FSI-Edit: Frequency and Stochasticity Injection for Flexible Diffusion-Based Image Editing
Authors: Kaixiang Yang
Xin Li
Yuxi Li
Qiang Li
Zhiwei Wang
Venue: NeurIPS 2025 poster ()
Keywords: Image Editing
Tuning-free
Non-rigid Editing
Primary area: deep_learning
TL;DR: FSI-Edit is a tuning-free framework that injects stochastic noise and fuses high-frequency residuals to bridge semantic gaps and preserve background, enabling high-quality rigid and non-rigid image editing.

Abstract: Latent Diffusion-based Text-to-Image (T2I) is a free image editing tool that typically reverses an image into noise, reconstructs it using its original text prompt, and then generates an edited version under a new target prompt. To preserve unaltered image content, features from the reconstruction are directly injected to replace selected features in the generation.
However, this direct replacement often leads to feature incompatibility, compromising editing fidelity and limiting creative flexibility, particularly for non-rigid edits (\emph{e.g.}, structural or pose changes).
In this paper, we aim to address these limitations by proposing \textbf{FSI-Edit}, a novel framework using frequency- and stochasticity-based feature injection for flexible image editing.
First, FSI-Edit enhances feature consistency by injecting \emph{high-frequency} components of reconstruction features into generation features, mitigating incompatibility while preserving the editing ability for major structures encoded in low-frequency information.
Second, it introduces controlled \emph{noise} into the replaced reconstruction features, expanding the generative space to enable diverse non-rigid edits beyond the original image’s constraints.
Experiments on non-rigid edits, \emph{e.g.}, addition, deletion, and pose manipulation, demonstrate that FSI-Edit outperforms existing baselines in target alignment, semantic fidelity and visual quality. Our work highlights the critical roles of frequency-aware design and stochasticity in overcoming rigidity in diffusion-based editing.

ESI-Edit: Frequency and Stochasticity Injection for Flexible Diffusion-Based Image Editing
Kaixiang Yang", Xin Lit, Yuxi Lit, Qiang Li, Zhiwei Wang"
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology :; Co-first authors, *: Corresponding author. fkxyang, lixin2023, liyuxi9, liqiang8, zwwang) edu.
Non-Rigid Editing
Rigid Editing
add car
sitting
jumping
meerkat
fion
dog
monkey
bird
onc
painting
photo
red
pink
turn to kiss
remove flower
Diffusion-FLOW
yovigg
old
Our method is capable of handling non-rigid editing, including pose changes, object addition, and removal, well rigid editing.
Figure 1:
Abstract
Latent Diffusion-basee Text-to- Image (T2I) is free image editing tool that typi- cally reverses an image into noise, reconstructs it using its original text prompt, and then generates edited version under target prompt. To preserve unaltered image content, features from the reconstruction are directly injected to replace selected features in the generation. However, this direct replacement often leads feature incompatibility, compromising editing fidelity and limiting creative flexibil- ity, particularly for non-rigid edits (e.g., structural pose changes). In this paper, we aim to address these limitations by proposing FSI-Edit, a novel framework using frequency- and stochasticity-base feature injection for flexible image editing. First, FSI-Edit enhances feature consistency by injecting high-frequency compo- nents of reconstruction features into generation features, mitigating incompatibility while preserving the editing ability for major structures encoded in low-frequency information. Second, introduces controlled noise into the replaced reconstruction features, expanding the generative space to enable diverse non-rigid edits beyond the original image's constraints. Experiments on non-rigid edits, e.g., addition, deletion, and pose manipulation, demonstrate that FSI-Edit outperforms existing baselines in target alignment, semantic fidelity and visual quality, Our work high- lights the critical roles of frequency-awar design and stochasticity in overcoming rigidity diffusion-based editing.
39th Conference on Neural Information Processing Systems (NeurIPS 2025).

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Source Reconstraction
A sitting dag
iig dag
sitting dag
Inversed
Inversed
Inversed
HiighePas
DK(O.1)
Target Generation
N, joupping dog
Lnversed.ag
jumping dog
At jumpping dog
(8) T21
(b) Existing Image Editing
(c) Ours
reconstruction; Bottom: target generation. (a) Pure T21: there is no interaction between source and target images, results are random and unrelated the source. (b) Existing Image Editing: typical editing methods directly inject features, causing semantic gaps and low flexibility, especially for non-rigid edits. (c) Ours: injects only high-frequency residuals and adds stochasticity, reducing semantic gap and improving edit quality.
Figure 2: Editing Paradigm Comparison. Top:
Introduction
Diffusion models [T] have achieved remarkable success in the domain of Text-to-Image (T2D) generation recent advances [2 In particular, Latent Diffusion Models (LDMs) demonstrate exceptional ability to translate textual descriptions (i.e., prompts) into high-quality images, leading to their widespread adoption in downstream applications such as image g 9 and video editing [10] 11].
Image editing refers to the task of transforming source image into desired target image guided by user seer-provided prompts. This technology has become an integral part of daily life, with broad applications across domains such social media and visual effects.
The typical workflow LDM--bsed image editing follows common paradigm. First, the source image is inverted back into latent noise representation, typically through DDIM inversion [12] or more advanced strategies [3]. From this latent noise, two parallel denoising processes are initiated: one reconstructs the original image conditioned on the source prompt, while the other generates a modified version guided by a new target prompt. key challenge is balancing the preservation of the original content with the incorporation of desired semantic changes, which is typically achieved via information replacement from the reconstruction stream to the generation stream.
simple way is partially blending the latent noise of the source image with random noise at beginning of the generation process, often in the frequency domain [16]. Recent methods [8] 17 20 have further explored intermediate feature-level replacement, as shown in Figure Zp. They focus cross-attention
primarily manipulating query, key, and value features within the self-attention layers, enabling more precise and controllable editing. Specifically, during target generation, selected attention layers are identified, and subset of their features (e.g., query, key, or value) is directly replaced with corresponding features from the reconstruction stream. This provides visual guidance for preserving image regions that are not intended to be edited.
While existing methods perform well in rigid editing tasks, non-rigid editing such as object addition, removal, or significant pose alteration often requires substantial modifications not only to the target object but also to surrounding context. In such cases, the editing region naturally extends beyond the object itself, and the direct feature replacement widely-employed in most methods faces two major limitations. First, a semantic gap exists between the reconstruction and generation processes. Injecting attention features from the reconstruction branch often does not faithfully preserve the original content and can introduce artifacts or structural distortions, especially when editing involves significant pose viewpoint changes. Second, non-rigid edits require fully leveraging the generative capacity of the base model. Semantic guidance alone is often insufficient to overcome the strong inductive bias of the original image, which restricts the model's ability perform flexible and meaningful content transformations. As a result, existing approaches struggle to achieve both content fidelity and effective structural reshaping in non-rigid editing tasks.
To address these issues, we propose FSI-Edit, a novel tuning-fee framework that enables more flexible and effective image editing through frequency- and stochasticity-bras based feature injection, as shown in Figure 2 To bridge the semantic gap between the reconstruction and generation features, we introduce a frequency residual fusion mechanism. It allows FSI-Edit to selectively

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inject only the high-freen -egh-freueeney components from reconstruction features into the generation stream. This preserves fine- aneegraaned textures from the original image while avoiding interference from low- frequency structural information, thereby supporting more expressive non-rigid edits. Furthermore, to fully activate the generative capacity of the base diffusion model, we incorporate stochastic noise injection during generation. This controlled perturbation enriches the latent space and empowers the model to perform substantial structural changes while maintaining semantic alignment.
We summarize our contributions as follows:
1) We identify and address two limitations of most current image editing methods for non-rigid edits, egg., semantic inconsistency between reconstruction and generation features, and insufficient generative flexibility due to strong image priors.
2) We propose FSI-Edit, featuring new frequency residual fusion module that selectively transfers feghfrreuency details for more accurate feature alignment, and a stochastic noise injection strategy that expands the generation space to enable more precise and flexible structural transformations.
3) We conduct extensive experiments PIE-Bench benchmark, and the comparison results demonstrate that FSI-Edit significantly outperforms existing methods on both rigid and non-rigid editing tasks, confirming its effectiveness and generalizability across diverse editing scenarios.
2 Related Works
2.1 Image Editing with Feature Replacement
The field of text-to-image (T21) generation has advanced rapidly in recent years, driven by powerful models such as DALL-E 21 3 222, Imagen [4], Stable Diffusion [5], and Diffusion Transformer (DiT) (7]. These models have enabled a wide range of upplications, among which image editing has emerged as prominent research direction. Early editing approaches [17 26) typically rely on fine-tuning the generative model on the source image using editing instructions. However, such methods often suffer from limited generalization and require additional training overhead.
To improve flexibility and generalization, line of tuning-free methods has been proposed. These approaches commonly exploit feature interaction between the source image reconstruction and the target image generation. For example, Compt-to-Prompp (P2P) (8) introduces cross-attention feature replacement to modify image content according to textual prompts while preserving background fidelity. Plug-and-Play (PnP) [9] further explores the effects of feature replacement at various layers, including residual and attention blocks. While effective for rigid edits, these methods struggle with non-rigid transformations such as pose or structure changes. MasaCtrl [19] improves on this by using attention based masks to localize and control editing regions, enabling more flexible manipulation within the self-attention mechanism. In parallel, the Transformer-based DiT architecture 7 27 has opened new opportunities for editing 28 Methods like FT-Edit 30 RF-Edit [31] and
Fireflow 32 focus on improving inversion and editing precision. DCEdit localization guide prompt-consisten modifications.
33 utilizes attention-
Despite their progress, most of these approaches rely on direct feature replacement from the source branch, which can introduce semantic mismatches, artifacts, structural distortions, especially in non-rigid edits involving significant content change. Furthermore, the reused features often earry strong prior from the source image, limiting the model's generative flexibility and editing expressiveness. To overcome these limitations, we draw inspiration from rreuuencydooman analysis. Rather than injecting full features, selectively transfer only high-frequency residuals from the source, preserving fine-grained textures while minimizing semantic conflicts. Additionally, we introduce stochastic noise injection during generation to expand the latent space, improving the model's s adaptability to diverse and complex edits.
2.2 Image Editing with Latent Frequency Processing
Recent works have begun exploring frequency-domain operations within the latent space of diffusion models to facilitate more controllable image editing. FlexiEdit (16] suppresses nigh-frequency components in the DDIM latent corresponding to editable regions, employs re-inversion mechanism,

