes.
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Published as a conference paper at ICLR 2026
Reference
FLUX.1 Kontextt
W: Eddi
Figure 19: Comparison with FLUX.1 Kontext.
Reference
Triumphal arch
Notra Dame
Purthenon Temple Eifidl Tower
Reference
Chelle
Ancient Ruins
Factories
Drilling Plarfomm
Street
Ancient
Hagle
Dog
Reférence
Elder Man
Elider Ladly
Robor
Clown
Refermnce
Dear
Factories
Pyramid
London
Reforence
Deseri
Night
Spring
Rivar night
Reference
Winter
Spring
Autumn
Sunny
Reforence
Monet
style
Gogh
skutch
Reference
color
Pen skench
Autumn
Sunny
Refenence
Water
Night
Autumn
Figure 20: Qualitative Results of Different Frequency Band Replacement Methods. For Low- Frequency Band Replacement, both the appearance and layout of the generated image controlled by the reference image; for high-FFreuency Band Replacement, the reference image controls the contours of the generated image (which is better observed upon magnification).
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Random
Random
Random
Random
A dd
A dd
Change content
Change content
Change pose
Change pose
Change background
Chenge background
Change material
Change material
Change color
Change color
Delete
Delete
Change style
Change style
Figure 21: Visualization results of W-Edit for different editing types on PIE-Bench.
25


Reviews:
[{"review_id": "Rij1wQ8D7h", "reviewer": "Reviewer_1U8u", "summary": "This paper proposes W-Edit, a training-free framework for text-guided image editing that operates in the frequency domain. The method uses wavelet transform to decompose diffusion model features into multi-scale frequency bands, aiming to separate low-frequency structural components from high-frequency details. By selectively injecting different frequency bands into a pre-trained diffusion model and using an inversion-based modulation strategy, W-Edit seeks to preserve the original image’s structure while applying localized edits in line with a target text prompt. The authors present qualitative results on various editing tasks (e.g. object addition/removal, attribute change, style transfer). While the approach addresses an important problem and shows some promising results, I have serious concerns regarding its novelty, theoretical justification, and evaluation rigor, which lead me to recommend rejection.", "questions": "The method largely repackages known frequency-domain techniques without clear innovation, and it doesn’t provide the necessary analysis or rigorous experiments to convincingly demonstrate its supposed advantages. The authors are encouraged to address the above weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Training-Free Framework: W-Edit does not require any fine-tuning of the diffusion model, instead introducing a lightweight wavelet-band injection module and an inversion-based adjustment strategy. This plug-and-play nature is a practical strength – it means the method can be applied on top of existing diffusion models with minimal overhead. Suggestion: Quantify this practical advantage. For example, report the runtime or memory usage compared to training-based methods, or discuss how easy it is to integrate W-Edit into various diffusion backbones. This would underscore the efficiency benefits of being training-free.\n\nEmpirical Quality Improvements: The proposed method demonstrates improved fidelity and consistency of edits in the reported results. Quantitatively, W-Edit achieves better scores than the compared baselines on automatic metrics like FID (image realism), CLIPScore (text-image alignment), PSNR/LPIPS (content preservation).", "weaknesses": "Limited Novelty Over Prior Work: The core idea of frequency-based feature modulation is not truly novel. Prior works [1][2][3] have already explored incorporating frequency domain information in diffusion models, image editing and image tokenizers. W-Edit largely recycles these ideas (wavelet transforms, frequency separation) without a clear new conceptual insight beyond combining them in one pipeline. The submission’s claimed contributions – wavelet-guided decomposition, frequency-band injection – are incremental extensions or amalgamations of known techniques. \nSuggestion: To address this, the authors must more explicitly distinguish W-Edit from existing frequency-domain methods. Clearly articulate what is fundamentally new (if anything). Without this, the paper fails to establish a compelling novelty claim.\n\n[1] Phung H, Dao Q, Tran A. Wavelet diffusion models are fast and scalable image generators[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 10199-10208.\n[2] Esteves C, Suhail M, Makadia A. Spectral image tokenizer[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025: 17181-17190.\n[3] Si C, Huang Z, Jiang Y, et al. Freeu: Free lunch in diffusion u-net[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 4733-4743.