Latent-Space Image Editing Directions for Artifact Reduction
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Solution Overview
Problem
Conventional systems for digital image processing suffer from inaccuracies, inefficiencies, and inflexibilities in generating and modifying images, often introducing artifacts and requiring extensive user input for edits.
Innovation Solution
The image modification system utilizes a regularized inversion model, edit direction generation model, and cross-attention guidance model to enhance accuracy, flexibility, and efficiency in image editing, employing machine learning models like generative neural networks and diffusion neural networks to preserve structural details and generate precise image editing directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text-to-image generative models are used to synthesize digital images, then image generation capability is achieved, but accuracy and fidelity of the generated images deteriorate
Solution Approach 1:
The patent introduces a latent space as an intermediary representation between the input image and the generative model. By projecting the input image into latent space and performing edits there, the system achieves more accurate and faithful image modifications while preserving the original image's structural details and reducing artifacts.
2Adaptability or versatility
If conventional image editing systems are used, then basic image modifications can be performed, but flexibility and efficiency in introducing modifications deteriorate
Solution Approach 1:
The patent transitions from editing images in pixel space to editing in latent space, adding a new dimensional layer to the editing process. This latent space representation enables more flexible and efficient modifications by decoupling semantic concepts from pixel-level details, allowing users to perform edits with fewer constraints and less manual intervention.
3Ease of operation
If conventional image modification systems are used, then image edits can be applied, but artifacts are introduced reducing image quality
Solution Approach 1:
The patent replaces traditional pixel-level mechanical editing operations with a learned latent space transformation approach. By using pre-computed edit directions in latent space and projecting them back to image space through the generative model, the system achieves artifact-free edits while maintaining ease of operation, as the complex artifact suppression is handled automatically by the learned representations.
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.