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Inversion
DDIM Rectified Flow Inversion
FFT
Xy ITN
(tyt)
Ey
Target Generation Source Reconstructio
womal siftieg of tablec with coll phane
FRF(u, M)
Cross Altention
Seif Altenttoo
(STC)
H
Kram
2--1
(b) Frequency Residual Fusion (FRF)
FRE
FRE
Ski
SNI
-W -.
SNI
SNI
opont
ppper
Croes Altention
Self-Aitention
Qur
tar
sitt tuble Betteresil pAizng
overview FSI-Edit
(c) Stochastic Noise Injection (SNI)
Figure 3: Overview of FSI-Edit, (a) The full pipeline of FSI-Edit, which consists of three core modules applied sequence: Frequency Residual Fusion (FRF), Stochastic Noise Injection (SNI), and Inversion Trajectory Navigation (ITN). (b) The structure of FRE, which injects high-frequency source features with low-frequency target features within the self-atte atffeattentonn blocks, (c) The SNI module, which injects controlled noise to enhance generative diversity and support non -e---iiid edits.
and combines attention feature replacement support non-rigid edits. FreeDiff 34 performs
staged requency-domait filtering during classifier-fre guidance (CFG) [35). focusing on attenuating high-frequency noise. However, it requires extensive manual tuning of parameters for each editing scenario. FDS [36) utilizes wavelet decomposition to adaptively select frequency bands based on the editing type, while Gao al. (37 propose a frequency-aware ControlNet module improve editing controllability.
While these approaches demonstrate the potential of frequency- based manipulation, they operate exclusively the latent feature space, limiting their ability to address semantic discrepancies between the source and target features. Additionally, their reliance on per-edit parameter tuning hampers practicality and scalability. In contrast, we introduce frequency-dome -omain processing directly at the feature level for the first time. Our method effectively bridges the semantic gap between reconstruction and generation branches without requiring model fine-tuning or laborious hyperparameter adjustment. generalizes well across both LDM and DiT backbones, offering robust and efficient solution for high-quality, flexible image editing.
Method
As shown in Figure FSI-Edit comprises three core components: (1) Frequency Residual Fusion (FRF), (2) Stochastic Noise Injection (SNI), and (3) Inversion Trajectory Navigation (ITN). It builds upon DDIM inversion, rectified flow, and classifier-F frre guidance (CFG), which are briefly summarized in the Appendix along with architectural details of both FSI-Edit-LDM. and FSI-Edit-Dit
FRF and SNI are applied during the early denoising steps. FRF operates self-attention layers by selectively fusing high-frequency components from the attention triplet features, i.e., query, key, and value, of the source and target branches. This enhances detail preservation while mitigating semantic inconsistencies. SNI is applied to both self-attention and cross-atiention layers, injecting controlled stochasticity into the attention features to promote diversity and enable more flexible, expressive edits. We denote attention features {Q, K, V.. Superscripts self and cross indicate features from self-attention and cross-attention layers, respectively, while subscripts src and tgt specify whether the features originate from the source reconstruction target generation stream. Timestep t and layer indices are omitted for clarity. Implementation details are provided in the Appendix.
3.1 Frequency Residual Fusion for Bridging Feature Diserepancy
A central challenge in image editing is reconciling the semantic and structural gap between the source and target representations. This gap becomes especially pronounced non-rigid edits, where the target must adhere to the prompt while preserving visual consistency with the source.

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To address this, we propose Frequency Residual Fusion (FRF), feature-level harmonization strategy that blends source and target attention features in the frequency domain. Our key idea is preserve the target's low-frequee wwwfrrquency components, which encode global layout and structure, while injecting only the high-frequency residuals from the source retain fine-grained texture and identity.
Let E RCxBxW denote features from the source branch and v E RCXHX from the target. We construct frequency-domain fusion Fuse via:
(I) where (-) denotes 2D Fast Fourier Transform, Ha. L. are Gaussian high-pass and low-pat filters with scaling coefficient o = 0.3, and fusion weight back to the spatial domain using inverse FFT IFFT(.), and combined residually:
Fuse Ho FFT(()) + a L. FFT(U))
set to 0.2. The fused feature is then mapped
(RF t) (FFT(FFuss
FRF is applied to the Query and Key projections the self-attention layers to harmonize attention computation:
QieL FRF ( Qight);
Keot= = FRF (Keett K
Veelt'
V.eelf
are
This selective frequency blending helps narrow the touree-target feature gap, enabling faithful yet flexible edits across diverse content while avoiding semantic drift or prompt misalignment.
3.2 Stochastic Noise Injection for Generative Flexibility
Non-rigid editing, especially when involving large structural shifts (e.g., transforming a bird into an "X" shape), requires sufficient generative flexibility, However, existing methods often underutilize this flexibility, resulting in limited editability.
Inspired by Stochastic DDIM 122, which shows that added noise improves inversion diversity, we introduce controlled stochasticity into attention features to enhance expressiveness and support structural transformation. Specifically, inject Gaussian noise into the Query and Key of the cross-attention layers, while preserving semantic consistency via source-based Value injection:
Qere = Qure + og IO, Kegees = Keroes + + (O, Verose' Verees
(4)
We further inject noise into frequency-fused Query and Key in self-attention:
Q where 7) = 0.2, 0g σ 0.1, and o 0.8 by default.
Q2e2e' + of 10, Keett" 1)- Keett' +oz
Since attention scores depend relative dot-produt magnitudes such QK injecting bounded noise into trequency-fuse fused features does not affect numerical stability.
This stochasticity allows the model to escape source constraints and adapt to diverse, complex edit patterns. For rigid edits, we anneal og. 0k, oy to and set - to revert deterministic attention for precise structure preservation.
3.3 Inversion Trajectory Navigation for Source Trajectory Refinement
Prior works often directly replace latent features during inversion, which can disrupt structural integrity and cause inconsistencies in the reconstructed source. Sections 3.1 and 3.2 we introduced frequency residual fusion and stochastic noise injection at the feature level to reduce semantic gaps and enrich generative diversity. Here, we extend these ideas to the latent space by applying a similar fusion strategy during inversion.
During source image inversion (via DDIM or Rectified Flow of latent states {2T, Zrr. .11) Then, we construct refined sequence {ër, ... #1 for source image reconstruction, where each latent ã, is obtained by blending the high-frequency components of I, with the low-frequency components of æT-1:
we obtain sequence
I IFFT FFT(2,) C. FFT(#,-))) + o -N,,
(6)
where a = 0.3, and o₂ = 1 controls the level injected noise.
The resulting (a:) ) is used solely to extract source-side features for cross-branch interactions. We initialize both source and target branches with Sr. This operation stabilizes the global structure of the source while retaining fine-grained details and introducing slight stochasticity for flexibility.
5

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Experiment
4.1 Experiment Design
Dataset and Baselines. To comprehensively evaluate method on both rigid and non-rigid editing tasks, we conduct experiments on the PIE-Bench [15] benchmark, which contains 700 image- prompt pairs across 10 diverse editing categories. For assessing non-rigid editing performance specifically,
such as object addition, deletion, and pose modification, we additionally curate a subset of 300 samples from PIE- Bench that emphasize such editing. Our comparisons include LDM-based methods (P2P 88 PnP (9) MasaCtrl 19). FlexiEdit [16]. Dife (34) and DiT-based approaches (RF-
StableFlow [40], RF-Edit [31], DCEdit 3. All models are tested using their publicly available implementations and default configurations for fair comparison.
Inv 39
Metries. To holistically assess editing performance and background preservation of different methods, we employ six complementary metrics. Structure Distance [41] measure the structural similarity be- tween edited images and original images, while PSNR, LPIPS [42), MSE and SSIM (43) collectively evaluate content preservation in unedited regions. For text-image consistency, we compute CLIP similarity 44 over both the entire image and the edited region. The dataset-provided masks used to identify the edited regions, but only during evaluation.
Implementation Details. All experiments for FSI-Edit-LDM were conducted using Latent Diffusion Model (LDM) [5] v1.5, while FSI-Edit-DiT was built upon DiT 7 v3.5-Medium. We use 50 DDIM steps for inversion, with CFG scale of 1. During generation, the target branch uses CFG scale of 7.5. The same settings are applied across both backbones. All experiments were single NVIDIA RTX 4090 GPU with 17 GB memory usage. The full editing pipeline, including inversion and generation, takes 20 seconds per image. Our code is available at hhtps ctstthub.co/kk444yy22
4.2 Comparisons Diverse Editing Types
Experimental results on the PIE-Bench summarized in Table Visual comparisons are shown in Figure 4 and Figure 5] These results are generated using our DiT-based version of FSI-Edit, additional examples and results for the LDM-based variant can be found the Appendix. Across all editing categories, our method achieves a better balance between semantic alignment and content preservation, effectively maintaining the appearance unedited regions while accurately applying edits. In contrast, existing methods often lean toward one end of this trade-off. For example, P2P tends to preserve background well but struggles with semantic modification. Our approach handles both aspects simultaneously, demonstrating stronger generalization, especially in non-rigid tasks. Among the based baselines, FSI-Edit-DiTs achieves competitive performance using only the lightweight DiT v3.5- 13.5-Medium model, running on a single RTX 4090 GPU with clear visual improvements over other approaches.
Table I: Quantitative comparisons on WIE-Bench 15 background and structure, ours attains the highest CLIP similarity score while maintaining competitive back- ground preservation.
P2P g achieves the best performance in preserving
Structure
Background Preservation
Similarity
Method
Model
Distenctxx10
PSNRT
LPIPS
MSE210
SSIM
Whole
Edited 1
P2P
11.65
84.76
25.02
PaP 9
24.29
22.46
106.06
80.45
79.68
25.41
22.62
MasaCui
h
24.70
22.64
87.94
81.09
81.33
24.38
21.35
UNet
FexxEEdit
22.13
82.62
25.15
FreeDiff A4
18:70
24.73
89.76
55.32
81.68
25.03
22.12
Ours-LDM
15.84
24.69
88.42
52.21
81.93
25.46
22.30
48.76
68.95
22.50
StableFlow
19.24
23.04
76.94
84.85
87.22
24.30
21.28
RF-Exin 31
Transformer
24.45
24.41
113.44
56,46
83.84
25.03
22.28
22.36
25.41
22.71
Ours- D
13:71
26.61
85.44
36.50
86.
25.69
22.50
Ours-DiT is built on DiT 23.5-Mediiie and runs fully on a single RTX 4090 GPU, In contrast, RF-Inv,
FlobleFlow. and RF-Edit rely on FLUX (45 or its variants, which require over 40GB of memory.

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Source Image
P2P
PoP
MasuCtrl
FreeDiff
StableFlow
Ours
old yowsgg woman is holding dog
dog raat: sittiig on wooden chair
bird
Whlii standing on top of a Wounttall
blue pyramid with reflection of wnvvrttalss ship
illustrattin of santoo lavoykingg face awary
chardh-in--te Hhe. countryside with fence and trees
Figure 4: Editing Results on Be-Bh.ch. Compared to previous methods, our approach better preserves unedited regions, maintaining high consistency with the original image while accurately reflecting the target semantics.
4.3 Comparisons on Non-Rigid Editing.
To evaluate non-rigid image editing, which involves structural changes such as addition, deletion, pose modification, we selected nearly 300 images from the PIE-Bench dataset and constructed corresponding editing prompts. P2P was excluded from the comparison as is not well-suited for non-rigid editing tasks, whereas DCEdit 33 was excluded, since its official implementation had not been released when our experiments were conducted. Quantitative results are reported in Table 2 while qualitative results visualized in Figure 5
Due to space constraints, the qualitative results of
other methods are provided in Appendix.
Our method consistently delivers high-quality non-rigid edits by accurately aligning with the target prompts while preserving the integrity of unedited regions. For example, F 3), our model successfully transforms the bird's shape into "X" form. In the fifth row, when removing the phone, our method preserves surrounding details such as the vase on the table and the watch on the left wrist. Overall, the effectiveness of our non-rigid editing benefits from the proposed FRF and SNI, which together enhance both semantic alignment and structural fidelity. This allows for precise yet flexible editing while maintaining high realism in unedited areas.
Table 2: Quantitative evaluation of non-rigid editing performance. Non-rigid editing requires substantial modifications the original image. Our method achieves the highest CLIP similarity while also maintaining strong background preservation.
Structure
Background Preservation
CLIP Similarity
Method
Model
Distoncr
PSNR
LPTPS
MSF
SSTM
Whole
Edited
PnP
19.23
22.17
115.86
87.83
77.47
25.10
21.32
MasaCut
1V
22.97
22.32
94.62
87.67
79.40
24.86
20.68
HexiEdit
UNet
16.83
299.58
FreeDiff
18.06
23.92
101.49
64.04
78.96
25.29
21.25
Ours-LDM
18.75
23.22
108.55
70.31
78.31
25.93
21.78
39
253.04
StableFlow 400
19.58
22.40
85.63
98.60
85.19
24.93
20.84
Transformer
Edit 31
25.36
23.65
128.30
64.37
81.43
25.06
21.58

