Source-linked AI summary
GRNEdit: Efficient General Video Editing from a New Binary-Evidence Perspective in Generative Refinement Networks
Feng Xie, Jiagao Hu, Fuhao Li, Zepeng Wang, Yuxuan Chen, Dahua Gao, Fei Wang, Daiguo Zhou
TL;DR
General video editing needs a more efficient alternative to resource-intensive source conditioning. GRNEdit uses binary evidence with identity-aligned supervision and achieves competitive or leading results on OpenVE-Bench and ReCo-Bench.
Problem
Existing video editors rely on costly conditioning mechanisms that must interpret editing intent and construct backbone-compatible source representations.
Method
GRNEdit models editing as source-referenced binary retain-or-flip decisions, using compact evidence modules and identity-aligned null supervision across two stages.
Results
With 0.6M training pairs and conditioning overhead below 3% of backbone size, GRNEdit achieves competitive or leading results on OpenVE-Bench and ReCo-Bench.
Takeaways & Limitations
The results support binary evidence as an efficient inductive bias for diverse video edits while decoupling editing control from content synthesis.
Takeaways & Limitations
GRNEdit remains limited for introducing new semantics with little source evidence, such as object addition, and depends on binary backbones.
Abstract
from arXiv · showhide
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.
1 Introduction
GRNEdit reframes general video editing as lightweight, coordinate-wise binary evidence modeling, leaving global content synthesis to the GRN backbone. Its two-stage design uses source reconstruction to establish an identity reference, then refines unresolved target-bit decisions efficiently.
- Motivation: Existing editors require heavyweight branches or costly source concatenation to interpret intent and produce backbone-compatible, target-aligned corrections.These conditioning mechanisms trade representation alignment against training and inference cost.
- Binary evidence perspective: GRNEdit treats aligned source and target codes as local retain-or-flip decisions, reducing the conditioning path’s local search burden.GRN predicts hierarchical binary codes by repeatedly refining the full bit map, while source coordinates provide observed binary states.
- Stage I: Stage I maps source bits into lightweight evidence messages, while instruction-aware modulation and GRN likelihoods determine which states to retain or overturn.The conditioning path learns source relevance, leaving target synthesis to the pretrained backbone.
- Identity anchoring: An empty instruction is defined as no edit and supervised through source reconstruction, strengthening preservation semantics and connecting edited and identity states.This redesign of classifier-free null conditioning sharpens edit/no-edit semantics and improves source consistency.
- Stage II: Stage II compares edited and source-preserving states with a Bit-Margin Router, revising unresolved target-bit logits using GRN’s native likelihood objective.It converts the identity reference into targeted revisions of residual errors after Stage I is frozen.
2 Related Work
Related work spans continuous and discrete video generation, instruction-based editing benchmarks and methods, and increasingly efficient source-conditioning designs. GRN distinguishes itself through global random refinement of hierarchical binary states rather than fixed generation orders.
- Video generation: GRN repeatedly revisits the full binary state through global random refinement, unlike visual autoregressive models that typically follow fixed token- or scale-wise orders.GRN combines discrete HBQ prediction with global random refinement.
- Video generation: Diffusion and flow models use continuous VAE latents, whereas VQ-based models predict discrete codebook indices and bitwise representations replace multiway prediction with binary decisions.Infinity introduces bitwise autoregression, while GRN extends hierarchical binary quantization to images and videos.
- Video generation: GRN reports 0.56 vs. 0.87 rFID on ImageNet, matching or surpassing continuous VAEs despite higher compression.The passage presents this as evidence for the reconstruction quality of hierarchical binary quantization.
- Instruction-based video editing: Instruction-based video editing covers object insertion, removal, replacement, background modification, and stylization, supported by paired datasets and benchmarks for instruction following, source preservation, and temporal quality.Named resources include InsV2V, InsViE-1M, Ditto, OpenVE-3M, OpenVE-Bench, and IVEBench.
- Source conditioning: Source conditioning includes latent fusion, source-token concatenation, multimodal conditioning, shared source/edit streams, explicit branches, adapters, shared weights, and LoRA.The cited methods include InstructPix2Pix, InsViE, EasyV2V, Omni-Video, ICVE, ControlNet, and VACE.
3 Method
GRNEdit reframes general video editing as source-referenced prediction of binary retain-or-flip decisions within GRN’s native refinement process. Its two-stage design injects instruction-aware source evidence, anchors an identity-preserving null path, and revises edited bit margins against that aligned reference.
- Formulation: GRNEdit represents editing as source-referenced target-bit prediction, with each edit encoded as a coordinate-wise retain-or-flip decision in GRN’s binary code space.The edit map is the bitwise difference between source and target codes, preserving the global representation while anchoring each target decision to the source.
