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EEdit: Rethinking the Spatial and Temporal Redundancy for Efficient Image Editing

Zexuan Yan, Yue Ma, Chang Zou, Wenteng Chen, Qifeng Chen, Linfeng Zhang

arXiv:2503.10270v3cs.CV

TL;DR

Inversion-based image editing has substantial computational overhead, including redundant computation in unedited regions and inversion. EEdit combines spatial caching and token preprocessing with inversion-step skipping; inversion timesteps can be reduced to 33.3% of diffusion timesteps with almost no noticeable performance degradation.

  • Problem

    Inversion-based editing computes unedited image regions and adds an inversion process whose cost matches denoising, creating substantial computation overhead.

    Source: §1 · p. 2

  • Method

    EEdit uses Spatial Locality Caching and token index preprocessing to skip computation in unedited regions, and inversion-step skipping to reduce inversion work.

    Source: §1 · pp. 2–3

  • Results

    Inversion timesteps can be reduced to 33.3% of diffusion timesteps with almost no noticeable performance degradation.

    Source: §1 · p. 3

  • Takeaways & Limitations

    EEdit provides an approach to reducing spatial and temporal redundancy in inversion-based image editing.

    Source: §6 · p. 8

    SLoC's cache cycle includes sequential initialization, edited-region bonus, sorting and selection, update, and refresh steps.

    Source: §4.2 · p. 5

Abstract

from arXiv · show

Inversion-based image editing is rapidly gaining momentum while suffering from significant computation overhead, hindering its application in real-time interactive scenarios. In this paper, we rethink that the redundancy in inversion-based image editing exists in both the spatial and temporal dimensions, such as the unnecessary computation in unedited regions and the redundancy in the inversion progress. To tackle these challenges, we propose a practical framework, named EEdit, to achieve efficient image editing. Specifically, we introduce three techniques to solve them one by one. For spatial redundancy, spatial locality caching is introduced to compute the edited region and its neighboring regions while skipping the unedited regions, and token indexing preprocessing is designed to further accelerate the caching. For temporal redundancy, inversion step skipping is proposed to reuse the latent for efficient editing. Our experiments demonstrate an average of 2.46 $\times$ acceleration without performance drop in a wide range of editing tasks including prompt-guided image editing, dragging and image composition. Our codes are available at https://github.com/yuriYanZeXuan/EEdit

1. Introduction

Inversion-based image editing carries substantial computation overhead, with redundant work across both image regions and inversion steps. EEdit targets these sources with spatial caching, token preprocessing, and inversion-step skipping.

1. Introduction

  • Edited regions show greater pixel differences and lower latent-state similarity than unedited regions, while inversion adds computation equal to denoising in the traditional editing pipeline.Source: §1 · p. 2
  • Skipping inversion steps had little perceptible effect, whereas reducing denoising steps first damaged fine details and then overall structural integrity.Source: §1 · p. 2
  • SLoC fully computes edited and neighboring regions while reusing cached features to skip computation elsewhere; token index preprocessing adds over 15% inference speed without changing the computation mathematically.This mixed computation is intended to preserve quality in edited regions while reducing work in unedited regions.Source: §1 · pp. 2–3
  • Inversion-step skipping reduced inversion timesteps to 33.3% of diffusion timesteps with almost no noticeable performance degradation.The method assigns fewer computations to inversion and more to denoising.Source: §1 · p. 3
  • 2.46× speedup over the original editing pipeline came with minimal performance differences, while EEdit reached up to 10.96× latency acceleration over other editing methods.Evaluations covered prompt-guided, reference-guided, and drag-guided editing.Source: §1 · p. 3

2.1. Acceleration of Diffusion Models

  • Existing caching methods can incur token-correlation overhead, require attention maps or KV matrices that conflict with FlashAttention, and fail to use editing-region priors; EEdit addresses spatial and temporal redundancy.Source: §2.1 · p. 3

2.2. Image Editing

  • Common editing approaches add noise in latent space and denoise it; attention-map-based methods may require storing maps and manipulating KV matrices, increasing latency and limiting compatibility with attention acceleration.Source: §2.2 · pp. 3–4

3. Preliminaries

EEdit targets spatial and temporal redundancy in image editing by guiding cache reuse with the edit mask and skipping selected inversion steps.

