Latent Denoising Networks for Multi-Modal Image Consistency

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Solution Overview

Problem

Existing approaches struggle to generate target images that maintain consistency with reference images and adhere to text instructions without merely copying the scene, while requiring significant computational resources.

Innovation Solution

A latent denoising neural network is used to generate target data items by conditioning each reverse diffusion step on encoded representations of reference data items and text instructions, utilizing existing components from a pre-trained text-to-image network to adapt to multi-modal inputs efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If a latent denoising neural network is used to generate target images conditioned on multi-modal inputs, then the quality and consistency of generated images improve, but the computational resources and processing time increase

Engineering Contradiction:

Improveimage generation quality and consistencyVSAvoidcomputational resources

Core Design Contradiction:

Manufacturing precisionVSUse of energy by moving object

Solution Approach 1:

The system pre-processes reference images and text instructions by encoding them into latent representations and feature vectors before the main generation process. This preliminary encoding of reference data and instructions enables the neural network to efficiently utilize these pre-computed representations during image generation, reducing the computational burden while maintaining high generation quality and consistency with reference images.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary latent space representation that mediates between the reference images, text instructions, and the final generated image. By operating in this compressed latent space rather than directly manipulating pixel data, the system reduces computational complexity while preserving the essential information needed for high-quality generation and consistency with multi-modal inputs.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If reference images and text instructions are processed through multiple neural network components, then the adherence to instructions and reference consistency improve, but the device complexity increases

Engineering Contradiction:

Improveinstruction adherence and reference consistencyVSAvoidneural network architecture

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent employs a universal latent denoising neural network that can process multiple types of inputs (reference images, text instructions, and their combinations) through a single unified architecture. This multi-functional network handles different input modalities and processing stages using the same core components, reducing overall system complexity while maintaining reliable instruction adherence and reference consistency through its versatile design.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system merges the processing of reference images and text instructions into a unified latent representation space. By combining these different input modalities in the latent space before generation, the network can efficiently integrate information from both sources without requiring separate processing pipelines, thereby reducing architectural complexity while improving reliability through comprehensive information integration.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20250348980A1Processing multi-modal inputs using denoising neural networks

Publication Date: 2025.11.13 GDM HOLDING LLC

AI summary

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing multi-modal inputs using denoising neural networks.