LOVECon: Text-driven Training-free Long Video Editing with ControlNet

Abstract

Leveraging pre-trained conditional diffusion models for video editing without further tuning has gained increasing attention due to its promise in film production, advertising, etc. Yet, seminal works in this line fall short in generation length, temporal coherence, or fidelity to the source video. This paper aims to bridge the gap, establishing a simple and effective baseline for training-free diffusion model-based long video editing. As suggested by prior arts, we build the pipeline upon ControlNet, which excels at various image editing tasks based on text prompts. To break down the length constraints caused by limited computational memory, we split the long video into consecutive windows and develop a novel cross-window attention mechanism to ensure the consistency of global style and maximize the smoothness among windows. To achieve more accurate control, we extract the information from the source video via DDIM inversion and integrate the outcomes into the latent feature maps of the generations. We also incorporate a video frame interpolation model to mitigate frame-level flickering issues further. Extensive empirical studies verify the superior efficacy of our method over competing baselines across scenarios, including replacing attributes of foreground objects, style transfer, and background replacement. In particular, our method manages to edit videos with up to 128 frames according to user requirements.