title: ReNoise: Real Image Inversion Through Iterative Noising
summary: Recent advancements in text-guided diffusion models have unlocked powerful
  image manipulation capabilities. However, applying these methods to real images
  necessitates the inversion of the images into the domain of the pretrained
  diffusion model. Achieving faithful inversion remains a challenge, particularly
  for more recent models trained to generate images with a small number of
  denoising steps. In this work, we introduce an inversion method with a high
  quality-to-operation ratio, enhancing reconstruction accuracy without
  increasing the number of operations. Building on reversing the diffusion
  sampling process, our method employs an iterative renoising mechanism at each
  inversion sampling step. This mechanism refines the approximation of a
  predicted point along the forward diffusion trajectory, by iteratively applying
  the pretrained diffusion model, and averaging these predictions. We evaluate
  the performance of our ReNoise technique using various sampling algorithms and
  models, including recent accelerated diffusion models. Through comprehensive
  evaluations and comparisons, we show its effectiveness in terms of both
  accuracy and speed. Furthermore, we confirm that our method preserves
  editability by demonstrating text-driven image editing on real images.
paper_authors._id: 65fd1063c3f470c772e18d54
paper_authors.name: Daniel Garibi
paper_authors.status: claimed_verified
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paper_authors.fullname: Daniel Garibi
paper_authors.type: user
paper_authors.user: garibida
paper_authors._id: 65fd1063c3f470c772e18d55
paper_authors.name: Or Patashnik
paper_authors.status: claimed_verified
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paper_authors.fullname: Or Patashnik
paper_authors.type: user
paper_authors.user: orpatashnik
paper_authors._id: 65fd1063c3f470c772e18d56
paper_authors.name: Andrey Voynov
paper_authors.status: admin_assigned
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paper_authors._id: 630551fd80bc5e03dad21ea6
paper_authors.avatarUrl: /avatars/849e41404df698cc89c68939de45ec9a.svg
paper_authors.fullname: Andrey Voynov
paper_authors.type: user
paper_authors.user: avoin
paper_authors._id: 65fd1063c3f470c772e18d57
paper_authors.name: Hadar Averbuch-Elor
paper_authors._id: 65fd1063c3f470c772e18d58
paper_authors.name: Daniel Cohen-Or
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-22T10:19:42.816Z
paper_authors._id: 628507161949ebcae8e24ec3
paper_authors.avatarUrl: /avatars/008ecb3daa4c8187b5f339f1176b3c39.svg
paper_authors.fullname: Daniel Cohen-Or
paper_authors.type: user
paper_authors.user: cohenor
paper_title: ReNoise: Real Image Inversion Through Iterative Noising
paper_summary: Recent advancements in text-guided diffusion models have unlocked powerful
  image manipulation capabilities. However, applying these methods to real images
  necessitates the inversion of the images into the domain of the pretrained
  diffusion model. Achieving faithful inversion remains a challenge, particularly
  for more recent models trained to generate images with a small number of
  denoising steps. In this work, we introduce an inversion method with a high
  quality-to-operation ratio, enhancing reconstruction accuracy without
  increasing the number of operations. Building on reversing the diffusion
  sampling process, our method employs an iterative renoising mechanism at each
  inversion sampling step. This mechanism refines the approximation of a
  predicted point along the forward diffusion trajectory, by iteratively applying
  the pretrained diffusion model, and averaging these predictions. We evaluate
  the performance of our ReNoise technique using various sampling algorithms and
  models, including recent accelerated diffusion models. Through comprehensive
  evaluations and comparisons, we show its effectiveness in terms of both
  accuracy and speed. Furthermore, we confirm that our method preserves
  editability by demonstrating text-driven image editing on real images.
paper_ai_keywords: diffusion models image manipulation inversion pretrained diffusion model denoising steps reconstruction accuracy diffusion sampling process iterative renoising mechanism forward diffusion trajectory ReNoise technique semi-iterative denoising forward diffusion trajectory accelerated diffusion models performance evaluation text-driven image editing