title: CODE: Confident Ordinary Differential Editing
summary: Conditioning image generation facilitates seamless editing and the creation
  of photorealistic images. However, conditioning on noisy or Out-of-Distribution
  (OoD) images poses significant challenges, particularly in balancing fidelity
  to the input and realism of the output. We introduce Confident Ordinary
  Differential Editing (CODE), a novel approach for image synthesis that
  effectively handles OoD guidance images. Utilizing a diffusion model as a
  generative prior, CODE enhances images through score-based updates along the
  probability-flow Ordinary Differential Equation (ODE) trajectory. This method
  requires no task-specific training, no handcrafted modules, and no assumptions
  regarding the corruptions affecting the conditioning image. Our method is
  compatible with any diffusion model. Positioned at the intersection of
  conditional image generation and blind image restoration, CODE operates in a
  fully blind manner, relying solely on a pre-trained generative model. Our
  method introduces an alternative approach to blind restoration: instead of
  targeting a specific ground truth image based on assumptions about the
  underlying corruption, CODE aims to increase the likelihood of the input image
  while maintaining fidelity. This results in the most probable in-distribution
  image around the input. Our contributions are twofold. First, CODE introduces a
  novel editing method based on ODE, providing enhanced control, realism, and
  fidelity compared to its SDE-based counterpart. Second, we introduce a
  confidence interval-based clipping method, which improves CODE's effectiveness
  by allowing it to disregard certain pixels or information, thus enhancing the
  restoration process in a blind manner. Experimental results demonstrate CODE's
  effectiveness over existing methods, particularly in scenarios involving severe
  degradation or OoD inputs.
paper_authors._id: 66caf58fe51ba30092ed6f6d
paper_authors.name: Bastien van Delft
paper_authors._id: 66caf58fe51ba30092ed6f6e
paper_authors.name: Tommaso Martorella
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-26T07:14:58.927Z
paper_authors._id: 648097daf5be39206aeff790
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/648097daf5be39206aeff790/BfDcQkmiCATncP6WuejpA.png
paper_authors.fullname: Tommaso Martorella
paper_authors.type: user
paper_authors.user: tommymarto
paper_authors._id: 66caf58fe51ba30092ed6f6f
paper_authors.name: Alexandre Alahi
paper_title: CODE: Confident Ordinary Differential Editing
paper_summary: Conditioning image generation facilitates seamless editing and the creation
  of photorealistic images. However, conditioning on noisy or Out-of-Distribution
  (OoD) images poses significant challenges, particularly in balancing fidelity
  to the input and realism of the output. We introduce Confident Ordinary
  Differential Editing (CODE), a novel approach for image synthesis that
  effectively handles OoD guidance images. Utilizing a diffusion model as a
  generative prior, CODE enhances images through score-based updates along the
  probability-flow Ordinary Differential Equation (ODE) trajectory. This method
  requires no task-specific training, no handcrafted modules, and no assumptions
  regarding the corruptions affecting the conditioning image. Our method is
  compatible with any diffusion model. Positioned at the intersection of
  conditional image generation and blind image restoration, CODE operates in a
  fully blind manner, relying solely on a pre-trained generative model. Our
  method introduces an alternative approach to blind restoration: instead of
  targeting a specific ground truth image based on assumptions about the
  underlying corruption, CODE aims to increase the likelihood of the input image
  while maintaining fidelity. This results in the most probable in-distribution
  image around the input. Our contributions are twofold. First, CODE introduces a
  novel editing method based on ODE, providing enhanced control, realism, and
  fidelity compared to its SDE-based counterpart. Second, we introduce a
  confidence interval-based clipping method, which improves CODE's effectiveness
  by allowing it to disregard certain pixels or information, thus enhancing the
  restoration process in a blind manner. Experimental results demonstrate CODE's
  effectiveness over existing methods, particularly in scenarios involving severe
  degradation or OoD inputs.
paper_ai_summary: CODE, a novel image synthesis method using a diffusion model and ODE, enhances image editing and restoration especially with noisy or out-of-distribution inputs by increasing input likelihood with enhanced confidence interval clipping.
paper_ai_keywords: diffusion model score-based updates Ordinary Differential Equation (ODE) conditional image generation blind image restoration confidence interval-based clipping