\n\nNo Theoretical Justification or Analysis: The paper provides no principled explanation for why the proposed frequency-band injection and modulation should preserve semantics or improve edit controllability. There are no theoretical derivations, formal proofs, or analytical measures in the main text or appendix to support this claim. Key questions – e.g., why does separating low-frequency structure from high-frequency detail lead to more localized or faithful edits? – are never addressed beyond intuition. The method is essentially heuristic, relying on empirical tuning. Suggestion: The authors should strengthen the paper with some form of theory or at least analytical insight.\n\nOverall, I recommend rejection of this paper in its current form. While W-Edit addresses an important problem and shows some empirical promise, the weak novelty, lack of theoretical grounding significantly undermine the contribution.", "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": "AYymTAlugE", "reviewer": "Reviewer_Cva5", "summary": "This work analyzes the feature representations of MM-DiT blocks from a frequency-domain perspective and study the frequency progression of different layers. With such observation, the paper proposed to first decompose the imtermediate features (features from attention blocks) recursively along the inversion path and then perform feature injection to the generation process based on energy thresholds.", "questions": "Please refer to the weakness above.\n\n1. And since the quantitative experiment mainly provides the result from PIE benchmark, qualitative results on PIE benchmark of each types should be provided, or rather, some of them should be included.\n\n2. Considering the performance of qualitative results provided in Fig.12, could the author also provide the scores of different editing types for the proposed method and compared methods?\n\nRating could be increased if the questions are well addressed, otherwise could be decreased.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper gives analysis to the block-wise frequency evolution on both the generation process and the MM-DIT block order, which form a solid basis for later applying control to the attention blocks based on frequency energy threshold. The motivation is clear and the framework is coherent.\n\n2. The proposed method is based on a general frequency evolution principle of diffusion process, which is shared among different settings of diffusion process including Flow-based, VE, VP based on different model structures, thus could be applied to diffusion models with different architechture, only that the behavior of each block should be analyzed concretely and the hyperparameters are different.\n\n3. The proposed method is vasertile to multiple editing tasks and get good performance on the PIE benchmark.", "weaknesses": "1. Several crucial implementation details of the proposed framework remain unclear:  (a) The number of recursive decompositions applied to the intermediate features is not specified. (b) The explicit energy threshold ratio, $\\eta$, which determines whether the referenced feature components are injected, is not reported. (c) The precise value of $r_{\\text{mid}}$, which defines the mid-frequency energy, is also omitted.\n\n2. The methodology section provides no analysis or rationale for the chosen hyperparameters, and the ablation study includes no experiments evaluating the sensitivity or impact of these key hyperparameters.\n\n3. The underlying intuition behind using Equation (9) for selecting referenced components requires further justification and theoretical or empirical support.\n\n4. Regarding the non-rigid editing task, the qualitative results presented in Fig.12 do not align with the authors’ claim that “W-Edit demonstrates remarkable capability in handling complex non-rigid transformations while preserving structural integrity.” The reviewer notes that all examples in Figure 12 feature simple, homogeneous backgrounds that are distinct from the main editing target, which would generally be considered easy cases for non-rigid editing. Moreover, both the object identity and background consistency are not well preserved in several of the displayed results.", "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": "d3SmwYu4lm", "reviewer": "Reviewer_KygT", "summary": "To address the dilemma in which existing text-driven image editing methods either sacrifice structural fidelity for flexible manipulation or demand expensive fine-tuning of large-scale models, the paper introduces W-Edit, a training-free framework for text-driven image editing based on wavelet-based frequency-aware feature decomposition. W-Edit employs wavelet transforms to decompose diffusion features into multi-scale frequency bands, disentangling structural anchors from editable details. The method establishes frequency-based modulation as both a sound and efficient solution for controllable image editing.", "questions": "- Regarding the selection of the hyper-parameter η, could the authors supply a more systematic sensitivity analysis and, ideally, a data-adaptive scheduling protocol that eliminates manual tuning?\n- Although quantitative metrics furnish unambiguous experimental indicators, user perception remains an indispensable dimension in image editing. It is recommended that the user-study component be expanded to corroborate the paper’s conclusions more convincingly.