Regularized Diffusion Inversion for Artifact-Resistant Image Editing

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Solution Overview

Problem

Conventional systems for digital image processing suffer from inaccuracies, inflexibility, and inefficiency in generating and modifying digital images, often introducing artifacts and requiring extensive user input.

Innovation Solution

The image modification system utilizes an edit direction generation model, regularized inversion model, and cross-attention guidance model to determine accurate image editing directions, improve inversion accuracy, and preserve structural details using diffusion neural networks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional text-to-image generative models are used to synthesize digital images, then image generation capability is provided, but accuracy and fidelity of the generated images deteriorate

Engineering Contradiction:

Improveimage generation accuracyVSAvoidimage fidelity

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The system performs preliminary actions by first inverting the input image into the diffusion model's latent space to obtain an initial latent representation, then applies regularization techniques (auto-correlation and KL divergence) during the forward diffusion process to ensure the inverted latent maintains high fidelity to the original image structure before进行修改

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by computing auto-correlation loss and KL divergence loss during the inversion process, using these loss signals to adjust and refine the latent representation iteratively, ensuring the generated images maintain both accuracy and fidelity

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If conventional image modification systems are used, then basic editing functionality is provided, but flexibility in introducing modifications deteriorates

Engineering Contradiction:

Improveediting flexibilityVSAvoiduser input requirement

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system achieves universality by creating a unified diffusion-based framework that can handle multiple types of image modifications (object transformation, style transfer, attribute changes) through a single inversion-modification-reconstruction pipeline, eliminating the need for separate specialized tools for each editing task

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If conventional image processing systems are used, then basic processing operations are provided, but efficiency in generating and modifying images deteriorates

Engineering Contradiction:

Improveimage processing efficiencyVSAvoidprocessing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system replaces traditional mechanical image processing operations with a unified diffusion-based generative model that handles inversion, modification, and reconstruction in an integrated manner, reducing the number of separate processing steps and improving overall efficiency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Measurement precision

If conventional inversion methods are used in diffusion models, then image inversion is achieved, but accuracy of inverted images deteriorates due to artifacts

Engineering Contradiction:

Improveinversion accuracyVSAvoidimage artifacts

Core Design Contradiction:

Measurement precisionVSObject-generated harmful factors

Solution Approach 1:

The system applies preliminary anti-action by introducing regularization terms (auto-correlation loss and KL divergence loss) during the forward diffusion inversion process that actively counteract the formation of artifacts, preventing distortion and blurring before they can degrade the inverted image quality

Inventive Principle:

Principle #9Preliminary anti-action

Solution Approach 2:

The system uses feedback mechanisms by computing auto-correlation loss to maintain spatial coherence and KL divergence loss to ensure distributional consistency during inversion, using these loss signals to iteratively refine the latent representation and eliminate artifacts

Inventive Principle:

Principle #23Feedback

5Adaptability or versatility

If diffusion neural networks are used for image generation, then generative capability is provided, but preservation of structural details deteriorates

Engineering Contradiction:

Improvegenerative capabilityVSAvoidstructural detail preservation

Core Design Contradiction:

Adaptability or versatilityVSShape

Solution Approach 1:

The system performs preliminary action by inverting the input image into the diffusion model's latent space while applying regularization to preserve structural information, ensuring that the latent representation maintains the original image's structural details before the generative modification process begins

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12518358B2Utilizing regularized forward diffusion for improved inversion of digital images

Publication Date: 2026.01.06 ADOBE INC

AI summary

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to generate modified digital images. In particular, in some embodiments, the disclosed systems generate image editing directions between textual identifiers of two visual features utilizing a language prediction machine learning model and a text encoder. In some embodiments, the disclosed systems generated an inversion of a digital image utilizing a regularized inversion model to guide forward diffusion of the digital image. In some embodiments, the disclosed systems utilize cross-attention guidance to preserve structural details of a source digital image when generating a modified digital image with a diffusion neural network.