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EditDistill: Is It Possible to Guide Video Editing with Image Editing?

Guojun Lei, Hong Li, Hongbing Yang, Lixue Gong, Chi Wang, Zheng Dong

arXiv:2610.34303v1cs.CV

TL;DR

Controllable video editing remains challenging for local changes, dynamic effects, and consistent edits across foreground and background. EDITDISTILL distills an image-pair transformation into a descriptor to guide video diffusion, and reports competitive edit fidelity with improved preservation, temporal consistency, and perceptual quality over evaluated baselines.

  • Problem

    Controllable video editing remains challenging for fine-grained local edits, dynamic background effects, and consistent foreground-background changes.

    Source: §1 · p. 2

  • Method

    EDITDISTILL distills an image-pair transformation into a compact descriptor, injects it into video diffusion, and uses edit-alignment and DINO structural-consistency objectives.

    Source: §1 · p. 2, §3 · p. 4, §4.3 · p. 6

  • Results

    EDITDISTILL reports competitive edit fidelity while improving preservation, temporal consistency, and perceptual quality over evaluated baselines.

    Source: §1 · pp. 2–3

  • Takeaways & Limitations

    The framework provides a practical paradigm for integrating image-level editing into video generation, with potential applications in content creation, post-production, and interactive media editing.

    Source: §6 · p. 10

    Because the descriptor comes from the first frame, performance can degrade with very large motion, severe occlusion, or scene changes that make that edit unrepresentative of later frames.

    Source: A Limitations and Impact · p. 13

Abstract

from arXiv · show

Recent advances in image editing have enabled fine-grained and highly controllable visual modifications. However, achieving similar levels of controllability in video editing remains an open challenge, particularly for precise local edits, dynamic temporal effects, and consistent lighting across frames.In this paper, we propose EditDistill, a framework that distills image-level visual transformations from edits to guide video editing.Specifically, given a source video and an edit prompt, we first generate an edited version of the initial video frame using an off-the-shelf image editing model. We then distill the visual transformation of this original-edited image pair into a compact representation termed an edit descriptor.This descriptor is injected into a video diffusion model through our lightweight fusion module, which steers the denoising process toward the desired edit. To align the video trajectory with the demonstrated image edit while preserving motion and layout, we train with edit-descriptor alignment and DINO structural consistency losses, and derive an equivalent inference-time guidance formulation under flow matching.Extensive experiments demonstrate that our method achieves strong performance on standard benchmarks and produces high-quality video editing results across diverse scenarios. For more details and videos, please refer to the anonymous webpage: https://yu-shaonian.github.io/EditDistill-Web/.

1 Introduction

EDITDISTILL addresses difficult local, dynamic, and jointly consistent video edits by transferring image-edit transformations into video diffusion. The authors report competitive edit fidelity and improvements in preservation, temporal consistency, and perceptual quality on standard benchmarks.

1 Introduction

  • Video editing remains challenging for fine-grained local changes, dynamic background effects, and consistent foreground-background edits.The paper notes that local edits often require masks, while dynamic effects lack explicit temporal control signals.Source: §1 · p. 2
  • EDITDISTILL encodes an image-pair transformation as a compact descriptor and injects it into video diffusion to guide denoising.The introduction describes a lightweight fusion module and edit-descriptor alignment with DINO structural similarity loss.Source: §1 · p. 2, Figure 2 · p. 3
  • EDITDISTILL achieves competitive edit fidelity while improving preservation, temporal consistency, and perceptual quality over evaluated baselines.The method’s contributions also report results across a curated five-family video-editing dataset.Source: §1 · pp. 2–3

2 Related Work

The related work contrasts image-level editing controls with the temporal-consistency challenges of video editing. EDITDISTILL uses an explicit learned transformation descriptor as video guidance.

2 Related Work

  • Image-editing methods provide rich text- or image-driven controls, but their image-level signals do not ensure temporal consistency or reuse transformations across videos.The paper frames its compact feature-level transformation as guidance for video editing.Source: §2 · p. 3
  • Video editors use attention control, feature propagation, inversion, correspondence guidance, and one-shot tuning, but can struggle with exemplar-specific changes, relighting, or complex local edits without masks.Motion-control approaches can add a relatively heavy conditional pathway and assume explicit trajectory or region-level controls.Source: §2 · p. 3
  • EDITDISTILL replaces implicit prompt interpretation and dense control specification with a learned transformation descriptor while relying on the video backbone for temporal rendering.The paper describes its injector as parameter-efficient relative to ControlNet-style residual branches and low-rank adapters.Source: §2 · p. 3

3 Problem Formulation

The formulation seeks an edited video whose transformation matches an edited image pair while retaining coherence with the source video. It factors this goal through a compact edit descriptor.