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Source Image
PnP
MasaCtrl
FlexiFdit
FreeDiff
StableFlow
Ours
jumping a corgii is Sirting on white backgrooudd
fallow deer with head *oorning leack facing forwards
X
logo of biidd shape a black backgground
a plooto of a grey SwaM swimmning, with her labioo
Woman in trauch coat sittinn at a tolole wwitt well----llllpeee pkal-.
elephant the forest and Widn riding it
Figure 5: Non-rigid Editing Results. Our method strikes superior balance between semantic alignment and background preservation, resulting high-fidelity edits.
4.4 User Study
Setup. To assess perceptual editing quality, conducted user study covering both rigid and non- rigid cases. Eight methods were compared: PnP, MasaCtrl, FlexiEdit, FreeDiff. RF-Iny, StableFlow; RF-Edir, and Ours-DiT. Participants were instructed rank results based on (1) faithfulness the editing instruction and (2) preservation of unedited regions. Each user received around 20 image sets, with method order fully randomized to ensure fairness.
Results. We collected 48 valid responses, in- cluding users without prior experience in image editing. reports the ranking distribution, where the x-axis represents each method and the y-axis shows user ranking (1 = best, 8 = worst). The solid red line denotes the median, while the dashed line indicates the mean.
Methods such as RF-/nv, StableFlow, and RF-Edit show large variance, suggesting unstable perfor- mance across different editing types. PnP. Mas- aCtrl, FlexiEdit, and FreeDiff achieve moderate consistency but frequently suffer from semantic drift, as reflected by their higher median.
User
PnP
Casa
FlexiEdit
FreeDiff
RF lnv
StableFlos RF-Edit
Ours- DIT
In contrast, Ours-DiT achieves the lowest me- dian and mean ranking, with compact distri- bution, indicating strong user agreement. From a perceptual perspective, participants consistently preferred our method for its balance between in- struction alignment and source fidelity. These re- sults confirm that FSI-Edit delivers the most reli- able and visually convincing edits across diverse scenarios.
Figure 6: User study ranking distribution across eight editing methods. The x-axis lists compared ap- proaches, while the y-axis denotes user ranking best, 8 = worst). The solid red line indicates the me- dian, and the dashed ine represents the mean. Our FSI-Edit achieves the lowest median and most com- pact variance, showing strong user preference and perceptual robustness.
8

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Table 3: Ablation study on non-rigid editing dataset. w/o FRF removes frequency residual fusion module, while w/o o SNI denotes the variant without stochastic noise injection, and wyo ITN disables inversion trajectory navigation.
Structure
Background Preservation
CLIP Similarity
Method
Distancex104
PSNR1
LPIPSx10>
MSEx10 4
SSIMx100
Whote
Edited
w/o FRF
10.51
26.97
81.33
30.88
86.38
25.50
21.14
w/o SNI
16.38
26.12
114.22
37.13
83,52
25.85
21.27
w/o ITN
19.90
23.92
120.14
60.47
81.72
26.47
22.05
Ours
19.87
23.91
120.82
59.95
81.71
26.58
22.16
Table 4: Editing performance comparison of ITTN with existing reconstructiio-reefingg methods.
Structure
Background Preservation
CLIP Similarity
Method
Distance L
PSNR1
LPIPSx101 L
MSEx10
SSIM x t
Whole
Edited
DDIM
69.43
17.87
208.80
219.88
71.56
25.01
22.44
Direct
11.65
27.22
54.55
32.86
25.02
22.10
NT
13.44
27.07
59.88
35.47
84.55
24.75
21.87
Friend
11.07
26.17
58.73
38.21
84.26
25.22
22.13
ITN (Ours)
10.31
26.34
57.57
37.20
84.60
25.35
22.13
4.5
Ablation Study
Effects of Key Components. To assess the contribution of each core component in our method, we conduct ablation studies on the curated non-rigid editing subset PIE-Bench. Specifically, we evaluate the impact of three modules: Frequency Residual Fusion (FRF), Stochastic Noise Injection (SNI), and Inversion Trajectory Navigation (ITN). We consider three ablated variants of FSI-Edit-DiTT (1) w/o FRF removes frequency fusion in self-attention; (2) w/o SNI disables stochastic noise injection in attention layers; (3) w/o ITN excludes frequency fusion during the source inversion trajectory.
Quantitative results are shown in Table which show that removing either FRF or SNI significantly compromises non-rigid editing quality, leading to weaker prompt alignment and limited structural transformations. In contrast, the full model combines stochasticity to unlock the base model's generative flexibility and FRF to bridge the semantic gap between source and target branches, ultimately achieving a better balance between structural preservation and editing fidelity.
ITN vs. Reconstruction-refinnee three widely-used reconstruction-refiinng methods under the same P2P backbone: Direct Inversion (Direct) [15]. Null-text Inversion (NT) [14), and Edit-Frienddl Inversion (Friend) [46). We evaluate both editing fidelity and report the results Table 4 As shown, ITN achieves the lowest editing distance and the highest whole CLIP alignment, which confirms ITN an essential component for balancing editability and fidelity in editing tasks.
Methods. We additionally conduct a dedicated comparison against
Case Study. Figure 7 illustrates the role of each component through rigid and non-rigid editing examples. Without FRF, the model fails to achieve large-scale structural deformation (e.g., turning a bird into an "X" shape). Without SNI, new content such as earrings or additional animals is synthesized by reusing existing textures, leading semantic distortion and local artifacts. Without ITN, the model struggles to introduce entirely new objects (e.g., a car graffiti), resulting in incomplete or collapsed structures. The red circles highlight key regions where the absence of each module causes failure. Together, FRF, SNI, and ITN enable balance of structural stability, semantic fidelity, and generative diversity, crucial for high-quality image editing.
Limitations and Broader Impact
5.1 Limitation
While our method demonstrates strong performance non-rigid editing, may still cause unintended
alterations in adjacent regions during fine-grained edits. For instance, in Figure Sh, removing the glasses inadvertently changes the woman's facial expression. Additionally, as shown in Fig approach tends to be less responsive to color-specific modifications. In some cases, it may also

---

Source Image
w/o FRF
w/ FRF
Source
woo SNI
SNI Source IIN
IIN
add cort
add earrings
add graffiti
doleto dhurch
cat dog
deleto fower
X
bird
add Mouse
add cor
Figure 7: Visual Ablation of Each Module. From left to right, we illustrate the effects of removing FRE, SNI, and ITN, respectively. Without FRE, large-scale non-rigid deformation cannot be achieved, leading to rigid or incomplete edits. Without SNI ITN, the non-rigid editing quality degrades, and crucial source details may be lost. The circles highlight the critical differences between using and omitting each module.
fail to fully address all regions implied by the textual prompt, illustrated in Figure 8 In future work, we plan to incorporate mask-based spatial guidance, particularly for rigid edits, to enable more localized and accurate modifications. Furthermore, aim to design frequency fusion strategies tailored to better handle color-sensitive edits, enhancing the controllability of our method both structural and appearance-level transformations.
Source Image
Ours
Source Image
Ours
Source Image
Ours
remove glassos
orainge ->> black
(e) pink -> green
Figure 8: Examples of failed editing
5.2 Broader Impact
This work advances the understanding of attention-basee feature manipulation in diffusion models by introducing feature-level frequency fusion and stochasticity. These mechanisms enable a more effective balance between background preservation and content modification, resulting in semantically coherent and visually consistent outcomes for both rigid and non-rigid image editing tasks. Looking forward, flexible non-rigid editing with large structural transformations holds potential for practical applications such as visual effects production, where creative structural changes are common, and in surgical imaging, where it can aid in generating physiologically plausible synthetic data for training and simulation. For instance, our method could facilitate the creation of risk-aware surgical scenarios by simulating realistic anatomical deformations. editing techniques, but also paves the way for their broader application in creative and scientific domains that demand both precision and flexibility.
summary, this research not only improves image
Conclusion
In this paper, we introduced FSI-Edit, novel tuning-fies free image editing framework that is effective across both LDM and DiT backbones. FSI-Edit enables flexible and high-fidelity non-rigid editing by incorporating two key mechanisms: (1) frequency residual fusion, which injects high-frequency details from the reconstruction branch to mitigate semantic inconsistency while preserving essential textures, and (2) stochastic noise injection, which expands the generative space and facilitates diverse structural transformations. In addition, FSI-Edit applics requency-domalle fusion not only at the feature level but also along the temporal inversion trajectory. helping preserve unedited content more faithfully while enhancing edit controllability. Extensive experiments on both non-rigid and rigid editing tasks demonstrate that FSI-Edit achieves superior performance, validating its effectiveness, generality, and practical value in a wide range of editing scenarios.
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Acknowledgments and Disclosure of Funding
This work was supported in part by National Natural Science Foundation of China (Grant No.62202189), National Key R&D Program of China (Grant No. 2023YFC2414000), and research grants from Wuhan United Imaging Healthcare Surgical Technology Co., Ltd.
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A
Preliminary
In this section, we provide brief overview of the three key preliminaries underlying our tuning-free
FSI-Edit framework: DDIM Inversion [2]. Rectified Flow 22 38 and Classifier-Free Guid- ance [35].
A.1
DDIM Inversion
DDIM extends DDPM into a non-Markovian diffusion process. In the LDMs, DDIM uses the model's noise estimator Ep to sample the latent 24-1 from I₁ by:
/4-1 az) (1 at-1) at
Tt-1
Tt
eg (2 1);
Va
where 2, denotes the latent noisy features at timestep t. By reformulating this discrete update as an ordinary differential equation (ODE), we can apply Euler Integration to solve the reverse process:
21-1 + (a ar)
((1 a4-1) at
21
Eg c - 1).
(8)
/a--1
Val-1
Consequently, when inverting given source image, we obtain the DDIM Inversion trajectory {z, 17
A.2
Rectified Flow
learns straight-line transport between two distributions by combining linear interpolation with an ODE-based sampler. Given two observed distributions zi ~ Po and x ~ Pr, defines continuous trajectory:
29
38
St im + (1 (1-() () xo, I [0, 1];
and models its time-aware velocity via neural network us (x t). In practice, one discretizes time with steps {o.} and applies Euler integration:
Tt fz-1 (o. 0z-1) vo 1).
(10)
To invert given endpoint xi back toward zo, simply reverse the Euler step:
Tt-1 2₁ (a,-1 σ,) vo 1-
(11)
By iterating this from t down to t. O, we recover the Rectified Flow inversion trajectory { which maps an observed source image latent back to its original distribution.
A.3 Classifier-Fres Guidance
To better integrate conditional control into generative diffusion models, Ho al. [35] proposed Classifier-Free Guidance (CFG). CFG replaces the need for an external classifier by interpolating between conditional and unconditional noise predictions. Let denote the conditioning, the noise prediction under condition e co( c), while its unconditional counterpart is ca (2,, Ø). CFG then computes the guided noise estimate as:
Eo (0(2,, c) 2) + X(Eo(z),)) co(x.,
(12)
where À > 1 is the guidance scale.
B Implementation Details of FSI-Edi-LDM and FSI-Edit-DiT
We implement our method on top of two popular T2I backbones: Latent Diffusion Models (LDM) S v1.5 and Diffusion Transformers (DiT) 7 v3.5-Medium, leading to two variants: FSI-EditLLDM and FSI-Edit-DiT. The core components, Frequency Residual Fusion (FRF), Stochastic Noise Injection (SNI), and Inversion Trajectory Navigation (TTN), remain consistent across both variants. However, their integration is adapted to accommodate the architectural differences between backbones.
21

