title: Video Editing via Factorized Diffusion Distillation
summary: We introduce Emu Video Edit (EVE), a model that establishes a new
  state-of-the art in video editing without relying on any supervised video
  editing data. To develop EVE we separately train an image editing adapter and a
  video generation adapter, and attach both to the same text-to-image model.
  Then, to align the adapters towards video editing we introduce a new
  unsupervised distillation procedure, Factorized Diffusion Distillation. This
  procedure distills knowledge from one or more teachers simultaneously, without
  any supervised data. We utilize this procedure to teach EVE to edit videos by
  jointly distilling knowledge to (i) precisely edit each individual frame from
  the image editing adapter, and (ii) ensure temporal consistency among the
  edited frames using the video generation adapter. Finally, to demonstrate the
  potential of our approach in unlocking other capabilities, we align additional
  combinations of adapters
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paper_authors.name: Uriel Singer
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paper_authors.fullname: Uriel Singer
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paper_authors.name: Amit Zohar
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paper_authors.fullname: Amit Zohar
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paper_authors.name: Yuval Kirstain
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paper_authors.fullname: Yuval Kirstain
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paper_authors.name: Shelly Sheynin
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paper_authors.fullname: Shelly Sheynin
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paper_authors.name: Adam Polyak
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paper_authors.fullname: Adam Polyak
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paper_authors.user: adampo
paper_authors._id: 65f3b141d09b5f817d5d85ab
paper_authors.name: Devi Parikh
paper_authors.status: admin_assigned
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paper_authors._id: 6340753995c20b9447379c67
paper_authors.avatarUrl: /avatars/f793c00a98335cd8f7efce41d403da16.svg
paper_authors.fullname: Devi Parikh
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paper_authors._id: 65f3b141d09b5f817d5d85ac
paper_authors.name: Yaniv Taigman
paper_title: Video Editing via Factorized Diffusion Distillation
paper_summary: We introduce Emu Video Edit (EVE), a model that establishes a new
  state-of-the art in video editing without relying on any supervised video
  editing data. To develop EVE we separately train an image editing adapter and a
  video generation adapter, and attach both to the same text-to-image model.
  Then, to align the adapters towards video editing we introduce a new
  unsupervised distillation procedure, Factorized Diffusion Distillation. This
  procedure distills knowledge from one or more teachers simultaneously, without
  any supervised data. We utilize this procedure to teach EVE to edit videos by
  jointly distilling knowledge to (i) precisely edit each individual frame from
  the image editing adapter, and (ii) ensure temporal consistency among the
  edited frames using the video generation adapter. Finally, to demonstrate the
  potential of our approach in unlocking other capabilities, we align additional
  combinations of adapters
paper_ai_summary: Emu Video Edit (EVE) uses unsupervised distillation, specifically Factorized Diffusion Distillation, to perform video editing by aligning image editing and video generation adapters without needing supervised data.
paper_ai_keywords: text-to-image model Factorized Diffusion Distillation