Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely on resource-intensive conditioning, using either heavyweight branches or costly source concatenation. Is there any efficient way to model editing intent? Thus, we introduce GRNEdit, a lightweight two-stage framework. GRN inspires our approach by encoding visual semantics through combinations of bits. Through task-specific fine-tuning, we take this representation further and recast editing semantics as local retain-or-flip decisions over individual bits. Source information is consequently modeled as coordinate-wise evidence supporting the observed binary states, while the GRN backbone remains responsible for resolving their global composition into coherent generative semantics. In Stage I, a compact encoder translates discrete source codes into continuous evidence signals, which GRN assimilates throughout binary refinement. Inspired by null-prompt training for classifier-free guidance, we further assign the null condition an editing-specific meaning: an empty instruction denotes no edit and is supervised through source reconstruction. This identity pathway not only implicitly strengthens evidence utilization and content preservation in Stage I, but also produces a source-preserving state in the same representation space as the edited state. Stage II can therefore directly compare each edited state with its source-preserving counterpart and use their discrepancy to revise unresolved target-bit decisions. Trained on only 0.6M pairs with less than 3% conditioning parameters, GRNEdit-2B and GRNEdit-8B achieve scores of 4.03 and 4.18 on OpenVE-Bench. The 2B model outperforms multiple 14B open-source editors, while the 8B model performs on par with leading open-source editors.
Figures & tables
Figure 1: Continuous and codebook latents require predicting both edit locations and target values/indices, whereas each binary bit has a unique alternative state, enabling lightweight localization-only control.
Figure 2: Overview of GRNEdit. Given a source video and an editing instruction, Stage I injects prompt-modulated source evidence into the GRN chunks through lightweight per-chunk projectors, each containing only about 3 M parameters for GRNEdit-2B and 7 M for GRNEdit-8B. Stage II refines the binary predictions by treating source evidence as support in keep regions and as counter-evidence in edit regions.
Figure 3: Qualitative comparison across diverse editing tasks. GRNEdit performs more accurate edits while better preserving unedited content, whereas competing methods often miss the intended change or disrupt scene consistency.
Method
Params / Time
Overall
G.Style
Bkg.Chg.
L.Chg.
L.Rem.
L.Add.
Sub.
Cond. Params
Runway Aleph (Commercial)
Commercial
4.49
4.41
4.40
4.31
4.64
4.36
4.21
N/A
VACE (ICCV 2025)
14B /
545s
3.01
3.46
2.81
2.47
3.99
1.76
4.41
3.05B
Omni-Video (Tech. Rep. 2025)
11B /
312s
3.66
3.41
4.11
3.75
4.52
2.80
4.95
>4.6 B
InsViE (ICCV 2025)
2B /
64s
3.25
3.63
2.68
2.82
3.56
2.25
4.77
∼ 59M
Lucy-Edit (Tech. Rep. 2025)
5B /
36s
3.77
3.64
3.25
3.93
3.95
3.92
4.23
∼ 0.6M
Kiwi-Edit (arXiv 2026)
8B /
45s
4.17
4.24
4.01
4.30
4.60
4.13
4.23
3B
Table 1: Comparison on OpenVE-Bench. Higher is better. Cond. Params counts structural parameters beyond the listed generator.
Add
Remove
Replace
Style
Method
SEA
SVN
SVQ
S
SEA
SVN
SVQ
S
SEA
SVN
SVQ
S
SEA
SVN
SVQ
S
InsViE
2.60
3.10
3.46
3.05
2.44
3.76
3.29
3.16
2.10
3.91
3.49
3.17
8.17
8.21
7.35
7.91
Lucy-Edit
6.47
5.70
6.77
6.31
7.02
6.88
6.81
6.90
7.08
6.21
6.88
6.72
4.65
4.67
5.17
4.83
DITTO
6.70
7.57
8.41
7.56
5.48
6.67
6.93
6.36
4.56
7.21
7.96
6.58
9.20
9.07
8.77
9.01
ReCo
8.54
7.55
8.61
8.23
7.28
6.90
6.82
7.00
9.43
8.01
8.77
8.74
9.42
9.19
8.90
9.17
OURS-2B
7.53
6.91
7.44
7.29
8.39
7.76
7.81
7.99
8.60
7.24
7.99
7.94
9.03
9.23
8.97
9.08
Table 2: Comparison on ReCo-Bench across four editing tasks. Results are ranked within each task; higher is better.
