"MFF multimodal dataset"

"We present a novel zero-shot video editing framework that extends pre-trained text-to-image (T2I) models with optical flow coherence enforcement to achieve temporally consistent and high-quality video edits. By seamlessly integrating optical flow into the attention mechanism, our method propagates edits across frames, effectively addressing challenges such as motion and deformation while minimizing artifacts common in frame-wise editing. A key innovation of our approach is its ability to condition edits on both text prompts and reference images, enabling precise and flexible control over style and content. It supports diverse editing tasks, including text-based editing, image-to-video style transfer, and a combination of the two. Compared to existing (text-only) video editing tools, it improves temporal coherence and visual fidelity significantly, achieving state-of-the-art results in zero-shot settings."