\n- The theoretical premise that “early blocks of Diffusion Transformers predominantly encode low-frequency structure whereas later blocks specialize in high-frequency refinement” underpins the entire subsequent development of the proposed method. To elevate this claim from an empirical observation to a rigorous theoretical basis, we recommend supplying more compelling evidence. For example, systematically quantifying the frequency bias of each block via spectrally-decomposed energy statistics, reporting statistical significance tests across multiple models and data subsets, and visualizing the block-wise frequency response curves—so that the asserted frequency progression is firmly corroborated before being leveraged as the foundation of the editing framework.\n- As the paper touches on several theoretical aspects, it is advice to clarify details and supplying intuitive diagrams would reduce the cognitive load on readers and improve overall accessibility.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper presents a novel frequency-domain perspective for text-driven image editing. Unlike existing methods that primarily manipulate features in the spatial domain, W-Edit integrates wavelet transforms into the feature decomposition of Diffusion Transformers. This frequency-aware modulation framework introduces a new paradigm for disentangling editable attributes, offering enhanced control and consistency in image editing tasks.\n- The paper is theoretically well-grounded. By systematically analyzing the internal architecture of Diffusion Transformers, it reveals that early blocks predominantly focus on low-frequency structures, while later blocks refine high frequency details. This observation motivates the introduction of wavelet decomposition to recalibrate and enhance existing image-editing paradigms.\n- W-Edit is a training-free framework characterized by lightweight module design; it can be directly applied to off-the-shelf diffusion models without incurring the prohibitive cost of full model retraining, yet still delivers editing results of high fidelity.", "weaknesses": "- The paper offers no systematic guidance for setting the hyper-parameter η (the energy threshold). Although the paper emphasizes that the selection of η is critical, it neither conducts a sensitivity analysis nor devises an adaptive scheduling scheme, potentially compromising the method’s generalizability across diverse datasets.\n- The limited sample size of the subjective assessment may undermine the statistical robustness of the conclusions. While the experimental section offers extensive quantitative and qualitative evaluations, the user study recruited only five participants—an insufficient number to yield statistically reliable inferences.", "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": "jAlcDqqmbA", "reviewer": "Reviewer_FsUB", "summary": "This paper proposes W-Edit, a DiT(FLUX)-based image editing model using wavelet-based frequency-aware feature decomposition. The authors point out that entanglement of global semantics and local signals creates difficulty in preserving structure and making precise modifications. Typically, low-frequency components in features encode layout and semantics, while high-frequency components capture texture and detail. They analyze DiT features using wavelet decomposition and show that early blocks focus on low-frequency and later blocks on high-frequency information. Based on this, they propose energy-aware feature fusion: replacing DiT features by taking wavelet components from the inversion branch and editing branch, respectively. Experiments show that W-Edit achieves strong performance in training-free image editing, preserving structure, prompt-aligned edits with minimal computational overhead.", "questions": "- In Figure 4, regarding the frequency progression analysis, the results for FLUX’s SingleStreamBlock show that only at block index = 30 does the high-frequency component increase significantly. By contrast, in DoubleStreamBlocks, the later blocks tend to have higher high-frequency components. Why do you think, in the case of the SingleStreamBlock, the high-frequency response appears only at block index = 30?\n- Comparison with other models: How does Flux 1, Kontext, and Qwen-Image perform by comparison? From my understanding, those two models (Kontext, Qwen-Image) achieve good textual alignment and can selectively preserve or modify structure as needed, even without explicit frequency manipulation. Is this consistent with your analysis? How would you compare these models in the context of frequency-aware editing and structure preservation?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper addresses the issue of semantic entanglement between global structure and local details in image editing, which improves the understanding of existing model limitations.