# EffiVED:Efficient Video Editing via Text-instruction Diffusion Models

arXiv: https://arxiv.org/abs/2403.11568 · HTML: https://mlsanity.com/paper/2403.11568/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Placed in Video generation, one of 14 directions in Diffusion models.
- Builds on 54 of its 59 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Placed in

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

## Builds on (most cited first, 30 of 54)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Cascaded Diffusion Models for High Fidelity Image Generation](https://mlsanity.com/paper/2106.15282/) (2021)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)

## Built on by (most cited first, 21 of 21)

- [Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset](https://mlsanity.com/paper/2510.15742/) (2025)
- [InsViE-1M: Effective Instruction-based Video Editing with Elaborate Dataset Construction](https://mlsanity.com/paper/2503.20287/) (2025)
- [OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing](https://mlsanity.com/paper/2512.07826/) (2025)
- [VEGGIE: Instructional Editing and Reasoning Video Concepts with Grounded Generation](https://mlsanity.com/paper/2503.14350/) (2025)
- [In-Context Learning with Unpaired Clips for Instruction-based Video Editing](https://mlsanity.com/paper/2510.14648/) (2025)
- [FiVE: A Fine-grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models](https://mlsanity.com/paper/2503.13684/) (2025)
- [EasyV2V: A High-quality Instruction-based Video Editing Framework](https://mlsanity.com/paper/2512.16920/) (2025)
- [Lynx: Towards High-Fidelity Personalized Video Generation](https://mlsanity.com/paper/2509.15496/) (2025)
- [Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning](https://mlsanity.com/paper/2512.09924/) (2025)
- [Evolution of Video Generative Foundations](https://mlsanity.com/paper/2604.06339/) (2026)
- [FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing](https://mlsanity.com/paper/2601.01720/) (2026)
- [Text-Driven Reasoning Video Editing via Reinforcement Learning on Digital Twin Representations](https://mlsanity.com/paper/2511.14100/) (2025)
- [SteerVTE: Seamless Video Text Editing with Style and Glyph Control](https://mlsanity.com/paper/2606.23254/) (2026)
- [CoT-Edit: Let CoT Guide Instruction Video Editing](https://mlsanity.com/paper/2608.01113/) (2026)
- [Instruction-Based Video Editing by Repurposing an Image Editing Model](https://mlsanity.com/paper/2608.14790/) (2026)
- [Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning](https://mlsanity.com/paper/2511.02194/) (2025)
- [SneakPeek: Future-Guided Instructional Streaming Video Generation](https://mlsanity.com/paper/2512.13019/) (2025)
- [EgoPlay: Event-Triggered Video Editing for Egocentric Streams](https://mlsanity.com/paper/2607.24560/) (2026)
- [PRIMED: Adaptive Modality Suppression for Referring Audio-Visual Segmentation via Biased Competition](https://mlsanity.com/paper/2605.07154/) (2026)
- [Reasoning to Align: Implicit Reasoning in Diffusion Transformers for Video Editing](https://mlsanity.com/paper/2605.24674/) (2026)
- [Joint and Cross-Modal Video-Audio Generation and Editing: A Unified Formulation and Design Taxonomy](https://mlsanity.com/paper/2609.34381/) (2026)

## Abstract (arXiv)

Large-scale text-to-video models have shown remarkable abilities, but their direct application in video editing remains challenging due to limited available datasets. Current video editing methods commonly require per-video fine-tuning of diffusion models or specific inversion optimization to ensure high-fidelity edits. In this paper, we introduce EffiVED, an efficient diffusion-based model that directly supports instruction-guided video editing. To achieve this, we present two efficient workflows to gather video editing pairs, utilizing augmentation and fundamental vision-language techniques. These workflows transform vast image editing datasets and open-world videos into a high-quality dataset for training EffiVED. Experimental results reveal that EffiVED not only generates high-quality editing videos but also executes rapidly. Finally, we demonstrate that our data collection method significantly improves editing performance and can potentially tackle the scarcity of video editing data. Code can be found at https://github.com/alibaba/EffiVED.