Latent-Space Image Editing Directions With Structure-Preserving Diffusion
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Solution Overview
Problem
Conventional systems for digital image generation and modification suffer from inaccuracies, inflexibility, and inefficiency, often producing unusable images due to artifacts and requiring extensive user input for edits.
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 generative neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional text-to-image generative models are used to synthesize and modify digital images, then hardware and software platforms can leverage computing advancements, but accuracy and fidelity in generating and modifying images deteriorate, producing artifacts and inaccurate results
Solution Approach 1:
The patent introduces a cross-attention guidance model as an intermediary between the diffusion model and image modification process. This mediator preserves structural details by guiding the generation process to maintain original image features while applying modifications, thereby resolving the contradiction between generation accuracy and modification fidelity
Solution Approach 2:
The system employs regularized inversion to adjust parameters in the latent space, transforming the image modification process to operate in a optimized parameter space. This allows for more precise control over modification fidelity while maintaining generation accuracy through parameter optimization
2Device complexity
If rigid model architectures are used for image processing, then system structure is simplified, but flexibility in generating and modifying images deteriorates, requiring extensive user input
Solution Approach 1:
The patent implements dynamic and adjustable model components that can adapt to different modification needs. The cross-attention guidance model and regularized inversion mechanism provide flexible parameter adjustment capabilities, allowing the system to handle diverse image modification tasks without requiring extensive user input while maintaining manageable architectural complexity
3Speed
If conventional image processing methods are used, then processing speed is maintained, but efficiency in introducing modifications deteriorates, requiring extensive user input and iterations
Solution Approach 1:
The system performs preliminary inversion in the latent space before actual image modification. This preliminary action in the optimized parameter space enables more efficient modifications with fewer iterations required, improving productivity while maintaining processing speed through pre-computed transformations
Data Source
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.