# Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion

arXiv: https://arxiv.org/abs/2501.04606 · HTML: https://mlsanity.com/paper/2501.04606/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- First posted on arXiv 8 Jan 2025.

## Placed in

- Diffusion models / [Video generation](https://mlsanity.com/diffusion/video-generation/)

## Built on by (most cited first, 28 of 28)

- [ForgetMe: Evaluating Selective Forgetting in Generative Models](https://mlsanity.com/paper/2504.12574/) (2025)
- [Attention Consistency for LLMs Explanation](https://mlsanity.com/paper/2509.17178/) (2025)
- [TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement](https://mlsanity.com/paper/2503.08168/) (2025)
- [PromptLNet: Region-Adaptive Aesthetic Enhancement via Prompt Guidance in Low-Light Enhancement Net](https://mlsanity.com/paper/2503.08276/) (2025)
- [User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data](https://mlsanity.com/paper/2505.06305/) (2025)
- [A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models](https://mlsanity.com/paper/2503.08199/) (2025)
- [AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation](https://mlsanity.com/paper/2504.05728/) (2025)
- [Learning an Efficient Optimizer via Hybrid-Policy Sub-Trajectory Balance](https://mlsanity.com/paper/2511.00543/) (2025)
- [Music Audio-Visual Question Answering Requires Specialized Multimodal Designs](https://mlsanity.com/paper/2505.20638/) (2025)
- [Learning Sparsity for Effective and Efficient Music Performance Question Answering](https://mlsanity.com/paper/2506.01319/) (2025)
- [A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning](https://mlsanity.com/paper/2506.07236/) (2025)
- [FloorPlan-DeepSeek (FPDS): A multimodal approach to floorplan generation using vector-based next room prediction](https://mlsanity.com/paper/2506.21562/) (2025)
- [Industrial Internet Robot Collaboration System and Edge Computing Optimization](https://mlsanity.com/paper/2504.02492/) (2025)
- [Segment Any Architectural Facades (SAAF):An automatic segmentation model for building facades, walls and windows based on multimodal semantics guidance](https://mlsanity.com/paper/2506.09071/) (2025)
- [MARS: Memory-Enhanced Agents with Reflective Self-improvement](https://mlsanity.com/paper/2503.19271/) (2025)
- [Efficient Temporal Consistency in Diffusion-Based Video Editing with Adaptor Modules: A Theoretical Framework](https://mlsanity.com/paper/2504.16016/) (2025)
- [ArchiLense: A Framework for Quantitative Analysis of Architectural Styles Based on Vision Large Language Models](https://mlsanity.com/paper/2506.07739/) (2025)
- [Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain](https://mlsanity.com/paper/2507.07854/) (2025)
- [TDRI: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generation](https://mlsanity.com/paper/2503.17669/) (2025)
- [Low-Cost Test-Time Adaptation for Robust Video Editing](https://mlsanity.com/paper/2507.21858/) (2025)
- [OMR-Diffusion:Optimizing Multi-Round Enhanced Training in Diffusion Models for Improved Intent Understanding](https://mlsanity.com/paper/2503.17660/) (2025)
- [Contextual Candor: Enhancing LLM Trustworthiness Through Hierarchical Unanswerability Detection](https://mlsanity.com/paper/2506.01104/) (2025)
- [UrbanSense:A Framework for Quantitative Analysis of Urban Streetscapes leveraging Vision Large Language Models](https://mlsanity.com/paper/2506.10342/) (2025)
- [ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes](https://mlsanity.com/paper/2506.14832/) (2025)
- [Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers](https://mlsanity.com/paper/2509.01161/) (2025)
- [JND-Guided Neural Watermarking with Spatial Transformer Decoding for Screen-Capture Robustness](https://mlsanity.com/paper/2603.26766/) (2026)
- [EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control](https://mlsanity.com/paper/2606.28938/) (2026)
- [FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift](https://mlsanity.com/paper/2607.09695/) (2026)

## Abstract (arXiv)

Recent advancements in text-to-image (T2I) generation using diffusion models have enabled cost-effective video-editing applications by leveraging pre-trained models, eliminating the need for resource-intensive training. However, the frame-independence of T2I generation often results in poor temporal consistency. Existing methods address this issue through temporal layer fine-tuning or inference-based temporal propagation, but these approaches suffer from high training costs or limited temporal coherence. To address these challenges, we propose a General and Efficient Adapter (GE-Adapter) that integrates temporal-spatial and semantic consistency with Baliteral DDIM inversion. This framework introduces three key components: (1) Frame-based Temporal Consistency Blocks (FTC Blocks) to capture frame-specific features and enforce smooth inter-frame transitions via temporally-aware loss functions; (2) Channel-dependent Spatial Consistency Blocks (SCD Blocks) employing bilateral filters to enhance spatial coherence by reducing noise and artifacts; and (3) Token-based Semantic Consistency Module (TSC Module) to maintain semantic alignment using shared prompt tokens and frame-specific tokens. Our method significantly improves perceptual quality, text-image alignment, and temporal coherence, as demonstrated on the MSR-VTT dataset. Additionally, it achieves enhanced fidelity and frame-to-frame coherence, offering a practical solution for T2V editing.