3.1. Rectified Flow

  • Rectified Flow models transformation from Gaussian noise to real data along a straight path, with Conditional Flow Matching providing an equivalent tractable objective.The objective uses conditional vector fields along the probability path.Source: §3.1 · p. 4

4. Approaches

  • EEdit targets spatial and temporal redundancy by using the edited-area mask to guide cache refreshing and reuse, alongside inversion step skipping.The approach comprises spatial locality caching, token index preprocessing, and inversion step skipping.Source: §4 · p. 4

4.1. Spatial Locality Caching

  • SLoC assigns lower computation importance to tokens outside the edit mask and boosts edited-region neighbors with distance-decaying scores.The mask-guided approach reuses cached features for other regions while computing the edited region and its neighbors.Source: §4.1 · p. 5

4.2. Token Index Preprocessing

  • Token index preprocessing moves cache scoring, sorting, and index selection offline with mathematically equivalent selections, leaving a single read/write cost during inference.The former four cache-cycle steps can be omitted during inference.Source: §4.2 · p. 6

4.3. Inversion Step Skipping

  • Inversion step skipping reuses the previous noise between scheduled RF-inversion steps, while the final inversion step remains fully computed.The paper reports using a skip step of 3 to balance quality and speed.Source: §4.3 · p. 6

5. Experiments

The experiments evaluate EEdit across prompt-guided, drag-guided, and reference-guided editing, then ablate SLoC, inversion step skipping, and TIP. They report stronger background consistency and reduced latency while preserving editing quality.

5.1. Implementation Details

  • Experiments use FLUX-Dev as the 12B-parameter base model, with SD-series models for other qualitative results, and draw on benchmarks for prompt-, reference-, and drag-guided editing.All experiments were conducted on an NVIDIA H20.Source: §5.1 · pp. 6–7

5.2. Compare with Baseline

  • EEdit’s prompt-guided results show superior consistency, detail preservation, and aesthetic style compared with the discussed baselines.The qualitative comparisons span prompt-guided, drag-guided, and reference-guided editing.Source: §5.2 · p. 7, Figure 5 · p. 7
  • EEdit achieves significantly better background consistency than other state-of-the-art editing methods, while ISS yields a CLIP score comparable to full-computation RF-Inversion.Table 1 compares quality and efficiency; the prose reports these findings without supplying specific metric values.Source: Table 1 · p. 7, §5.2 · p. 7

5.3. Ablation Study

  • SLoC improves text-editing results over full computation despite using more steps, while incurring lower computational overhead.The ablation uses this comparison to assess regionally focused computation.Source: §5.3 · p. 8, Figure 6 · p. 8
  • Increasing the inversion skip interval reduces inference time while maintaining background preservation and producing negligible differences in foreground fidelity.The reported comparisons include different skip settings against full-step computation.Source: Table 2 · p. 8, §5.3 · p. 8
  • TIP remains compatible with ISS while maintaining comparable foreground fidelity and CLIP scores across editing tasks, and reduces inference latency by over 20% on average versus the SLoC baseline.Table 3 covers prompt-guided, drag-guided, and reference-guided editing.Source: Table 3 · p. 8, §5.3 · p. 8

6. Conclusions

EEdit targets spatial and temporal redundancy in inversion-based editing with SLoC, TIP, and inversion step skipping. The authors present it as an exploration toward efficient, potentially real-time image and video editing.

6. Conclusions

  • EEdit combines spatial locality caching, token index preprocessing, and inversion step skipping to address computation costs from spatial and temporal redundancy in inversion-based editing.The authors describe cache-based acceleration across varied editing tasks as a first adaptation of this kind for diffusion inference.Source: §6 · p. 8

7. Analysis on Hidden States in MM-DiT

The hidden-state analysis finds greater similarity, and thus redundancy, in Cross-Attention than in Self-Attention. The accompanying method material describes cache frequency control and vanilla SLoC’s regional caching procedure.

7. Analysis on Hidden States in MM-DiT

  • Cross-Attention hidden states show higher cosine similarity across timesteps than Self-Attention, indicating greater redundancy in that module.EEdit therefore either fully computes or entirely skips Cross-Attention to optimize efficiency.Source: §7 · p. 13, Figure 9 · p. 13, Figure 10 · p. 13

8.1. Cache Frequency Control

  • Cache frequency control adjusts token scores using reuse frequency, with γ controlling how strongly that frequency affects the score map.The accompanying description updates frequency counts for tokens selected for cache updates.Source: §8.1 · p. 13

8.2. Vanilla SLoC w/o ISS and TIP

  • Vanilla SLoC first inverts the image, then uses scored token selection and cached features during editing to limit computation to selected regions.The algorithm’s inputs include the image, editing mask, prompt, random map, edited-region bonus, and cache.Source: §8.2 · p. 13

9. Discussion on TIP

TIP preprocesses token selection to reduce cache-related latency, with reported acceleration on top of SLoC. The discussion describes its scoring and selection procedure and argues that preprocessing preserves the online method’s selections.