3 Problem Formulation

  • The task is to generate an edited video consistent with the image-level transformation while maintaining temporal coherence with the source video.The notation defines Is as the source image, Ie as its prompt-edited version, and Vs as an N-frame source video.Source: §3 · pp. 3–4
  • The formulation factors video editing into encoding the transformation between an image pair as descriptor d, then generating an edited video from the source video, prompt, and descriptor.This separates transformation modeling from video generation so image-level edits can be applied during video synthesis.Source: §3 · p. 4
  • The learned transformation focuses on regions relevant to the edit intent, as indicated by the attention visualization.The formulation describes the desired video as applying the image-level transformation while maintaining coherence with the source.Source: §3 · p. 4

4 Method : EDITDISTILL

EDITDISTILL separates image-edit extraction from temporal video rendering. A compact descriptor conditions a frozen video diffusion backbone, while alignment objectives guide editing and preserve source structure.

4.1 Stage I: Distilling Image Edits into Compact Descriptors

  • Stage I encodes a source-edited image pair into a 1024-dimensional descriptor and reconstructs the edited latent as a residual transformation of the source latent.The descriptor is sampled from a learned posterior during training and taken as its mean at inference; a KL regularizer encourages a smooth, compact transformation space.Source: §4.1 · p. 4
  • Descriptor attention concentrates on changed regions for local edits and responds more broadly, but still structurally, to global edits such as relighting or style changes.The descriptor represents a transformation rather than a target image or fixed mask; its interaction with video features determines where changes apply in each frame.Source: §4.1 · p. 5, Figure 4 · p. 5

4.2 Stage II: Descriptor-Guided Video Diffusion

  • Stage II conditions Wan2.1-I2V-14B with a lightweight fusion module while keeping the backbone frozen and training only the injection module.The injector uses cross-attention for localized edits, FiLM for global appearance control, and an additive branch for low-rank edit biases.Source: §4.2 · p. 5

4.3 Training Objectives and Inference Guidance

  • Stage II combines flow matching with edit-descriptor alignment and DINO structural consistency objectives to guide the edit while preserving source-video layout.The alignment term matches the induced feature shift with d; the DINO term preserves the structural layout of the source video.Source: §4.3 · pp. 5–6
  • The same auxiliary objectives can be applied as test-time gradient guidance under the score–velocity identity, supporting tuning-free inference-time control.The paper relates the objectives to guidance on the denoising trajectory under flow matching.Source: §4.3 · p. 6

5 Experiments

Experiments evaluate EDITDISTILL’s edit fidelity, preservation, temporal coherence, perceptual quality, and video consistency, alongside ablations of its injection branches and auxiliary losses. Results show competitive edit fidelity and stronger preservation and temporal metrics, with distinct trade-offs between edit strength and structural consistency.

5.1 Experimental Setup

  • Training freezes the Wan2.1-I2V-14B backbone and trains only the edit-injection modules in Stage II.The reported setup uses 30,000 Stage II iterations and 81-frame inference clips at 480 × 832 resolution.Source: §5.1 · p. 6
  • The evaluation spans varied edit types and video benchmarks, using complementary metrics for fidelity, preservation, temporal coherence, perceptual quality, and video consistency.The setup reports a five-family benchmark average and describes training on 100K filtered video-editing samples alongside reconstruction samples made from real videos.Source: §5.1 · pp. 6–7, §5 · p. 6, §5.2 · p. 7

5.2 Main Experimental Results

  • DINO-sim rises from 0.701 to 0.738, Warp falls from 3.42 to 2.97, and FVD falls from 914.6 to 852.1 versus VACE; CLIP-E is second-best at 0.245 versus VACE’s 0.247.EDITDISTILL has the best DINO-sim, Temp., Warp, and FVD scores, while CLIP-T remains below the strongest baseline.Source: §5.2 · p. 7, Table 1 · p. 7
  • EDITDISTILL leads on BkgC, MoS, AesQ, and DyaD, but its SubC of 0.942 trails Omni-Video’s 0.964.The reported combination of background consistency and dynamic degree suggests coherent edits under motion, while subject consistency remains a relative weakness.Source: §5.2 · p. 7, Table 2 · p. 8
  • Qualitative comparisons show that EDITDISTILL’s transformation descriptor distinguishes what changes from where it changes, supporting stable local and global edits across frames.Other methods may miss fine exemplar-specific attributes, jitter or contaminate nearby regions, or transfer irrelevant reference details into backgrounds; relighting comparisons also report coherent global illumination changes while preserving geometry and motion.Source: §5.2 · pp. 7–8, Figure 5 · p. 8, Figure 6 · p. 9

