title: MoViE: Mobile Diffusion for Video Editing
summary: Recent progress in diffusion-based video editing has shown remarkable
  potential for practical applications. However, these methods remain
  prohibitively expensive and challenging to deploy on mobile devices. In this
  study, we introduce a series of optimizations that render mobile video editing
  feasible. Building upon the existing image editing model, we first optimize its
  architecture and incorporate a lightweight autoencoder. Subsequently, we extend
  classifier-free guidance distillation to multiple modalities, resulting in a
  threefold on-device speedup. Finally, we reduce the number of sampling steps to
  one by introducing a novel adversarial distillation scheme which preserves the
  controllability of the editing process. Collectively, these optimizations
  enable video editing at 12 frames per second on mobile devices, while
  maintaining high quality. Our results are available at
  https://qualcomm-ai-research.github.io/mobile-video-editing/
paper_authors._id: 675985c7b51431a566ca75f9
paper_authors.name: Adil Karjauv
paper_authors.status: admin_assigned
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paper_authors.fullname: Adil Karjauv
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paper_authors.user: mikolez
paper_authors._id: 675985c7b51431a566ca75fa
paper_authors.name: Noor Fathima
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paper_authors.fullname: Noor Fathima
paper_authors.type: user
paper_authors.user: Noor000
paper_authors._id: 675985c7b51431a566ca75fb
paper_authors.name: Ioannis Lelekas
paper_authors._id: 675985c7b51431a566ca75fc
paper_authors.name: Fatih Porikli
paper_authors._id: 675985c7b51431a566ca75fd
paper_authors.name: Amir Ghodrati
paper_authors.status: claimed_verified
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paper_authors.fullname: Amir
paper_authors.type: user
paper_authors.user: aghodrati
paper_authors._id: 675985c7b51431a566ca75fe
paper_authors.name: Amirhossein Habibian
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-11T13:39:07.789Z
paper_authors._id: 628372aa09aa80237c6d2044
paper_authors.avatarUrl: /avatars/c1e34dee217d73f5539a0807adc12c80.svg
paper_authors.fullname: Amir Habibian
paper_authors.type: user
paper_authors.user: habibian
paper_title: MoViE: Mobile Diffusion for Video Editing
paper_summary: Recent progress in diffusion-based video editing has shown remarkable
  potential for practical applications. However, these methods remain
  prohibitively expensive and challenging to deploy on mobile devices. In this
  study, we introduce a series of optimizations that render mobile video editing
  feasible. Building upon the existing image editing model, we first optimize its
  architecture and incorporate a lightweight autoencoder. Subsequently, we extend
  classifier-free guidance distillation to multiple modalities, resulting in a
  threefold on-device speedup. Finally, we reduce the number of sampling steps to
  one by introducing a novel adversarial distillation scheme which preserves the
  controllability of the editing process. Collectively, these optimizations
  enable video editing at 12 frames per second on mobile devices, while
  maintaining high quality. Our results are available at
  https://qualcomm-ai-research.github.io/mobile-video-editing/
paper_ai_summary: Optimizations including a lightweight autoencoder, classifier-free guidance distillation, and an adversarial distillation scheme enable high-speed, high-quality video editing on mobile devices.
paper_ai_keywords: diffusion-based video editing lightweight autoencoder classifier-free guidance distillation adversarial distillation mobile devices