- Formulation: Source evidence is learned through its effect on source-aligned bit margins, while GRN remains responsible for resolving those local signals into globally coherent target synthesis.The conditioning path receives supervision only from final target-bit likelihood, without a separate feature target.
- Identity-Aligned Null Condition: An empty instruction is trained as an identity operation: the conditional path predicts target bits, while the null path reconstructs source bits from source states.This CFG-inspired identity supervision strengthens content preservation and creates a source-preserving state in the same coordinate-aligned representation space.
- Stage I: Layerwise Source Evidence Injection: Stage I injects instruction-aware source messages at the input and selected Transformer boundaries, preserving GRN’s packed sequence, full attention, and native bit head.Source bits are adapted to each chunk’s hidden basis, with visual-span-restricted additions and instruction-dependent gating and residual scaling.
- Stage II: Identity-Referenced Bit-Margin Revision: Stage II freezes Stage I and uses its clean identity endpoint as a preservation reference for residual bit-margin revision, avoiding a separate encoder.The Bit-Margin Router predicts a bounded per-bit coefficient on the discrepancy between edited and identity-reference margins; zero initialization leaves Stage I unchanged.
4 Experiments
GRNEdit achieves strong, efficient video-editing performance with lightweight conditioning on OpenVE-Bench and robust transfer to ReCo-Bench. Ablations attribute its gains to evidence-based conditioning, identity-aligned null supervision, selective injection, and Stage II refinement.
- Main results: With 37M conditioning parameters, GRNEdit-2B scores 4.03 Overall, while GRNEdit-8B reaches 4.18 with 45M conditioning parameters and matches the best open-source score.Their per-video inference times are 39 s and 84 s, respectively.
- Main results: GRNEdit ranks first across all ReCo-Bench Remove components and reaches 9.08 on Style with the best S_VN and S_VQ.The benchmark evaluates motion naturalness, temporal stability, and edit stability beyond the OpenVE-derived training distribution.
- Evidence analysis: Positive evidence values retain source bits whereas negative values flip them, directly acting on the backbone’s binary decision axis rather than serving as unordered residual features.Heatmaps visualize the direction and strength of source-induced changes in target-bit margins.
- Efficiency and convergence: Evidence conditioning converges faster than VACE-style conditioning on both GRN and Infinity while using fewer additional parameters; on GRN, it reaches 3.70 after 2K updates and surpasses VACE at 16K by 6K.The VACE-style comparison uses architecture-compatible implementations, with a branch over 100× smaller in the reported GRN comparison.
- Ablations: Endpoint injection gives the best Overall, G.Style, and L.Chg. scores at 4.03/4.36/3.93, while removing the MLLM reprompt lowers Overall from 4.03 to 3.97.Stage II adds only 30M parameters and revises unresolved details using the discrepancy between edited generation and the same-space source reference.
- Ablations: Using 5% and 10% identity samples raises Overall from 3.97 without identity supervision to 4.01 and 4.03, respectively.Identity-aligned null supervision provides an edit/no-edit anchor, supports content preservation, and supplies the source reference connecting Stage I and Stage II.
5 Conclusion
GRNEdit presents general video editing as source-referenced binary decisions, decoupling editing control from content synthesis. Its compact evidence modules and identity-aligned null supervision support content preservation and residual bit-margin revision with low training and conditioning costs.
- 5 Conclusion: GRNEdit is a lightweight two-stage framework that casts general video editing as source-referenced binary decisions.This formulation decouples editing control from content synthesis.
- 5 Conclusion: Compact source-evidence modules guide target-bit decisions, while identity-aligned null supervision strengthens content preservation.The null pathway also provides an intrinsic reference for residual bit-margin revision.
- 5 Conclusion: 0.6M training pairs and conditioning overhead below 3% of backbone size yield competitive or leading results on OpenVE-Bench and ReCo-Bench.These results support the framework's efficiency claims.
CONTENTS 9 SECTIONS · APPENDIX
The appendix contains sections on reproducibility details and on data preparation, text conditioning, and MLLM reprompting rules.
- APPENDIX: The appendix includes a section on model architectures and training configurations.This section is labeled “A1.1 Reproducibility Details.”
- APPENDIX: The reproducibility section is specifically titled “A1.1 Reproducibility Details.”Its stated scope covers model architectures and training configurations.
- APPENDIX: Model architectures are identified as a topic within the reproducibility details.The passage lists them alongside training configurations.
- APPENDIX: Training configurations are identified as a topic within the reproducibility details.The passage lists them alongside model architectures.