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B.1 FSI-Edit-LDM
Shown in Algorithm for the LDM-based version of our method, we follow the feature interaction strategy of PnP [9] and apply our proposed modules, Frequency Residual Fusion (FRF), Stochastic Noise Injection (SNI), and Inversion Trajectory Navigation (ITN), specific locations within the UNet architecture:
FRF: embedded in the feature map of the 4th decoder residual block. This is active for the first 80% of the denoising process (i.e., the first 40 timesteps).
SNI: applied to the self-attention blocks from the 4th to the 11th decoder layers of the UNet. We inject noise into the attention queries and keys (Qugt= Kege'), while directly injecting
the values (Vaels). This operation is performed during the first 50% of the denoising steps (i.e., the first timesteps).
ITN: operated over the entire denoising trajectory, performing frequency-basee blending
between successive latent states to extract source-consistenl features for cross-branch editing.
Algorithm FSI-Edit-LDM
1: Input: origin image 2n, inversion steps T, denoising model cg. source target prompts Parcs Pegt res-block and self-attention thresholds Tres and Taelf
2: Stage I: DDIM Inversion
3: for 1, ... T do
Jan 4: It va--1
a1-1 0.)
(1-04-1)0 v
co c0(x1-1.t Parc)
5: end for
6: Get the inversion trajectory (xe/E=1
7: Stage II: FSI Editing
8: 2T IINN
tar
9: for TT......,1d
10:
i, = IITN(N (t-1)
11:
free Qreit 1,r0: Veslf + co(0t, 1, Pagt)
12:
i Trgs then
13:
fiig (FRF (t fTage))
else
15:
SrOe" fres
end if
17:
ift >>
Teely then
18:
Queir" Kigg =SNI(FRF(Q1.L) KTttt))
Vaell'
V
19:
else
20:
Quair" Kigtt Vaair' Qiitg Keelt Vselr
21:
end if
22:
It-1
=u( , T.Ptgh Pigti ftege Qi.ig Keels" Vergee
23:
2 DDIM- Samppx xxarr)
end for Output: Editing image af
25:
B.2 FSI-Edit-DIT
we adopt DiT v3.5-Medium (bfloat16) as the backbone for our transformer-based implementation. FSI-Edit-DiT incorporates all three key modules with the follow- ing configurations:
As illustrated in Algorithm 2
FRF: embedded to the self-attention layers of the Oth to 12th Transformer blocks, and is active during the first 50% of the denoising timesteps.
SNI: applied to all cross-attention layers throughout the Transformer blocks, and is active for the first 50% of the denoising steps.
ITN: performed across all timesteps refine source latent representations
22

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Algorithm 2 FSI-Edit-DiT
1: Input: origin image zo. inversion steps T, velocity field vs, source target prompts Percs Pegt. cross-block and self-atlention thresholds Teross and Tself
2: Stage I: Rectified Flow Inversion
for -....... T do
4: It = It-1 + (or 01-1)00((1-1, 1, Pare)
5: end for
6: Get the inversion trajectory (ae)
7: Stage II: FSI Editing
8: IT
tar IIN
9: for To.rt= do
10:
It = -INN(T,, 11-1)
(Qi,ere Kr,ec Varoes) ) (Qi (, Vileec) <- vo(3t, Pigt)
12:
if I. > Toroge then
13:
Vi.gi Krross Vicress' t,gt
SNI K Verres)
14:
else
15:
Qeroae' Krross'
Veross' Litt t,egt
Qryge Ki
Verees
L,tge
16:
end if
17:
if
Tself then
18:
Q i,gtt
=S KT,tt) V.eer V/ellt
else
20:
Keair" end Lggt f,zt
Qu Ligtt r,gg
Kselt
21:
22:
Ii-1 vo( va(ztor L, Pegii Qiroge' Ki,gt Veross' Qrolf"
L,gg Vrole
23:
tor = RectifiedFlow (lmmpp/( ,ater)
24:
end for Output: Editing image tar
25:
C Comparison Methods and Experimental Setup
In this section, we present the experimental setup and parameter configurations for the baseline methods used in our comparisons.
C.1 LDM-Based
For P2P [8 PnP 99. and MasaCtrl [19], adopt DDIM Direct Inversion 15
as the inversion
backbone. All image editing experiments are conducted using their default parameter settings.
For FlexiEdit [16, we use the official implementation To support large-scale, consistent batch processing across methods, we follow the configuration most commonly used in the official examples by fixing the reinversion steps to t& 30. Since FlexiEdit requires 'blended word' localize the editing region, we extract this information from corresponding prompts in PIE-Bench [15]. cases where the model fails to locate the semantic region associated with the 'blended word', default to editing the entire image. All other parameters remain at their default settings.
For FrfeDiff (34, we use the official implementation and adopt the most representative configuration from the official examples, setting the time steps filter scheduling to т; (801, 781, 581) and the high-pass filter sizes to r (32,32, 10, 10). All other parameters are kept at their default values.
All LDDebased methods above implemented using the v1.4 v1.5 Stable Diffusion backbone and are executed on single NVIDIA RTX 4090 GPU with 24GB of memory.
23

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Table 5: User study ranking results (Mean Standard Deviation; lower indicates beiter perceptual quality). Supplement to Fig. i the main paper.
Method
MasaCul
FlexiEdit
FreeDiff
RF-Inv
StableFlow
RF-Edit
Ours-DiT
Ranking 2.22
4.80
2.59
4.09
2.42
4.29 2.51
+
2.66
C.2 DiT-Based
We use the official implementation of StableFlow 40 with the inversion steps 50. For RF-Inv [39], we adopt the official code with the default settings. For RF-Edit [31], we use the official repository with the guidance scale set to 2 and the injection step to 5. All other parameters remain at their default values. The above three methods are executed on a single NVIDIA A100-PCIE-80GB GPU.
D User Study Statisties
To complement Fig.6in main paper, we report the detailed statistical results of the user study, including the mean and standard deviation of ranking scores for each method. As described in the main text, each participant was asked to rank eight editing methods based on overall quality (semantic alignment and preservation). Lower scores indicate better perceived performance. Table. provides clearer view of user preference consistency and further confirm the advantage of ESI-Edit-Doy which shows both the lowest mean rank and the smallest variance.
E Extended Ablation Studies
In this section, we investigate how different parameter choices in each module our method affect editing quality and controllability. We conduct series of controlled experiments to analyze the sensitivity and contribution of key hyperparameters within the FRF. SNI, and ITN components. Specifically, we set the default values as follows: pairing distance d = for ITN, fusion weight 0.2 and Gaussian scaling coefficient 0.3 for FRF, noise ratio 1) 0.2 (corresponding to of 0.8) for SNI, and FSI-Edit intervention durations of 50% and 65% for non-rigid and rigid editing tasks, respectively.
To ensure fairness, all other parameters are kept fixed at these default values when exploring the effect of any single variable. To directly assess the influence each parameter editing behavior, all ablation studies are conducted on the non-rigid editing dataset using the FSI-Edit-DiT.
E.1 Effect of ITN Pairing Distance
Table 6: Ablation study the pairing distance d in ITN.
Structure
Background Preservation
CLIP Similarity
Distancex1wf
PSNRT
LPIPSx101 + MSE 1
SSIM
Whole
Edited 1
w/o ITN
19.90
23.92
120.14
60.47
81.72
26.47
22.05
(Ours)
19.87
23.91
120.82
59.95
81.71
26.58
22.16
19.80
23.94
119.08
60.04
81.80
26.57
22.06
19.80
23.94
119.44
60.28
81.74
26.59
22.15
10
19.88
23.99
119.22
59.87
81.83
26.48
22.07
In default ITN design, each latent æ, fused with its immediate predecessor It-1 stabilize inversion while preserving detail. Here, we investigate how increasing the pairing distance d affects the quality of source reconstruction and downstream editing.

---

Table 7: Ablation study on pairing distance d ITN for source image reconstruction.
Structure
Background Preservation
CI.IP Similarity
d
Distancex10
PSNR1
LPIPSx10> +
MSEx10
SSIM MSIMx100 100
Whole
w/o ITN
3.06
32.30
25.35
9.06
91.40
25.97
1 (Ours)
3.08
32.29
25.37
9.07
91.40
25.97
2 3.06
32.30
25.35
9.06
91.40
25.97
32.30
25.35
9.06
91.40
25.97
10
3.06
32.30
25.35
9.06
91.40
25.97
5, 10), and apply the Specifically, we vary the reference latent from 24-1 Zt-ds where d E 2,, same frequency-domain fusion strategy:
ft IFFT (Ha FFT().) + L.r FFT( +0, (O,
(13)
We keep a = 0.3, and og = - here.
The results are summarized in Table 6 Additionally, we evaluate the impact of different pairing distances d in ITN on source reconstruction quality, as shown Table From Table we observe that varying d has negligible effect on source image reconstruction. Combined with Table we find that applying ITN consistently improves the CLIP similarity in the edited regions compared to w/o ITN.
In addition, a complementary result Table the main paper, further provide a quantitative comparison of source image reconstruction among ITN and other iroostrrctioon-eeining methods in Table 8 These results further demonstrate that ITN provides superior reconstruction fidelity, preserving both structural details and semantic consistency.
Table 8: Ablation study on the ITN with other reconstruction-refiiieee methods for source image reconstruction.
Structure
Background Preservation
CLIP Similarity
d
Distancex109 4
PSNR1 f
LPIPS
MSE
SSTM
Whole
DDIM
70.23
17.76
210.84
224.43
71.39
27.07
Direct
2.95
30.57
31.41
17.60
87.50
25.45
NT
3.30
30.17
33.50
18.94
87.13
25.50
Friend
3.12
30.37
32.66
18.21
87.19
25.55
ITN (Ours)
2.33
30.49
31.88
17.85
87.47
25.64
E.2 Ablation Study Filter Strength and Fusion Weight in FRF
To evaluate the effect of key parameters in Frequency Residual Fusion (FRF), we conduct systematic ablation studies on the Gaussian filter strength. Specifically, the low-pass and high-pass filters f., and H. are defined as:
C. 2nd² E WXH
H. L. R WxH
(14)
where r denotes the frequency distance from the center and controls degree of Gaussian curve.
We experiment with various values of (0.1, 0.3, 0.4, 0.5, 0.6, 0.8, 0.9) control the fre- the fusion weight a E
quency selectivity the filters, and investigate the influence (..,, 0.2, 0.3, 0.5, 0.7, 0.9, 1.0). which modulates the relative contribution of the target's low- frequency components. The frequency-domain fusion is then computed as:
Fruse Ho FFT(u) + C. FFT(v),
(15)
(16) As shown in Table 9 and Table 10 the value and a have little impact on the results. However, setting = 0 leads to a noticeable drop in editing performance, indicating that the lack of low- frequency information from the target feature impairs the quality of non-rigid editing. This also highlights the limitations of directly performing feature injection.
(u, v) IFFT
25