\n- It presents a wavelet-based frequency decomposition approach to analyze frequency characteristics in DiT feature. Specifically, similar to UNet, they experimentally show that early blocks focus on low frequency and later blocks on high frequency details, which seems to give useful insight to the community.\n- The method works in a training-free manner, making it computationally efficient and easy to apply to various pre-trained diffusion models.", "weaknesses": "- This paper aims to disentangle the preservation of object structure (low-frequency) from detailed texture (high-frequency) during editing. However, it remains unclear how effectively the method actually disentangles these components. The current energy-aware adaptive frequency fusion is designed to take low-frequency information from the inversion trajectory and high-frequency details from the editing trajectory. This works well when the goal is to preserve the original structure while changing the details. But what about cases where the structure should change but the details are retained (e.g., non-rigid edit, pose change, or view change without altering the object itself)? In such cases, shouldn’t high-frequency detail information be taken from the inversion trajectory, rather than low-frequency? Overall, I think the adaptive frequency fusion method should be more precisely designed depending on the editing type.\n- Fig. 4’s frequency progression analysis seems dependent on the input image and prompt. For example, editing tasks with significant changes should yield different frequency profiles compared to minimal edits. If frequency analysis were performed across various editing cases, the claim would be much stronger.\n- In Fig. 4, in the frequency analysis for “FLUX”, the axis labels are not clearly visible.\n- For Eq. 4, the explanation for r_mid is insufficient. Why is only mid-frequency energy calculated, and not the total energy? The actual value of r_mid used in the experiments is also missing.\n- For Eq. 9, it’s unclear what value was used for the threshold η, and how it is determined during experiments. I believe this threshold should be adaptively chosen according to the editing type for better results.\n- There are some missing citations for image editing methods utilizing frequency analysis, such as “FlexiEdit: frequency-aware latent refinement for enhanced non-rigid editing” (ECCV 2024) and “FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing” (CVPR 2025).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5VDRjoVvhh", "reviewer": "Reviewer_psXC", "summary": "This paper presents W-Edit, a training-free text-driven image editing framework using wavelet-based frequency decomposition. It separates structural (low-frequency) and detail (high-frequency) features, enabling controllable edits without retraining diffusion models. By integrating frequency modulation into attention and inversion processes, W-Edit achieves high-quality, structure-preserving edits. Experiments show superior fidelity and realism compared to prior training-free methods.", "questions": "1. In the block-wise frequency analysis shown in Figure 4, why do the high-frequency components in the SingleStreamBlocks appear only in a specific region?\n\n2. In Figure 5, it would be helpful to include comparisons with existing methods for a variety of edits, such as object removal or attribute modification.\n\n3. Stable-Flow emphasizes that injecting features only into the Vital Layers is key for image editing in the DiT architecture. It would be informative to provide quantitative measures showing how well the layers selected in this study align with the Vital Layers identified in Stable-Flow.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "W-Edit introduces a novel training-free framework for text-driven image editing that eliminates the need for costly retraining, making it efficient and practical for real-world applications. By leveraging wavelet-based frequency decomposition, it effectively disentangles global structural features from fine details, enabling precise edits while preserving the overall layout and coherence of the reference image. The method integrates seamlessly with pretrained diffusion models and demonstrates strong generalization across multiple architectures, achieving superior visual quality, realism, and alignment with textual instructions in both quantitative benchmarks and user studies.", "weaknesses": "1. The experimental setup for the block-wise frequency analysis of Diffusion Transformers is not clearly provided. Which dataset was used for this analysis? If it was conducted using only the image in Figure 1, can the results be considered generalizable?\n\n2. In Figure 4, there is insufficient interpretation as to why the high-frequency components in the SingleStreamBlocks appear only in a specific region.\n\n3. It is unclear whether the roles of early and later blocks in Diffusion Transformers are directly comparable to those in U-Net, where early blocks encode structural information and later blocks encode fine details. More explanation is needed on whether this functional separation holds consistently in DiTs.", "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)