9. Discussion on TIP

  • With preprocessing overhead of no more than 150ms, TIP reduces cache-induced inference latency by over 1000ms and enables further lossless acceleration on top of SLoC.The authors attribute the speed improvement to reducing function calls within the cache module.Source: §9 · pp. 13–14

9.2. Notation and Problem Setup

  • SLoC scores tokens using a random or seed-based component and their selection frequency, then selects the top R% for cache updates.The setup omits layer and module indices for simplicity in the proof.Source: §9.1 · p. 14, §9.2 · p. 14

9.1. Proof of TIP Equivalence with Original Operations

  • TIP is mathematically equivalent to online scoring, sorting, and token selection, selecting identical tokens at each step.The proof concludes that preprocessing produces the same outcome as the online approach.Source: §9.1 · p. 14, §9.5 · pp. 14–15

9.4. Proposed Optimization (TIP)

  • TIP simulates token selection offline and stores the selected indices for inference, which reads the precomputed indices instead of recomputing scores.The online procedure scores, sorts, selects, updates the cache, and increments selection-frequency counts.Source: §9.4 · p. 14, §9.3 · p. 14

9.5. Proof of Equivalence

  • The equivalence proof uses induction: matching frequency maps preserve matching scores and top-R% selections across steps.It concludes that token selection and cache updates remain identical throughout the process.Source: §9.5 · pp. 14–15

10. Metrics

The experiments use feature-based and pixel-space similarity measures, an instruction-adherence score, and computational-cost metrics.

10. Metrics

  • FID and LPIPS are feature-based similarity metrics, with lower values indicating higher similarity.The measurements use InceptionV3 for FID and AlexNet for LPIPS.Source: §10 · p. 15
  • PSNR and MSE measure pixel-space similarity; higher PSNR and lower MSE indicate greater image similarity.Source: §10 · p. 15
  • CLIPScore measures prompt adherence, while FLOPs quantify inference cost; higher CLIPScore indicates stronger adherence and higher FLOPs greater overhead.CLIPScore uses clip-vit-base-patch16.Source: §10 · p. 15

11. More Experiments

The experiments report efficiency and fidelity results for EEdit’s optimizations, alongside a qualitative example and an ablation study scope.

11. More Experiments

  • The Table 5 ablation studies imbalanced inversion and denoising for background preservation, foreground fidelity, and inference time.Source: Table 5 · p. 15
  • Token index preprocessing is described as providing loss-less acceleration for editing quality in a qualitative example.Source: Figure 11 · p. 15
  • 2.46× inference-latency improvement and 3.53× computational-efficiency acceleration are reported over the original unaccelerated version; TIP + ISS achieves best fidelity and efficiency.Source: §11 · p. 15

12. Related work

The section lists related image- and video-editing work, discusses AI safety and controllable generation, and presents additional editing examples.

12. Related work

  • The related-work discussion names work on editing both images and video.It lists examples including VideoGrain, FastVAR, IntLora, Tpsence, Pointnorm, MotionDiff, FastScene, DreamRelation, Dreamvideo, and Lazymar.Source: §12 · p. 15

12.1. AI safety discussion

  • The authors describe watermarking AI-generated content as a responsibility and safety measure and say they will release code to support safe use.Source: §12.1 · p. 15

12.2. More relative controllable methods

  • Controllable-generation work inspires the authors to consider locally sensitive caching for a wider range of controllable generation and editing tasks.Source: §12.2 · p. 15

13. Gallery

  • The gallery includes prompt-, drag-, and reference-guided edits, and adapts Redux for image-to-image tasks to generate image variations.Source: §13 · p. 15

Drag Diffusion FastDrag RegionDrag Ours Drag-guided

The gallery includes comparisons and examples of drag-guided and other image-editing outputs.

Drag Diffusion FastDrag RegionDrag Ours Drag-guided

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