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Table Ablation study on the Gaussian filter scaling coefficient o FRE
Structure
Background Preservation
CLIP Similarity
Distancexue
PSNRT
MSEx10
SSIM
Whole
Edited
w/o FRF
10.51
26.97
81.33
30.88
86.38
25.50
21.14
0.1
19.84
23.92
120.65
59.88
81.72
26.59
22.15
0.3 (Ours)
19.87
23.91
120.82
59.95
81.71
26.58
22.16
0.4
19.87
23.92
120.64
59.89
81.73
26.60
22.25
0.5
19.73
23.93
120.19
59.74
81.76
26.60
22.13
0.6
19.87
23.94
119.95
59.92
81.78
26.54
22.19
0.8
19.57
23.96
119.42
59.67
81.84
26.60
22.14
0.9
19.62
23.97
119.10
59.59
81.59
26.59
22.18
Table 10: Ablation study on the Fusion Weight a FRF
Structure
Background Preservation
CLIP Similarity
Distance
PSNRT
LPIPS
MSEx10
SSIM
Whole
Edited
M/OFRR FRF
10.51
26.97
81.33
30.88
86.38
25.50
21.14
0.0
19.08
23.98
116.44
59.27
82.03
23.39
22.04
0.1
19.81
23.92
120.56
59.88
81.72
26.62
22.27
0.2 (Ours)
19.87
23.91
120.82
59.95
81.71
26.58
22. 16
0.3
19.81
23.92
120.71
59.88
81.72
26.54
22.21
0.5
19.90
23.92
120.56
$9.85
81.72
26.61
22.22
0.7
19.92
23.92
120.74
59.94
81.72
26.57
22.17
0.9
19.90
23.92
120.61
59.87
81.72
26.61
22.25
1.0
19.85
23.92
120.64
59.92
81.71
26.58
22.19
E.3
Ablation Study the Noise Mixing Coefficient in SNI
To investigate the effect of the noise-content trade-off in our Stochastic Noise Injection (SNT) module, we conduct ablation experiments on the mixing coefficient in the query and key injection formulation:
Q = Q tay (O,,
Keelr" Egt
= 1)
Keelt' tg:
+a, N(o.N(
(17)
To ensure a balanced contribution between deterministic content and injected noise, fix to 1 and vary 1) across (0.0, 0.1, 0.2, 0.3, 0.5, 0.7, 0.9, 1.0). This allows us to systematically evaluate how different levels of stochasticity affect the flexibility and consistency of non-rigid edits.
Table 11: Ablation study the Noise Mixing Coefficient in SNI. w/o o SNI corresponds to the setting where 1 1.0, and Gaussian noise injected into the query and key the cross- cross-attention layers the target branch.
Strueture
Background Preservation
CLIP Similarity
7
Distancesios
PSNRT
LPIPS
MSF 4
SSTM2100
Whote Edited
w/o SNI
16.38
26.12
37.13
83,52
25.85
21.27
0.0
19.27
23.92
118.06
59.71
81.90
26.64
22.12
0.1
19.42
23.93
118.41
59.56
81.86
26.60
22.15
(Ours)
19.87
23.91
120.82
59.95
81.71
26.58
22.16
0.3
20.46
23.88
123.56
60.37
81.48
26.47
22.19
0.5
21.73
23.77
130.38
61.52
80.88
26.50
22.23
0.7
23.04
23.71
137.19
62.57
80.37
26.50
22.14
0.9
24.45
23.64
144.15
63.02
80.18
26.58
22.25
1.0
24.88
23.60
147.03
63.04
80.14
26.53
21.99
As shown in Table varying y/ has limited influence on the CLIP similarity of the edited regions, indicating stable editing performance, However, setting 1) 1.0, which corresponds to the absence
26

---

of randommess, leads to suboptimal editing quality and background preservation. To better leverage the refined source and larget branch features for editing, we ultimately choose 1 0.2 and of 0.8 as our default settings.
E.4
Ablation Study on the Duration of FRF and SNI Interventions
Source Image
0.1
= 0.3
= 0.5
0.65
0.8
Romove
Laptap
Add
Flowors
Add
Cat
Figure 9: Effect of the duration of the intervention for FRF and SNI. As increases, the target images better preserve source background structures maintain semantically meaningful edits. However, excessively large T may overly constrain generation, limiting the extent of semantic transformations.
To understand the impact the duration of the intervention for FRF and SNI, we conduct ablation studies by varying the proportion of timesteps during which these modules applied. Specifically, we experiment with 10%, 30%, 50%, 65%, and 80% the total denoising steps. Note that r = 0% corresponds random generation, which tends to strietly adhere the semanties of the target prompt. As the proportion of guided timesteps increases, background preservation improves, but this often comes at the cost of reduced editability.
preuren Table 12 and Figure 9 present the quantitative and qualitative trade-offs between content preservation
and editability. Based on these results, select duration of 50% for non-rigid editing, and 65% for rigid editing to achieve balanced performance.
Table 12: Ablation study on the Duration of FRF and SNI Interventions.
Structure
Background Preservation
CLIP Similarity
Distancex1os
PSNRT
LPIPS
MSF 4
SSIM
Whole
Edited
0.1
87.01
14.96
292.32
402.72
64.17
27.63
23.40
0.3
41.97
19.75
187.95
144.51
74.50
27.13
23.29
(Ours)
19.87
23.91
120.82
59.95
81.71
26.58
22.16
0.65
11.17
26.69
82.69
32.40
85.76
25.69
21.29
0.8
5.48
29.94
49.90
16.25
89.35
24.39
20.22
Additional Qualitative Comparisons
F1
Supplementary Visual Comparisons
In this section, we present additional comparison results that could not included the main paper due to space limitations. As shown in the Figure 10 our DiT-based method demonstrates superior editing performance across both non-rigid and rigid tasks. However, the L.DM-based version more constrained by the limitations of its underlying generative backbone, resulting in suboptimal
27

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outputs in certain cases. For example, in the first row, the model incorrectly alters the dog from the original image; in rows 8 and the results fail to reflect the intended semantic edits, indicating the difficulty LDM has with large-scale non-rigid transformations. In the deletion task of row 11, the model introduces unintended changes, such as adding glove the woman's 's hand. Overall, while our method is partially influenced by the choice of backbone, it achieves consistently strong results when built upon DiT v3.5-Medium.
F.2 Extended Visual Results and Analysis
Figure I and Figure 12 provide additional visual comparisons against all baseline methods on both non-rigid and rigid editing tasks. Our approach, in both DiT-based and LDM-based versions, consistently demonstrates superior editing performance across various scenarios.
In particular, Figure 12 includes failure cases from main paper (rightmost three columns). As illustrated, most competing methods struggle to preserve fine-grained details and maintain color consistency. more challenging cases, they fail execute the intended semantic transformations, such as removing glasses or altering the color of a cat.
F.3 Diverse Editing Capabilities of FSI-Edit-DiT
To further demonstrate the versatility of our approach, present qualitative results across broad spectrum of editing categories. These include rigid edits (e.g., changes in color, material, or style) as well non-rigid edits (e.g., object addition, removal and pose changes). As shown in Fig. [33 and Fig. 14 our method consistently produces high- figh-iideitty results across all scenarios, underscoring its robustness and generalizability.
28

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Source Image
RF-Inv
RF-Edit
Ours-LDM
Ours-DiT
Youag, woman is holding dog
dog Dak sitting on wooden chair
bird Wran standing on top of mountain
blue pyramid with reflection of monntaais ship
illustration of santa tanghing face angry
arohurch in the countryside with fence and trees
jumping corgi is Sitking a white background
fallow deer with head Parning back facing forwards
X
logo of 'iidd shape black background
photo of a grey swan swimming, with her babies
a woman in trench coat sitting at table with poll--poonne
a elephant in the forest ^ and man riding it
Figure 10: Visual comparison of different methods, This figure supplements the main paper with additional results, including comparisons with RF-Inv, RF-Edit, and our LDM-bascd approach both non-rigid and rigid editing tasks.
29

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Edit Instruction
White
Clods Eggs
Shirt 1 Sweater
Add
Remove
Stream
Leopard
Deer
iMoo
Road
Source Image
Ours-DiT
Ours-LDM
P2P
PnP
MasaCtrl
FlexiEdit
FreeDiff
RF-Inv
StableFlow
RF-Edit
Figure 11: Additional qualitative editing results on PIE-Bench Part I.
30

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Edit Instruction
Add Goat
Remove
Cat Tiger
Remove
Orange I Black
Pink hearts
Butterfly
Glasses
Green hearts
Source Image
Ours-DiT
Ours-LDM
P2P
PnP
MasaCtrl
FlexiEdit
FreeDiff
RF-Inv
StableFlow
RF-Edit
Additional qualitative editing results on PIE-Bench Part The rightmost columns show failure cases from our main paper. Competing methods often fail to preserve fine details or to perform the desired semantic edits (e.g., removing woman's 's glasses, changing the cat's color).
Figure 12:
31

---

Add Object
Hat
Butterfly
Mouse
Castle
Delete Object
Laptop
Paraglider
Lightning
Dog
Change Object
Duck
Marmot
Angel Demon
Glowing jar Crystaal
Tulip Lion
Change Color
Rod lpptiik PPurll lipstick
Green eyes
Blue eyes
White Yellow
Colorful Red
Change Material
Soulpture
Origami
Crochet
Bronze
More
Long hair ->
Short hair
Reticular -> spark
Closed Blooming
Tiger
-> Dog
Figure 13: Additional qualitative results on diverse editing types from PlE--Bench Part
32

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Visial Text
Happy
Hepe
LDM - DIT
adidas NIKE
Birthday ->
Year
World San Diego
Facial Attribute
Calm Laughing
Curly hair - Straigght hair
Laughing Crying
Serious Angry
Change Background
Snow Rain
Marlle wall - Flower wall
Street Forest
Mountains Cities
Change Pose
Sitting
Standing
Sitting - Sleeping
Shattered, plashing
Walking
Rumning
Change Style
Watercolor Anime
Stained glass window
abstract Digital
Watercolor Oilpainting
More
Delete faces
Add cat
Butterfly Parrot
Wotha -> chintzy doll
Figure 14: Additional qualitative results on diverse editing types from PlE:-Bench Part II.
33