- APPENDIX: The appendix includes a section on data preparation, text conditioning, and MLLM reprompting rules.This section is labeled “A1.2 Data Preparation, Text Conditioning, and MLLM Reprompting Rules.”
- APPENDIX: The data-preparation section is specifically titled “A1.2 Data Preparation, Text Conditioning, and MLLM Reprompting Rules.”Its stated scope combines three topics.
- APPENDIX: Text conditioning is identified as a topic in the appendix’s A1.2 section.The passage lists it with data preparation and MLLM reprompting rules.
- APPENDIX: MLLM reprompting rules are identified as a topic in the appendix’s A1.2 section.The passage lists them with data preparation and text conditioning.
A2 BINARY EVIDENCE ANALYSIS
The section analyzes binary evidence through ordered support for binary decisions, attention comparisons against Wan 2.1, and progressive editing guidance.
- A2.1 Why Call It Evidence? Ordered Support for Binary Decisions: It explains why GRNEdit’s binary representation is interpreted as evidence, emphasizing ordered support for binary decisions.
- A2.2 Attention Comparison: Evidence-Guided GRNEdit vs. Wan 2.1: It compares evidence-guided GRNEdit’s attention with Wan 2.1.
- A2.3 How Binary Evidence Guides Progressive Editing: It examines how binary evidence guides progressive editing.
A3 ADDITIONAL RESULTS & LIMITATIONS
This section presents additional visualization results across different tasks and discusses the method’s limitations and failure cases.
- A3.1 More Visualization Results across Different Tasks: The section reports more visualization results across different tasks.
- A3.2 Limitations and Failure Cases: It also examines limitations and failure cases.
A4 USER STUDY
The section presents a user study measuring human preference across six evaluation dimensions, alongside supplementary presentation material.
- A4.1 User Study: Human Preference across Six Evaluation Dimensions: The user study evaluates human preference across six evaluation dimensions.
- No quantitative results or comparative findings are reported in the supplied passages.
- The section also includes supplementary presentation material.
A1.1 Reproducibility Details: Model Architectures and Training Configurations … OVERALL PREFERENCE
GRNEdit combines lightweight binary-evidence conditioning with progressive refinement, broad editing demonstrations, explicit failure analysis, and user-study validation. Its main limitations concern source-absent additions and weak physical integration, while preference results favor it for edit success, artifact control, and source consistency.
- A1.1 Reproducibility Details: Model Architectures and Training Configurations: GRNEdit adds 62.614M and 156.379M parameters for its 2B and 8B models, respectively, corresponding to ~ 3.1% and ~ 2.0% of the main trunk.Stage I source modules contribute 24.855M and 37.873M parameters, while Stage II Bit-Margin Router contributes 37.758M and 118.506M.
- A1.2 Data Preparation, Text Conditioning, and MLLM Reprompting Rules: MLLM reprompting uses randomly selected Qwen3.5-Flash or Qwen3-VL-Flash to describe only the plausible final video state.Prompt rules require final-state nouns and adjectives and adapt descriptions to background, style, addition, and local-change instructions.
- A1.3 GRN Training Curves: GRNEdit-2B uses 40K training steps and GRNEdit-8B uses 80K training steps for the main-paper results.GRN-2B-VACE uses 20K steps only for the early-stage convergence-efficiency comparison.
- A2.1 Why Call It Evidence? Ordered Support for Binary Decisions: Binary evidence controls source-aligned retain-or-flip decisions, with signed margin change indicating direction and RMS magnitude indicating spatial strength.Support acts on native backbone decisions rather than transient routing weights.
- A2.2 Attention Comparison: Evidence-Guided GRNEdit vs. Wan 2.1: Attention preserves source structure and isolates regions requiring synthesis, while evidence determines what to preserve or revise and attention determines how it fits generation.This complementary interaction supports sharper task separation across background, style, and local edits.
- A2.3 How Binary Evidence Guides Progressive Editing: Editing intent appears early, while intermediate iterations resolve semantic structure and later iterations stabilize identity, geometry, and texture.The highlighted visualization reports RMS Δ-margin, where brighter regions indicate stronger influence and darker regions indicate released edit regions.
- A3.1 More Visualization Results across Different Tasks: The model generalizes from object removal to text removal despite excluding subtitle-related editing samples from training.Visualization results cover style transfer, background replacement, object addition, and local replacement tasks.
- A3.2 Limitations and Failure Cases: ADD is structurally harder because target semantics are absent from the source, and overlay-like supervision weakly constrains natural integration.Observed failures include under-rendered or detached objects and inconsistencies in scale, depth, contact, occlusion, illumination, shadows, and reflections.