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Latent Diffusion Models (LDMs) are renowned for their powerful capabilitiesin image and video synthesis. Yet, video editing methods suffer frominsufficient pre-training data or video-by-video re-training cost. Inaddressing this gap, we propose FLDM (Fused Latent Diffusion Model), atraining-free framework to achieve text-guided video editing by applyingoff-the-shelf image editing methods in video LDMs. Specifically, FLDM fuseslatents from an image LDM and an video LDM during the denoising process. Inthis way, temporal consistency can be kept with video LDM while high-fidelityfrom the image LDM can also be exploited. Meanwhile, FLDM possesses highflexibility since both image LDM and video LDM can be replaced so advancedimage editing methods such as InstructPix2Pix and ControlNet can be exploited.To the best of our knowledge, FLDM is the first method to adapt off-the-shelfimage editing methods into video LDMs for video editing. Extensive quantitativeand qualitative experiments demonstrate that FLDM can improve the textualalignment and temporal consistency of edited videos.

Fuse Your Latents: Video Editing with Multi-source Latent Diffusion Models