Reviews:
[{"review_id": "U4SfmByxQd", "reviewer": "Reviewer_9JHL", "summary": "FSI-Edit introduces a novel diffusion-based image editing framework that enhances flexibility in both rigid and non-rigid edits. While traditional methods often struggle with semantic mismatches and limited generative diversity due to direct feature replacement, FSI-Edit effectively alleviates this drawbacks through two key mechanisms: Frequency Residual Fusion (FRF), which injects only high-frequency details from the source to preserve texture while avoiding structural conflicts, and Stochastic Noise Injection (SNI), which introduces controlled randomness to unlock diverse transformations. Extensive experiments show that FSI-Edit outperforms existing methods in terms of both semantic alignment and content preservation.", "questions": "Q1. For further questions and concerns, please refer to the Major and Minor Weaknesses sections above.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\nS1. This paper analyzes the drawbacks of the prior image editing method, especially for non-rigid edits, and effectively alleviates the problem by proposing the FSI-Edit framework. Furthermore, using frequency-domain features for image editing is both novel and impressive.\n\nS2. This paper proposes three novel components; 1) Frequency Residual Fusion, 2) Stochastic Noise Injection, and 3) Inversion Trajectory Navigation to enable flexible and complex translations for the image. \n\nS3. The authors provide various qualitative results in the main and the supplementary material, including non-rigid editing tasks. Moreover, by evaluating the framework using two distinct generative backbones (LDM [1] and DiT [2]), the authors demonstrate the robustness and generalizability of the proposed approach across different backbone architecture.\n\nS4. The paper includes comprehensive quantitative comparison against a diverse set of baseline methods. FST-Edit consistently shows superior performance across prior works, which strongly supports the effectiveness of the proposed work.\n\n**Major Weakness**\n\nW1. The ablation study results (including those in Appendix Sec. D) are not compelling. It raises the question of whether every component is necessary. While the differences in CLIP similarity are minor, performance in background & structure preservation varies significantly (especially w/o FRF and SNI). A more detailed investigation into the effect of each component would strengthen the foundation of the paper. Furthermore, it would be highly beneficial if the authors could provide a computational overhead (including GPU memory consumption and runtime) introduced by each component, to better quantify the trade-off in efficiency. This is my major concern with this paper.\n\nW2. For ITN, it lacks comparison with reconstruction-refining methods, such as Null-text inversion or edit friendly inversion.\nEffectiveness of Inversion Trajectory Navigation (ITN) as a latent-space refinement strategy is not compared against other established reconstruction-refinement techniques, such as Null-text inversion [3] or Edit-Friendly inversion [4]. A direct comparison with these methods would provide more clarity on the advantage of ITN for source image reconstruction.\n\n\n**Minor Weakness**\n\nW3. Is there any basis that stochastic noise injection on query and key in self-attention map makes sense and derives a good result? While the authors reference stochastic DDIM [5, 6] for the motivation, it’s weak since in stochastic DDIM, noise injection into the latent variable is mathematically grounded via SDE [7] (and Langevin MCMC). In contrast, injecting noise into attention maps lacks such theoretical justifications and it seems quite ambiguous.\n\nW4. The authors wrote preliminaries and details in the supplementary material. I appreciate the detailed discussion of those. In the final version, moving some of these sections (e.g. preliminary, ablation study) into the main paper could improve overall clarity and reader comprehension.\n\nReferences\n\n[1] Rombach, Robin, et al. \"High-resolution image synthesis with latent diffusion models.\" in CVPR (2022).\n\n[2] Peebles, William, and Saining Xie. \"Scalable diffusion models with transformers.\" in ICCV (2023).\n\n[3] Mokady, Ron, et al. \"Null-text inversion for editing real images using guided diffusion models.\" in CVPR (2023).\n\n[4] Huberman-Spiegelglas, Inbar, Vladimir Kulikov, and Tomer Michaeli. \"An edit friendly ddpm noise space: Inversion and manipulations.\" in CVPR (2024).\n\n[5] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. \"Denoising diffusion probabilistic models.\" in NeurIPS (2020).\n\n[6] Song, Jiaming, Chenlin Meng, and Stefano Ermon. \"Denoising diffusion implicit models.\" in ICLR (2021).\n\n[7] Song, Yang, et al. \"Score-based generative modeling through stochastic differential equations.\" in ICLR (2021).", "was_revised": true, "final_justification": "During the rebuttal, my major concerns regarding (1) the effectiveness and necessity of each proposed component, (2) computational cost, and (3) comparison with additional baselines were resolved. In addition, the authors provided a detailed motivation and formulation for stochastic noise injection (SNI), and discussed improvements in paper writing and presentation, which further alleviated my concerns. Therefore, I have increased my score to 4.", "rebuttal": "We sincerely thank the reviewer for the thorough and constructive feedback. We appreciate the recognition of our contributions, including the introduction of a novel frequency-domain editing framework, and the strong empirical performance across diverse settings. Your positive assessment of our method's design, robustness, and evaluation is highly encouraging. Below, we provide point-by-point responses to address the concerns and clarify potential misunderstandings.\n\n**[W1.1 The necessity of each component]**\nWe wish to highlight that evaluating ablation studies for **non-rigid editing** using preservation metrics can be inherently challenging and potentially misleading. This is a recognized difficulty in the field. For instance, non-rigid editing methods, MasaCtrl (ICCV23) and FlexiEdit (ECCV24), primarily rely on qualitative visual comparisons for their ablation studies rather than quantitative metrics. Following this established practice, we will include detailed visual ablations in the final version to more effectively demonstrate the contribution of each component.\n\nNevertheless, we respectfully argue that our current quantitative results are also compelling once they are interpreted correctly for this challenging task. The core of the issue lies in how to define success when significant structural changes are required.\n\nFirst, we establish the correct framework for interpretation. **Under-Editing Trap.** It is often easy to achieve a deceptively high CLIP score by under-editing (Our Baseline, Table1 below). This is because the target prompt frequently contains descriptions of the background or other non-edited elements (e.g., a blue cat sitting-> a blue cat standing). A model that fails to edit the subject will still get credit for the correctly rendered un-edited regions. **Metric Scaling Trap.** Our preservation metrics are scaled by large factors (e.g., LPIPS×10³). This makes numerical differences appear much larger than their actual visual impact.\n\nTable 1: More Ablation Study\n|Method|Distance×10³↓|PSNR↑|LPIPS×10³↓|MSE×10⁴↓|SSIM×10²↑|CLIP Whole↑|CLIP Edited↑|\n|-|-|-|-|-|-|-|-|\n|Baseline|8.45|27.97|68.94|24.93|87.80|24.71|20.91|\n|Base-X|96.66|56.54|8.08|0.02|99.14|19.74|19.80|\n|Ours-X|238.81|36.50|61.66|2.24|94.80|29.60|30.62|\n|Base-add|1.81|27.38|50.32|18.26|78.96|22.13|14.93|\n|Ours-add|17.29|22.69|139.98|53.83|68.96|25.82|25.83|\n\nTo illustrate this principle, we compare against the baseline on two representative non-rigid edits from our paper. \n- **Bird-to-X (Fig5 Row3).** The base result is still a blue bird. It achieves near-perfect preservation scores precisely because it completely fails to perform the edit. In contrast, ours successfully executes the edit. The associated \"worsening\" of preservation scores is direct proof that the required drastic structural change has actually occurred. \n- **Adding a Person to an Elephant (Fig5 Row6).** The base barely changes the image and consequently fails the semantic task. Ours succeeds. This semantic improvement is correctly accompanied by a significant change in the image structure, which the task demanded.\n\nThis not only demonstrates the effectiveness of our overall framework but also proves that its components are necessary to solve non-rigid editing.\n\n**[W1.2: Computational overhead of each module]**\nWe first report the resource consumption and runtime of the baseline methods. GPU memory usage and runtime are measured for processing a single image using our method (FSI-Edit-DiT) and several open-source baselines. All methods are run on a single NVIDIA RTX 4090 GPU, except for RF-Inv, StableFlow, and RF-Edit, which are executed on an A100 GPU due to their higher memory demands. Runtime is averaged over 10 images to ensure stable measurement.\n\nTable 2: GPU Memory Usage and Inference Time (Per Image)\n|Method|P2P|PnP|MasaCtrl|FlexiEdit|FreeDiff|RF-Inv|StableFlow|RF-Edit|Ours (DiT)|\n|-|-|-|-|-|-|-|-|-|-|\n| **GPU (GB)** |10.95|8.99|11.42|18.73|6.08|69.22|35.39|32.91|16.08|\n|**Time (s)**|34.84|18.09|21.71|38.97|17.41|76.74|26.07|34.51|21.38|\n\nThe frequency-domain operations do not introduce significant additional GPU overhead, so the overall GPU memory usage remains around $16.08 GB$. In terms of runtime, the baseline requires $17.19s$. The SNI module introduces a negligible overhead of $0.73s$, as it only injects noise into the intermediate features. The ITN module, applied during the inversion stage over 50 steps, adds $0.31s$. The FRF module is applied to the Q and K features across 13 self-attention layers and runs for 25 denoising steps, contributing $3.15s$ to the total runtime. It shows that our method achieves a well-balanced trade-off between computational efficiency and editing effectiveness.\n\n**[W2: Direct Comparison for ITN]**\nWe conducted a dedicated experiment based on the P2P backbone, comparing three representative inversion approaches: Direct Inversion (Direct) [1], Null-text Inversion (NT) [2], and Edit-Friendly Inversion (Friend) [3]. We evaluate these methods from two perspectives: **editing image quality**, and **source image reconstruction quality**. The corresponding results are reported in the following tables:\n\nTable 3: Editing Performance Comparison\n|Method|Distance×10³↓|PSNR↑|LPIPS×10³↓|MSE×10⁴↓|SSIM×10²↑|CLIP Whole↑|CLIP Edited↑|\n|-|-|-|-|-|-|-|-|\n|DDIM|69.43|17.87|208.80|219.88|71.56|25.01|**22.44**|\n|Direct|11.65|**27.22**|**54.55**|**32.86**|**85.10**|25.02|22.10|\n|NT|13.44|27.07|59.88|35.47|84.55|24.75|21.87|\n|Friend|11.07|26.17|58.73|38.21|84.26|25.22|22.13|\n|**ITN(Ours)**|**10.31**|26.34|57.57|37.20|84.60|**25.35**|22.13|\n\nTable 4: Source Image Reconstruction Quality\n|Method|Distance×10³↓|PSNR↑|LPIPS×10³↓|MSE×10⁴↓|SSIM×10²↑|CLIP Whole↑|\n|-|-|-|-|-|-|-|\n|DDIM|70.23|17.76|210.84|224.43|71.39|27.07|\n|Direct|2.95|**30.57**|**31.41**|**17.60**|**87.50**|25.45|\n|NT|3.30|30.17|33.50|18.94|87.13|25.50|\n|Friend|3.12|30.37|32.66|18.21|87.19|25.55|\n|**ITN(Ours)**|**2.33**|30.49|31.88|17.85|87.47|**25.64**|\n\nAs shown, our ITN achieves the lowest distance in both editing and reconstruction settings, and performs best in CLIP similarity. While Direct Inversion slightly outperforms in other background metrics. These results confirm the benefit of our ITN design, which leverages trajectory-aware frequency fusion between latent states to improve semantic coherence and source fidelity. ITN not only ensures **high-quality source reconstruction** but also facilitates **flexible and meaningful image edits**.\n\n**[W3: Stochastic Noise Injection into Query/Key]**\nThank you for this insightful question. Our stochastic noise injection (SNI) design is not ad hoc, but a deliberate and well-justified component of our framework. While the form of injection differs from that in stochastic DDIM, the core motivation remains consistent, introducing controlled stochasticity to enhance generative diversity. We argue that applying noise to attention components is both meaningful and effective, especially for non-rigid editing tasks, and we will elaborate on the reasoning and benefits of this design below.\n\n1. **Motivation: Enhancing Generative Diversity via Controlled Stochasticity.**\nOur core motivation is to introduce controlled, targeted stochasticity into the self-attention mechanism to enhance generative diversity, which is especially critical for complex non-rigid editing tasks. Unlike methods that inject noise into the entire latent variable, perturbing the Query (Q) and Key (K) vectors allows the model to explore diverse local semantic associations without disrupting the overall image structure. This provides the necessary flexibility for challenging edits precisely where it is most impactful.\n2. **Formulation.