Figure 4: Stage II residual evidence refinement. Comparing the Stage I edited state with its identity-aligned source reference restores source-consistent details in preserved regions.
Configuration
Overall
G.Style
L.Chg.
Sub.
(a) Source-evidence injection placement
Head only (1)
3.88
4.03
3.68
4.87
First + last chunks (2)
4.03
4.36
3.93
4.72
Uniform 3 chunks
3.97
4.23
3.77
4.74
All 7 chunks
3.95
4.29
3.78
4.62
(b) Identity supervision ratio
Table 3: Ablation studies on OpenVE-Bench using the 2B backbone. Results are ranked within each controlled group.
Figure 5: Why call it evidence? The heatmaps visualize source-induced changes in target-bit margins. Unlike conventional residual representations, source evidence provides measurable, and ordered support for binary editing decisions.
Figure 6: Under a fixed two-injection budget, we retain the first and last chunks, which exhibit the strongest and temporally complementary residual utilization in the fully model.
Figure 7: Cross-backbone convergence on OpenVE-Bench. Under matched settings within each backbone, evidence conditioning converges faster than VACE-style conditioning on both GRN and Infinity, while using fewer additional parameters. Marker size denotes additional parameters.
Instruction-based video editing is a natural way to control video content with text, but adapting a video generation model into an editor usually appears data-hungry. At the same time, high-quality video editing data remains scarce. In this paper, we show that a video generation backbone can become a strong video editor without large scale video editing data. We present InsEdit, an instruction-based editing model built on HunyuanVideo-1.5. InsEdit combines a visual editing architecture with a video data pipeline based on Mutual Context Attention (MCA), which creates aligned video pairs where edits can begin in the middle of a clip rather than only from the first frame. With only O(100)K video editing data, InsEdit achieves state-of-the-art results among open-source methods on our video instruction editing benchmarks. In addition, because our training recipe also includes image editing data, the final model supports image editing without any modification.
Zhefan Rao, Bin Zou, Haoxuan Che +5
The Hong Kong University of Science and Technology · Celia Research HK · City University of Hong Kong
Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guided editing within a single unified framework remains challenging. We introduce EditVid, a training-free framework combining sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. The same framework supports instruction-guided and reference-guided edits, including style transfer, attribute modification, object insertion, part-level editing, and subject replacement. On FiVE, EditVid achieves 78.16 FiVE-Acc, compared with 58.95 for the strongest evaluated training-free baseline, while obtaining competitive results on IVEBench. A user study further shows a 51.8% overall preference for EditVid over 7 competing methods.
Adheesh Sunil Juvekar, Onkar Kishor Susladkar, Kiet A. Nguyen +6
Building a capable video editor remains significantly harder than a video generator: editing requires (source, instruction, edited) triplets that are prohibitively expensive to annotate and difficult to synthesize at scale, whereas image editing has already reached maturity with millions of such pairs readily available. In this work, we introduce VINCIE-NExT, a unified framework that transfers editing capability from images to videos through in-context visual demonstrations, alleviating the need for large-scale paired video editing data. VINCIE-NExT decomposes video editing into a structured chain of composable sub-tasks (Video -> Image -> Image -> Video), routing editing intent through the image domain and enabling scalable joint training from heterogeneous image and video corpora under a unified diffusion objective. An image editing pair, synthesized by the model or supplied by the user, is prepended as an in-context visual demonstration that serves as a spatial appearance blueprint for every output frame. To ground appearance edits across the interleaved context, we introduce a novel position encoding that links image demonstrations and video frames in a shared spatial coordinate system, enabling pixel-faithful propagation of appearance changes to every output frame. Chain-of-Editing further provides principled test-time scaling: by executing the sub-task chain as progressive diffusion stages, editing quality can be improved by investing additional compute without retraining. Comprehensive experiments on OpenVE-Bench demonstrate the state-of-the-art performance across diverse editing categories, with ablations confirming the effectiveness of each component.
Leigang Qu, Feng Cheng, Ziyan Yang +7
National University of Singapore · ByteDance Seed · University of Science and Technology of China