High-Resolution Image Layout Manipulation With Sparse Attention

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Solution Overview

Problem

Conventional systems for semantic image layout manipulation suffer from inaccuracies, inefficiencies, and inflexibilities in generating high-resolution digital images, often failing to transfer visual details and maintaining high-resolution samples, leading to unrealistic and fragmented outputs due to high computational costs and rigid, simplistic approaches.

Innovation Solution

The implementation of a sparse attention warped image neural network and a digital image layout neural network, utilizing sparse attention mapping and a two-stage decoder, to generate refined digital images that accurately align with edited semantic layouts, preserving high-resolution visual details and textures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional systems use dense attention mapping to transfer visual details, then image detail accuracy is improved, but computational cost increases exponentially making high-resolution generation infeasible

Engineering Contradiction:

Improvevisual detail transfer accuracyVSAvoidcomputational cost

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent segments the attention computation from dense to sparse by selecting only K representative key positions out of N total positions. This segmentation reduces the computational complexity from O(N²) to O(NK) while preserving the essential visual detail transfer capability through the selected key positions that capture the most important spatial relationships.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by making the attention mechanism adaptive - different regions of the image receive different attention weights. The sparse attention mechanism identifies and focuses computational resources on local regions that contain critical visual information, rather than uniformly processing all regions. This allows high-resolution detail preservation in important areas while reducing computation in less critical areas.

Inventive Principle:

Principle #3Local quality

2Productivity

If conventional systems downsample images to reduce computational cost, then processing efficiency is improved, but image resolution and visual detail quality deteriorate

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidimage resolution

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent performs preliminary action by maintaining high-resolution feature representations throughout the network architecture. Instead of downsampling early to improve efficiency, the system preserves full resolution features and applies sparse attention mechanisms to efficiently process these high-resolution features. The sparse attention computation is performed preliminarily on key positions to guide subsequent detailed processing, avoiding the need for early downsampling.

Inventive Principle:

Principle #10Preliminary action

3Device complexity

If conventional systems use simplistic layout manipulation approaches, then device complexity is reduced, but image generation accuracy and realism deteriorate

Engineering Contradiction:

Improvesystem architecture complexityVSAvoidimage generation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary sparse attention mechanism that bridges the gap between simple computational operations and complex visual detail preservation. The sparse attention module acts as an intermediary layer that selectively processes key positions to capture essential spatial relationships, enabling accurate layout manipulation without requiring overly complex architectures. This intermediary mechanism achieves high accuracy while maintaining reasonable system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Stability of the object's composition

If conventional systems use rigid attention mechanisms, then system stability is improved, but adaptability to different image layouts and styles deteriorates

Engineering Contradiction:

Improvesystem stabilityVSAvoidlayout manipulation flexibility

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The patent applies dynamics by making the attention mechanism adaptive rather than rigid. The sparse attention weights are dynamically computed based on the input image content and layout requirements. The system can adaptively select different key positions and adjust attention distributions according to the specific image and layout manipulation task, enabling flexibility across diverse scenarios while maintaining computational stability through the structured sparse attention framework.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12361512B2Generating digital images utilizing high-resolution sparse attention and semantic layout manipulation neural networks

Publication Date: 2025.07.15 ADOBE INC

AI summary

This disclosure describes one or more implementations of a digital image semantic layout manipulation system that generates refined digital images resembling the style of one or more input images while following the structure of an edited semantic layout. For example, in various implementations, the digital image semantic layout manipulation system builds and utilizes a sparse attention warped image neural network to generate high-resolution warped images and a digital image layout neural network to enhance and refine the high-resolution warped digital image into a realistic and accurate refined digital image.