FADE edits real videos by guiding sampling with low-frequency attention-output differences from the first blocks of a text-to-video diffusion model, enabling training-free appearance and motion edits.
Masactrl: Tuning-free mu- tual self-attention control for consistent image synthesis and editing
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FADE: Frequency-Aware Diffusion Model Factorization for Video Editing
FADE edits real videos by guiding sampling with low-frequency attention-output differences from the first blocks of a text-to-video diffusion model, enabling training-free appearance and motion edits.