CODE: Confident Ordinary Differential Editing
Authors: name: Bastien van Delft, name: Tommaso Martorella, name: Alexandre Alahi

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.