5.3 Ablation Studies

  • Cross-attention is the strongest individual branch for edit fidelity at 0.243 CLIP-E; the full gated fusion improves CLIP-E by 0.002 and lowers FVD by 69.3.FiLM supports global appearance control and layout preservation but has limited spatial selectivity; the additive branch supplies a residual bias when combined with the others.Source: §5.3 · pp. 8–9, Table 3 · p. 9
  • Adding Ledit raises CLIP-E from 0.184 to 0.273, while the full objective improves DINO-sim, Temp., Warp, and FVD but has CLIP-E of 0.245.Edit alignment alone leaves structural drift insufficiently controlled; LDINO alone improves preservation and temporal coherence but leaves edit fidelity low.Source: §5.3 · p. 9
  • The experiments’ overall pattern is a trade-off: EDITDISTILL combines a compact transformation descriptor with injection into a frozen video backbone to support controllable edits with temporal coherence and structural consistency.The comparison notes that strong video priors can under-edit, while image or inversion control can lose temporal stability and reference conditioning can copy irrelevant details.Source: §5.3 · p. 9

6 Conclusion

The conclusion presents EDITDISTILL as a framework that distills image-level edits into a compact feature and injects it into video diffusion. It frames the approach as a practical way to integrate image-level editing into video generation while preserving the backbone’s generative prior.

6 Conclusion

  • EDITDISTILL distills image-level editing into a compact feature and injects it into a video diffusion model to guide denoising.The conclusion pairs this conditioning with edit-descriptor alignment and DINO structural consistency losses for temporal consistency.Source: §6 · p. 9
  • Removing Ledit weakens edit transfer, while removing LDINO increases structural drift and warp error.Source: Table 4 · p. 10
  • The framework is presented as a practical paradigm for integrating image-level editing into video generation while preserving the backbone’s generative prior.The stated potential applications include content creation, video post-production, and interactive media editing.Source: §6 · pp. 9–10

A Limitations and Impact

EDITDISTILL has limitations when edits are highly localized or when later frames diverge substantially from the first frame. The paper also flags misuse risks and recommends safeguards and restricted deployment settings.

Limitations

  • Large motion, severe occlusion, or scene changes can make a first-frame descriptor poorly representative of later frames and degrade performance.The implicit denoising injection can also affect nearby regions when attention spreads beyond a highly localized edit.Source: A Limitations and Impact · p. 13

Impact

  • Visual-exemplar-constrained editing may lower the barrier to targeted misuse, including deceptive media generation, identity manipulation, and unauthorized content modification.Source: A Limitations and Impact · p. 13

Responsible use

  • The paper recommends provenance tracking, watermarking or disclosure, privacy- and licensing-respecting data, and restricted deployment for portrait editing and safety-sensitive scenarios.The work does not involve human-subject experiments.Source: A Limitations and Impact · p. 13

B Stage I Architectural Details

Stage I compresses the change between source and edited image latents into a stochastic descriptor, then reconstructs the edited latent using only the source latent and descriptor. Its architecture is designed to preserve source structure while concentrating capacity on the edit.

Latent-delta encoder

  • The encoder combines shared source/edited latent features, their explicit difference, and a multi-scale hierarchy to infer a stochastic edit descriptor.The descriptor is sampled during training and the posterior mean is used at inference.Source: B Stage I Architectural Details · pp. 13–14

FiLM-conditioned residual decoder

  • The decoder receives only the source latent and descriptor, predicting a residual that is added to the source latent to reconstruct the edited latent.Its attention and conditional residual blocks route descriptor information while source-only skip connections preserve layout and identity.Source: Figure 7 · p. 13, B Stage I Architectural Details · p. 14

Bottleneck capacity

  • The default 1024-dimensional descriptor balances reconstruction fidelity and transferability: smaller descriptors lose fine edit attributes, while larger ones offer marginal gains and risk source-specific leakage.Source: B Stage I Architectural Details · p. 14

Stage II implication

  • Because the descriptor acts as a global edit operator rather than a spatial template, video features can re-localize it to moving regions across frames.Figure 8 illustrates the same descriptor attending to relevant regions in different frames.Source: B Stage I Architectural Details · p. 14

C Full Flow-Matching Derivation for Stage II

The forward process defines a straight-line path between clean data and noise. Its interpolation parameter runs from the data endpoint to the noise endpoint.

