One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing

arXiv:2609.04190 · cs.CV, cs.AI · Submitted 2026-09-03 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-03

Updated: 2026-09-03

Code: https://github.com/black-forest-labs/flux

Project page: https://plan-lab.github.io/editvid

Terminology

• Consistency — 27× in this paper · explained in 3 episodes
• Fidelity — 14× in this paper · explained in 2 episodes
• Temporal consistency — 12× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Alignment — 9× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 9× in this paper
• Diffusion Model — 9× in this paper · explained in 8 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Diffusion Models — 8× in this paper · explained in 6 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Training-Free Framework — 8× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Flow Matching — 3× in this paper · explained in 8 episodes
• Personalization — 3× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Optical Flow — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Guided attention — 1× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 1× in this paper
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• DFVEdit: Conditional Delta Flow Vector for Zero-shot Video Editing
• LTX-Video: Realtime Video Latent Diffusion
• ConceptMaster: Multi-Concept Video Customization on Diffusion Transformer Models Without Test-Time Tuning
• StreamEdit: Training-Free Video Editing via Few-Step Streaming Video Generation
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• In-Context Learning with Unpaired Clips for Instruction-based Video Editing
• Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance
• Wan: Open and Advanced Large-Scale Video Generative Models
• InstantID: Zero-shot Identity-Preserving Generation in Seconds
• FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation
• I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

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