TIGER-AI-Lab/AnyV2V
Code and data for "AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks" [TMLR 2024]
Leverages image-to-video (I2V) diffusion models to reduce video editing to single-frame image editing, enabling diverse editing tasks (stylization, object manipulation, semantic changes) through plug-and-play integration with any image editing method. Uses latent space DDIM inversion and PnP guidance to propagate first-frame edits temporally while maintaining appearance and motion consistency across frames. Supports multiple I2V backbones (i2vgen-xl, ConsistI2V, SEINE) with modular architecture compatible with InstantStyle, InstructPix2Pix, and other image editors.
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MIT
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Last pushed
Oct 29, 2024
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