C.1 Forward Interpolation Path

  • The path interpolates between a data sample x0 and noise ε as xt = (1 − t)x0 + tε, with t = 0 at clean data and t = 1 at pure noise.Source: §C.1 · p. 15

C.2 Training Objective

The derivation sets out flow-matching training and reverse-time inference, then formulates edit and structural guidance for video denoising. The two guidance terms target the demonstrated image edit and preservation of video structure, and can be applied through an optional inference-time correction.

C.2 Training Objective

  • The model regresses the true velocity along the interpolation path, with c denoting the conditioning signal.Source: §C.2 · p. 15

C.3 Reverse ODE for Inference

  • Inference generates samples by integrating the vector field from t = 1 to t = 0, discretized in practice with an Euler solver or DPM-Solver.Source: §C.3 · p. 15

C.4 Notation

  • The notation identifies source and edited reference images, the edited-video target, DINO features, and the base model conditioning.Source: §C.4 · p. 15

C.5 Edit Consistency Assumption

  • The edit-consistency assumption requires the image-level transformation encoded in ΔF edit to be applied consistently to every video frame.Source: §C.5 · p. 15

C.6–C.7 Vector-field derivation

  • The vector field is decomposed into an unconstrained base prediction and an edit-guidance correction, with score–velocity duality derived under the linear interpolation path.Source: §C.6 · p. 16, §C.7 · p. 16

C.8 Bayesian Guidance Framework

  • The Bayesian guidance framework conditions on edit-descriptor features and converts the resulting joint score into a guided vector field.Source: §C.8 · p. 16

C.9–C.10 Guidance losses

  • Edit-feature alignment matches each frame's predicted feature shift to the image-level edit direction, while DINO alignment compares predicted frames with original-frame features to preserve structure.DINO features are sensitive to semantic structure but relatively insensitive to local appearance changes, allowing structural regularization without suppressing the desired edit.Source: §C.9 · p. 16, §C.10 · pp. 16–17

C.12 Complete Guided Vector Field

  • The complete guided field combines base denoising with edit-alignment and temporal-consistency terms; optional inference-time guidance measures both losses and corrects velocity, with gradient clipping for outliers.The edit term steers frames toward the image-level direction, while DINO consistency regularizes structural drift in unchanged content.Source: §C.12 · p. 17

E Edit Descriptor Feature Injection Modules: Per-Branch Details

The appendix expands the three edit-injection branches and their fusion design. The branches target spatially selective edits, global appearance changes, and residual edit biases.

Qualitative ablation

  • Removing individual branches or alignment losses weakens edit fidelity or temporal preservation, consistent with the quantitative ablation trends reported in Section 5.3.The appendix describes this as a qualitative ablation.Source: Figure 9 · p. 19

Shared MLP stem

  • A shared MLP stem adapts the descriptor between the Stage I latent space and the video transformer’s residual stream, while sharing the stem keeps trainable parameters small and discourages conflicting branch interpretations.The latent space is 1024-dimensional; the residual stream is 4096–6144-dimensional depending on the block.Source: E Edit Descriptor Feature Injection… · pp. 18–19

Branch 1: cross-attention

  • Cross-attention uses spatial queries to attend to the edit code per token, allowing edits to follow moving content across frames.The branch is suited to spatially selective changes such as object-local color edits, face-region identity swaps, or accessory additions.Source: E Edit Descriptor Feature Injection… · p. 19

Branch 2: FiLM

  • FiLM predicts per-channel scale and shift parameters applied at every token, making it suited to global appearance transforms without spatial selectivity.The branch starts as an identity transformation, with γ initialized to one and β to zero.Source: E Edit Descriptor Feature Injection… · p. 19

Branch 3: additive

  • The additive branch projects the descriptor into learned token slots and fuses it through a low-rank residual path, providing an edit bias for transformations not suited to the other branches.It is the most expressive branch, but also the most prone to overfitting.Source: E Edit Descriptor Feature Injection… · p. 19

Additional results

Gated fusion

  • A learnable scalar gate combines branch outputs and starts at zero, so the wrapped backbone initially matches the pretrained model before the edit pathway opens during training.The zero-initialized gate makes the initial model bit-identical to the pretrained Wan2.1-I2V-14B backbone.Source: E Edit Descriptor Feature Injection… · pp. 19–20

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