Title: FSI-Edit: Frequency and Stochasticity Injection for Flexible Diffusion-Based Image Editing
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image Editing

Tuning-free

Non-rigid 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.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
Advantages:
1. The paper is fluently written with clear expressions, and the figures are highly appealing.
2. The demonstrated effects outperform comparative methods both qualitatively and quantitatively.

Disadvantages:
1. 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.

QUESTIONS:
See Weaknesses

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-14

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\n- 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-18"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:

1.	The proposed method is conceptually well-motivated and integrates frequency-domain information and stochasticity.

2.	The paper is generally well-organized and clearly written. Figures help to convey the methodology effectively.

Weaknesses:

1.	While 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.

2.	The 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.

3.	The 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.

4.	It is not clear whether the limitations mentioned also occur in other methods. A more comprehensive and critical analysis is recommended.

QUESTIONS:
See the strengths and weaknesses

LIMITATIONS:
See the strengths and weaknesses
TITLE: Official Review
CLARITY: 4
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFSI-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.\n\nSTRENGTHS AND WEAKNESSES:\n**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).\n\nQUESTIONS:\nQ1. For further questions and concerns, please refer to the Major and Minor Weaknesses sections above.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}