**\nOur method perturbs the attention mechanism by injecting zero-mean Gaussian noise into the Q and K, i.e., $\\mathbf{Q}' = \\mathbf{Q} + \\sigma_q \\cdot \\epsilon_q$ and $\\mathbf{K}' = \\mathbf{K} + \\sigma_k \\cdot \\epsilon_k$, where $\\epsilon_q, \\epsilon_k \\sim \\mathcal{N}(0, \\mathbf{I})$ and $\\sigma_q, \\sigma_k$ are scalar coefficients that control the noise strength. The attention is then computed as $\\text{softmax}\\left(\\frac{\\mathbf{Q}'{\\mathbf{K}'}^\\top}{\\sqrt{d}}\\right)\\mathbf{V}$. \nWe deliberately choose to inject noise before the softmax rather than after, for two reasons: First, for **stability and controllability**: by injecting noise pre-softmax, any perturbations are normalized, leading to only smooth, localized changes in the attention distribution. This ensures the effect is bounded and stable, avoiding the severe content distortions that could arise from adding noise directly to the Value (V) vectors. Second, this approach provides **semantic guidance** rather than content distortion. Perturbing Q and K affects where the model attends, not what content it attends to. This enables a more targeted exploration of token relationships to better align with the target prompt, a crucial capability for edits requiring fine-grained semantic adjustments.\n3. **Empirical Validation.**\nOur empirical results strongly validate this design's effectiveness. SNI significantly improves the model's semantic alignment with the target prompt, particularly for non-rigid transformations of pose or shape, while maintaining high consistency in the background and unedited regions. This confirms that our stochastic attention serves as a lightweight yet potent inductive bias, providing essential flexibility for challenging generative tasks.\n\n**[W4: Paper Structure Suggestion]**\nThank you for the suggestion. We appreciate your positive feedback on the supplementary material, and will incorporate key parts in the main paper.\n\nReferences\n\n[1] Xuan Ju, et al. Direct inversion: Boosting diffusion-based editing with 3 lines of code. in ICLR (2024)\n\n[2] Mokady, et al. Null-text inversion for editing real images using guided diffusion models. in CVPR (2023).\n\n[3] Huberman-Spiegelglas, et al. An edit friendly ddpm noise space: Inversion and manipulations. in CVPR (2024)."}, {"review_id": "xiX61jyVMG", "reviewer": "Reviewer_Xe12", "summary": "This paper proposes an image editing framework, i.e., FSI-Edit, designed for both rigid and non-rigid editing tasks in diffusion-based image generation. The proposed method introduces two main mechanisms: Frequency Residual Fusion (FRF) for high-frequency detail injection and Stochastic Noise Injection (SNI) to enhance generative flexibility. The expensive experiences are conducted on the PIE-Bench dataset.", "questions": "See the strengths and weaknesses", "limitations": "See the strengths and weaknesses", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1.\tThe proposed method is conceptually well-motivated and integrates frequency-domain information and stochasticity.\n\n2.\tThe paper is generally well-organized and clearly written. Figures help to convey the methodology effectively.\n\nWeaknesses:\n\n1.\tWhile the method incorporates some novel ideas, such as frequency-based feature fusion in attention layers, however, similar principles have been explored in recent works like FlexiEdit and FreeDiff.\n\n2.\tThe overall comparisons, especially among UNet-based methods, most of evaluation metrics show a noticeable gap between the proposed method and state-of-the-art (SOTA) baselines. This weakens the claim of superiority of the proposed approach.\n\n3.\tThe ablative results (Table 3) indicate that both \"w/o FRF\" and \"w/o SNI\" variants outperform the full model in most of metrics, which fails to clearly demonstrate the contribution of each component.\n\n4.\tIt is not clear whether the limitations mentioned also occur in other methods. A more comprehensive and critical analysis is recommended.", "was_revised": true, "final_justification": "Thanks for the rebuttal. Most of my concerns have been addressed.", "rebuttal": "We sincerely thank you for the constructive feedback and for recognizing the conceptual novelty and clarity of our proposed method. While we appreciate the positive remarks regarding our framework and its presentation, we would like to clarify several misunderstandings in the evaluation and address the concerns regarding the experimental comparisons, component effectiveness, and related work overlap. Below, we provide point-by-point responses to each comment.\n\n**[W1: Difference between FlexiEdit and FreeDiff]**\nThank you for acknowledging the novelty of our frequency-based feature fusion. We appreciate the opportunity to clarify the key distinctions between our method and prior works like FlexiEdit and FreeDiff. While the concept of frequency-domain manipulation has appeared in prior works, the way we leverage it is fundamentally different in both purpose and implementation. **FlexiEdit** applies frequency filtering at the latent noise level, which is early in the generation process and lacks semantic alignment with intermediate representations. **FreeDiff** operates on classifier-free guidance (CFG) signals using frequency-based constraints, without direct interaction with model features.\n\nIn contrast, our Frequency Residual Fusion (FRF) is, to our knowledge, the first to systematically inject frequency-domain manipulation directly into **deep feature maps** during the generation process. This design specifically targets the challenge of non-rigid semantic editing, which requires complex, large-scale structural changes (e.g., turning a bird into X shape). Our approach facilitates more controllable and precise edits by integrating high-frequency details from the source while preserving low-frequency semantics from the target. Visualizations and experiment results in the paper and supplementary materials demonstrate the superior effect of this design in such challenging tasks.\n\nFurthermore, our other components, Stochastic Noise Injection (SNI) and Inversion Trajectory Navigation (ITN), address distinct aspects of controllability and reconstruction quality. These ideas are not explored in the aforementioned works, further differentiating our method.\n\nThus, while frequency-domain ideas are not entirely new, our framework offers a novel, integrated, and effective way to apply them, particularly for flexible and large-scale image manipulation.\n\n**[W2: Gap in comparison with SOTA]**\nWe respectfully disagree with the characterization that a gap on specific metrics weakens our claim of superiority. In fact, this phenomenon highlights a critical misunderstanding of the evaluation of image editing. The task is not about maximizing any single metric, but about achieving an optimal **trade-off** between **semantic alignment** and **background preservation**.\n\n1.**The Inherent Trade-Off of Image Editing.**\nExcelling at one type of metric often signifies failure in the other, leading to two undesirable extremes. **Under-editing**: Methods that rank SOTA on preservation metrics (e.g., LPIPS) often do so by failing to meaningfully alter the image according to the prompt. This results in `high background consistency` but `poor semantic alignment`. In our paper, baselines like P2P, FreeEdit, and StableFlow exemplify this, achieving top preservation scores at the cost of insufficient editing. \n**Over-editing**: Conversely, a method that perfectly matches the text prompt but destroys the original background and structure is no longer performing an edit, but rather an unconstrained text-to-image generation. Hence, `high semantic alignment` but `poor background consistency`.\n\nA truly superior editing method must navigate this trade-off effectively. Our approach is designed to find this balance, delivering strong semantic changes while respecting the original image's integrity, as our qualitative results demonstrate.\n\n2.**Holistic Quantitative Analysis.**\nTo provide a fair, holistic comparison that honors this trade-off, we analyzed the average rank of each method across all $7$ evaluation metrics from our main paper. This approach normalizes the performance across competing objectives and reveals the most well-balanced method overall. The results below unequivocally demonstrate that our method is SOTA.\n\nTable 1: Average Ranking for General Editing (Main Paper Table 1)\n|Method|Avg Rank|Final Rank|\n|-|-|-|\n|P2P|3.71|2|\n|PnP|7.43|8|\n|MasaCtrl|8.71|10|\n|FlexiEdit|4.29|4|\n|FreeDiff|6.29|6|\n|Ours-LDM|5.00|5|\n|RF-Inv|9.43|11|\n|StableFlow|6.86|7|\n|RF-Edit|7.57|9|\n|DCEdit|4.14|3|\n|Ours-DiT|**2.43**|**1**|\n\nTable 2: Average Ranking for Non-rigid Editing (Main Paper Table 2)\n|Method|Avg. Rank|Final Rank|\n|-|-|-|\n|PnP|5.43|6|\n|MasaCtrl|5.71|7|\n|FlexiEdit|8.57|9|\n|FreeDiff|3.14|2|\n|Ours-LDM|3.71|3|\n|RF-Inv|6.57|8|\n|StableFlow|4.57|4|\n|RF-Edit|4.71|5|\n|Ours-DiT|**2.57**|**1**|\n\nA crucial point highlighted by these tables is the consistent effectiveness of our method across different backbones. While `Ours-DiT` establishes the new state-of-the-art, our UNet variant also achieves a top-tier rank (#3 in non-rigid editing). This proves that our method significantly enhances the standard UNet architecture, while also unlocking the full potential of the more powerful DiT backbone. This versatility further strengthens our claims.\n\n3.**Qualitative and User Study Validation.**\nThis quantitative superiority is further supported by our qualitative results. To formalize this, we conducted a user study to assess human perceptual preferences. We presented 11 participants with about 20 randomized image sets from PIE-Bench and asked them to score the results based on both semantic alignment with the prompt and background consistency. The results confirmed that our method was judged to be the best overall, achieving the most favorable balance of successfully editing the subject while preserving the background.\n\nTable 3: User Study\n|Method|Editing (Semantic)|Background|\n|-|-|-|\n|PnP|34.58|73.35\n|MasaCtrl|35.68|72.05|\n|FlexiEdit|39.55|64.60|\n|FreeDiff|36.69|73.04|\n|RF-Inv|55.86|63.09|\n|StableFlow|44.78|71.80|\n|RF-Edit|60.75|67.88|\n|**Ours-DiT**|**78.55**|**80.66**|\n\nTherefore, our quantitative, qualitative, and human-centric results robustly support our claim of superiority.\n\n**[W3: Ablation variants outperform full model]**\nThank you for the comment. The ablation results in Main Paper Table 3 can validate each component when interpreted correctly for the **non-rigid editing task**. For this task, an excessively high preservation score (e.g., LPIPS) signifies editing failure (under-editing), not superior performance, as it indicates the model failed to make the required structural changes.\nOur ablation results clearly demonstrate this trade-off:\n- **w/o FRF**: Its top preservation score and lowest semantic alignment prove that FRF is essential to enable structural deformation. Without it, the model simply resists the edit.\n- **w/o SNI**: Its high preservation and weak alignment show that SNI is crucial for providing the flexibility needed to find a successful edit path.\n- **Full Model**: It achieves the best semantic alignment with a necessary and expected drop in preservation—the signature of a successful and significant non-rigid edit.\n\nIn summary, the study confirms via proof by contradiction that both FRF and SNI are indispensable. Their synergy is essential for performing the challenging, flexible edits that are the core goal of our work.\n\n**[W4: Failure cases analysis]**\nThank you for the comment. We should point out that the comprehensive analysis is already provided in our paper. The **last three columns of Figure 11 in the Appendix** are dedicated to this exact head-to-head comparison. These results make it clear that the identified limitations are not weaknesses of our work, but are systemic challenges faced by all current state-of-the-art methods.\n\nTo summarize the findings from that comparison:\n- **Removing glasses**: Most methods either fail entirely or introduce severe artifacts. In contrast, our approach provides a more visually coherent result.\n- **Color-specific edits**: Most methods struggle with fine-grained color manipulation, often failing to correctly identify the target object before applying the specified color. \n\nTherefore, this direct comparison demonstrates that while most of all methods fail, our approach exhibits stronger and more consistent performance than others on these editing tasks."}, {"review_id": "oCHPU9yyFq", "reviewer": "Reviewer_7PE5", "summary": "The authors propose a method that allows both rigid and non-rigid image editing using diffusion models. The method is based on introducing high frequency part of the reconstruction features into the generation features along with some noise.", "questions": "- It is not clear what is the motivation behind Inversion Trajectory Navigation (ITF). Please add a few sentences to discuss the motivation behind including this component in your proposed framework.\n- Please provide the visual ablation of FRF in figure 6.", "limitations": "- According to equation (1), and (2), you multiply the low frequency component of $v$ by $alpha$ and then you add $v$ again after the inverse FFT. This basically boosts the low frequency part of $v$ by a factor of $\\alpha$. Please explain the reasoning behind this design. Also adding back $v$ in equation (2) basically keeps all the high frequency components of $v$. So if there is a clash between the high frequency parts of the edited image and the source your method will fail. This failure is also evident in the teaser figure (rigid editing - painting -> photo - bunny and cat image) where the edited image still looks like a painting and not a photograph.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n- The proposed method enables faithful editing for both rigid and non-rigid cases in a single unified framework.