Control Model for Region-Specific Multi-Condition Image Synthesis

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Solution Overview

Problem

Existing image synthesis methods face challenges in combining multiple conditions, such as Canny Edge and Semantic Label Map, leading to a degradation of image synthesis quality, especially when applied to different image regions.

Innovation Solution

A method involving a control model like ControlNet that allows for the simultaneous application of multiple conditions, with selective masking of areas within the image to control the generation process, enabling flexible and precise image synthesis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple conditions (Canny Edge, Depth Map, Semantic Label Map) are combined for image synthesis, then control flexibility and adaptability are improved, but image synthesis quality degrades

Engineering Contradiction:

Improvecontrol flexibilityVSAvoidimage synthesis quality

Core Design Contradiction:

Adaptability or versatilityVSManufacturing precision

Solution Approach 1:

The image canvas is divided into multiple spatial regions, each region allowing selective application of different conditions. This segmentation enables the system to apply multiple conditions overall while controlling their interaction at local levels, preventing quality degradation in specific areas.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different conditions are applied with varying strengths or exclusivity to different spatial regions of the image. This local quality approach allows the system to maintain high synthesis quality in regions where condition conflicts would otherwise occur, while still achieving overall control flexibility through the combination of multiple conditions across the entire image.

Inventive Principle:

Principle #3Local quality

2Ease of operation

If conditions are applied to all image areas, then comprehensive control is achieved, but generation freedom in specific areas is lost

Engineering Contradiction:

Improvecomprehensive controlVSAvoidgeneration freedom

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The condition application is made dynamic and adjustable per region. Users can configure which conditions apply to which areas, allowing the system to switch between comprehensive control and generation freedom based on regional requirements. This dynamic approach resolves the contradiction by making control flexibility itself adaptable.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If multiple control models are used to combine conditions, then condition coverage is improved, but system complexity increases

Engineering Contradiction:

Improvecondition coverageVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Multiple control models are merged into a single integrated control model that handles multiple conditions simultaneously. This consolidation maintains comprehensive condition coverage while reducing system complexity by eliminating the need to manage and coordinate multiple separate control models.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentEP4697272A1Method for generating a data set for training and/or testing a machine learning system

Publication Date: 2026.02.18 ROBERT BOSCH GMBH

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AI summary

The invention relates to a method (100) for generating at least one data set for training and/or testing a machine learning system (55), wherein the generation is provided by a control model (50).