Reference Image Structure Encoding for Faster Diffusion Generation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional image generation models struggle to accurately and efficiently generate synthetic images that depict a target spatial structure from an input image due to architectural complexity, leading to longer processing times and inconsistent output.

Innovation Solution

A system utilizing a condition encoder to generate structural encoding, combined with a U-Net architecture, which reduces model parameters and training cost, enabling efficient generation of synthetic images that accurately depict the target spatial structure.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If conventional image generation models are used to generate synthetic images with target spatial structure, then the model can produce output images, but the processing time is long and the output is inconsistent

Engineering Contradiction:

Improvestructural accuracyVSAvoidprocessing time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The model is segmented into distinct functional components: a spatial structure encoder that extracts structural features from input images, a text encoder that processes prompts, and a diffusion model that generates images. This segmentation allows each component to specialize in specific tasks, improving both processing efficiency and structural accuracy of generated images

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The spatial structure encoder performs preliminary extraction and encoding of structural features from input images before the main generation process. By pre-processing and encoding the spatial structure information in advance, the diffusion model can directly utilize these encoded features during generation, significantly reducing processing time while maintaining consistent structural accuracy

Inventive Principle:

Principle #10Preliminary action

2Manufacturing precision

If conventional image generation models are used, then the model can generate synthetic images, but the model complexity and training cost are high

Engineering Contradiction:

Improvestructural accuracyVSAvoidmodel complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

A spatial structure encoder is introduced as an intermediary component between the input image and the diffusion model. This encoder transforms complex spatial structure information into compact encoded representations, which simplifies the input for the diffusion model and reduces overall model complexity while preserving structural accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The spatial structure information is copied and encoded into a separate representation that can be efficiently processed and integrated with text prompts. This copying mechanism allows the model to work with simplified structural representations rather than raw pixel data, reducing computational complexity while maintaining fidelity to the original spatial structure

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250308083A1Reference image structure match using diffusion models

Publication Date: 2025.10.02 ADOBE INC

AI summary

A method, apparatus, non-transitory computer readable medium, and system for image processing include obtaining a structural input indicating a target spatial structure, encoding, using a condition encoder, the structural input to obtain a structural encoding representing the target spatial structure, and generating, using an image generation model, a synthetic image based on the structural encoding, where the synthetic image depicts an object having the target spatial structure.