VINCIE: Unlocking In-context Image Editing from Video

TL;DR

VINCIE introduces a video-trained diffusion transformer for in-context image editing, enabling high-quality, multi-turn editing and concept composition without task-specific pipelines.

Contribution

The paper presents a scalable video annotation method and a novel transformer model trained on proxy tasks, advancing in-context image editing from videos.

Findings

Achieves state-of-the-art results on multi-turn editing benchmarks.

Demonstrates strong multi-concept composition and story generation abilities.

Operates effectively without task-specific training pipelines.

Abstract

In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pipelines and expert models (e.g., segmentation and inpainting) to curate training data. In this work, we explore whether an in-context image editing model can be learned directly from videos. We introduce a scalable approach to annotate videos as interleaved multimodal sequences. To effectively learn from this data, we design a block-causal diffusion transformer trained on three proxy tasks: next-image prediction, current segmentation prediction, and next-segmentation prediction. Additionally, we propose a novel multi-turn image editing benchmark to advance research in this area. Extensive experiments demonstrate that our model exhibits strong in-context image editing capabilities and achieves state-of-the-art results on two multi-turn image editing benchmarks. Despite being trained exclusively on videos, our model also shows promising abilities in multi-concept composition, story generation, and chain-of-editing applications.