CODE: Confident Ordinary Differential Editing
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.
66caf58fe51ba30092ed6f6d
Bastien van Delft
66caf58fe51ba30092ed6f6e
Tommaso Martorella
claimed_verified
2024-08-26T07:14:58.927Z
648097daf5be39206aeff790
https://cdn-avatars.huggingface.co/v1/production/uploads/648097daf5be39206aeff790/BfDcQkmiCATncP6WuejpA.png
Tommaso Martorella
user
tommymarto
66caf58fe51ba30092ed6f6f
Alexandre Alahi
CODE: Confident Ordinary Differential Editing
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.
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.
diffusion model score-based updates Ordinary Differential Equation (ODE) conditional image generation blind image restoration confidence interval-based clipping