\n- Extensive quantitative evaluation with many different image-editing methods. \n- The qualitative results demonstrate that the proposed method outperforms the existing baselines.\n- The quantitative results are mixed but they show clear improvement for non-rigid editing and for the transformer architecture.\n\n\nWeaknesses:\n- The proposed method is complicated and involves many components with many hyperparameters to tune.\n- ITN only slightly improves the quantitative results. according to Table 3 (main). The authors also don't provide any motivations for including it in their proposed model.\n- The quantitative results are mixed. The results on PIE-Bench, Table 1, show marginal or no improvement compared to the existing method. A user study is probably needed to obtain a more decisive quantitative evaluation.", "was_revised": true, "final_justification": "After reading author's rebuttal I have increased my score from weak accept (4) to accept (5)", "rebuttal": "We sincerely thank the reviewer for the detailed and thoughtful feedback. We greatly appreciate the recognition of our unified framework's effectiveness across both rigid and non-rigid editing tasks, as well as the positive assessment of our extensive experiments and superior qualitative results.\nWe also acknowledge the reviewer’s concerns regarding the model’s complexity, the marginal gains of ITN in quantitative metrics, and the design rationale behind frequency manipulation. Below, we provide clarifications on these aspects and address all specific questions and limitations point by point.\n\n**[W1: Complicated methods, and many hyperparameters]**\nWe understand the concern about complexity. Our method is indeed structurally enriched to handle challenging non-rigid editing, but it remains practical: it runs with moderate memory ($16.08 GB$) and reasonable inference time ($21.38 s$).\nImportantly, our ablation studies in Appendix D show that performance is robust to a wide range of hyperparameter settings, meaning users can directly adopt our recommended defaults without tuning. Thus, while our method involves several components, it remains efficient, robust, and easy to use.\n\n**[W2&Q1: The motivation of ITN]**\nThe motivation for Inversion Trajectory Navigation (ITN) is to improve DDIM inversion fidelity by preventing information loss across timesteps. Inspired by our successful FRF module, ITN applies frequency fusion to the temporal domain. It enriches the current latent by incorporating stable, low-frequency structural cues from a previous, cleaner timestep, while retaining current high-frequency details. This creates a more robust trajectory for more faithful reconstructions, as validated by our ablation studies (Main Paper Table 3, Figure 6a).\n\n**[W3: Mixed quantitative results and User study]**\nThank you for your supportive review and for your insightful feedback. We particularly appreciate your comment on the quantitative results and your excellent suggestion that a user study would provide a more decisive evaluation.\n\n1.**User Study Confirms Superior Performance.**\nWe completely agree with your assessment and are pleased to report that we have followed your advice and conducted a formal user study. For this study, we used approximately 20 cases from PIE-Bench. Participants were asked to evaluate results from our method and key baselines on a scale of 0-100 across two critical axes: (1) **semantic alignment** with the text prompt and (2) **background consistency**. To prevent bias, the order in which the results were presented was randomized. From this process, we collected 11 valid responses, with an average completion time of 20 minutes. The detailed results are summarized below:\n\nTable 1: User Study\n|Method|Editing (Semantic)|Background|\n|-|-|-|\n|PnP|34.58|73.35\n|MasaCtrl|35.68|72.05|\n|FlexiEdit|39.55|64.60|\n|FreeDiff|36.69|73.04|\n|RF-Inv|55.86|63.09|\n|StableFlow|44.78|71.80|\n|RF-Edit|60.75|67.88|\n|Ours-DiT|**78.55**|**80.66**|\n\nThe results confirmed that our method was consistently rated as the best overall, achieving a superior, perceptually preferred balance between semantic accuracy and background preservation. We are committed to including an expanded version of this study, with more participants and diverse editing tasks, in the final paper.\n\n2.**A Holistic View on Quantitative Metrics.**\nThis user study helps to contextualize why the quantitative metrics in Main Paper Table 1 might appear mixed at first glance. Image editing evaluation involves an inherent trade-off between semantic fidelity and background preservation. Often, methods that excel on a single preservation metric do so by under-editing, making only minimal changes and thus failing to meet the user's intent. For example, methods like P2P or StableFlow score very high on background preservation but low on semantic similarity.\n\nOur goal was never to maximize a single metric at the expense of the edit's actual quality, but to achieve the most effective and balanced result. To provide a clearer quantitative picture that reflects this balance, we calculated the average rank of each method across all metrics. This holistic analysis smooths out the effects of the trade-off and reveals the most robust method overall.\n\nTable 2: Average Ranking for General Editing (Main Paper Table 1)\n|Method|Avg Rank|Final Rank|\n|-|-|-|\n|P2P|3.71|2|\n|PnP|7.43|8|\n|MasaCtrl|8.71|10|\n|FlexiEdit|4.29|4|\n|FreeDiff|6.29|6|\n|Ours-LDM|5.00|5|\n|RF-Inv|9.43|11|\n|StableFlow|6.86|7|\n|RF-Edit|7.57|9|\n|DCEdit|4.14|3|\n|**Ours-DiT**|**2.43**|**1**|\n\nTable 3: Average Ranking for Non-rigid Editing (Main Paper Table 2)\n|Method|Avg. Rank|Final Rank|\n|-|-|-|\n|PnP|5.43|6|\n|MasaCtrl|5.71|7|\n|FlexiEdit|8.57|9|\n|FreeDiff|3.14|2|\n|Ours-LDM|3.71|3|\n|RF-Inv|6.57|8|\n|StableFlow|4.57|4|\n|RF-Edit|4.71|5|\n|Ours-DiT|**2.57**|**1**|\n\nAs shown, when performance is viewed holistically, our DiT-based method consistently achieves the 1st overall rank, demonstrating its superior, well-rounded performance. We hope that the positive results of the new user study, combined with this more holistic perspective on our quantitative results, have addressed your concerns. Thank you again for your valuable and constructive feedback which has helped us strengthen the evaluation of our work.\n\n**[Q2: Visual ablation of FRF]**\nThank you for this request. While rebuttal stage limitations prevent us from including new figures, we will certainly add detailed visual ablations to the final paper.\n\nTo describe the key visual difference, the ‘bird-to-X’ non-rigid transformation is the most striking example. Without FRF, the model exhibits a clear failure of structural adaptation. The bottom-right quadrant of the object retains the shape of the original bird's body, failing to form the lower-right ‘leg’ of the target ‘X’. This results in a malformed, hybrid object. This specific failure demonstrates that FRF is crucial for mitigating this source structure inertia and enabling the complete, coherent transformations that are central to our method's success.\n\n**[L1.1: Reason of FRF]**\nThank you for this excellent question regarding the design of our Frequency-aware Residual Fusion (FRF). The formulation is deliberately designed to achieve a principled fusion of features from the source ($u$) and target ($v$), which we will break down below.\n\nOur core objective is to enrich the target feature $v$ with high-fidelity details from $u$, without corrupting the essential structure of $v$ that guides the generation.\n\n**Equation (1): Fusing Structure and Detail in the Frequency Domain.**\nThis step combines the best of both features in the spectral domain. \n$\\mathcal{L}$ is used to isolate the **low-frequency** components of the target $v$. These represent the global structure, layout, and overall shape—the essential \"scaffolding\" of the target. \n$\\mathcal{H}$ is used to extract the **high-frequency** components of the source $u$. These correspond to fine-grained textural details, edges, and patterns. By adding these, $\\mathcal{F}_{fuse}$ becomes a hybrid feature that contains the structural backbone of the target $v$ and the textural richness of the source $u$.\n\n**Equation (2): Reinforcement and Stabilization via Residual Connection.**\nThe second step, adding back the original $v$, is equally critical. This residual connection serves two vital purposes: \n**Reinforcing Semantic Integrity**: It ensures the final output remains strongly anchored to the target $v$'s complete structure, preventing the high-frequency details from $u$ from overpowering it.\n**Mitigating Artifacts**: It helps correct for potential phase misalignments or other artifacts that can arise from manipulating signals purely in the frequency domain, resulting in a cleaner and more stable feature map.\nIn essence, this two-step process allows our model to perform a sophisticated transplant of detail while rigorously preserving the target's core shape and identity.\n\n**[L1.2: Limitation]**\n    Thank you for this insightful observation. The reviewer is entirely correct, and the failure case in our teaser figure (the painting-to-photo edit) is a perfect illustration of this inherent challenge. When the high-frequency texture of the source (e.g., paint strokes) clashes with the semantics of the target prompt (e.g., a photograph), a conflict arises.\n\nThis highlights the deliberate synergy between our FRF and SNI modules. While FRF is designed to transfer rich, high-frequency details, this carries an inherent risk of introducing an overly strong texture bias. The another role of the SNI module is to counteract this effect. By injecting stochasticity, it provides the model with the flexibility to deviate from the source texture and better align with the target semantics. However, it's not always perfectly resolved.\n\nThis feature clash is a fundamental challenge for most tuning-free editing methods that rely on direct feature fusion. Preserving source identity via its features will always carry a risk of preserving unwanted source attributes like style or texture.\nWhile SNI serves as an effective mitigation, we agree that a more principled solution is a valuable direction. For future work, we plan to explore a semantically-guided fusion mechanism. For example, one could use attention maps to dynamically weigh the high-frequency fusion, reducing its influence in regions where a complete style change is required."}, {"review_id": "qYlSXV3Qd5", "reviewer": "Reviewer_MHta", "summary": "This paper aims to address a critical issue in text-to-image (T2I) editing based on Latent Diffusion Models (LDMs): how to enable flexible image editing without destroying the original image content. The study seeks to overcome the limitations of existing LDM-based T2I editing methods through frequency and randomness injection.", "questions": "See Weaknesses", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Advantages:\n1. The paper is fluently written with clear expressions, and the figures are highly appealing.\n2. The demonstrated effects outperform comparative methods both qualitatively and quantitatively.\n\nDisadvantages:\n1. As I have limited understanding of this field and am an outsider, I still find it difficult to comprehend the paper thoroughly even after careful reading, making it challenging to provide effective suggestions. It is recommended to refer more to the opinions of other professional reviewers. Apologies for the inconvenience.", "was_revised": true, "final_justification": "I'm very sorry, but I really can't understand this paper. Please refer to the comments of other reviewers.", "rebuttal": "We sincerely thank the reviewer for their time and effort in reading our paper and providing feedback, especially from the perspective of a broader audience. We appreciate your thoughtful comments, and we will take this as a valuable reminder to further improve the clarity and accessibility of our presentation for readers from diverse backgrounds."}]

Author rebuttal:

Meta-review: This paper presents FSI-Edit, a novel framework for flexible image editing. It uses frequency-domain fusion to preserve source details while allowing significant structural changes. It also injects noise to increase generative diversity. The method is technically sound, and it tackles the challenging problem of non-rigid editing. The experiments show strong performance across different model architectures. While initial reviews noted weaknesses in the ablation studies and mixed quantitative results, the paper's novelty and impressive qualitative results are significant. 

During the discussion period, reviewers raised several key concerns. For example, Reviewers 9JHL and Xe12 questioned the necessity of each proposed component. Reviewers Xe12 and 7PE5 noted that quantitative results were mixed. Reviewer 9JHL also requested better justification for the noise injection module and more comparisons. The authors provided an informative rebuttal, clarifying that standard metrics can be misleading for non-rigid edits. They also conducted a new user study, which strongly favored their method. The authors provided additional ablation results for rigid edits that clearly showed the contribution of each component. These comprehensive responses successfully addressed all major concerns, leading multiple reviewers to raise their scores. I agree with the reviews and recommend accepting this paper.
Decision: Accept (poster)