Semantic-aware initial latent code selection for image editing

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Solution Overview

Problem

Conventional image editing systems use predefined initial latent codes for image morphing, which can lead to inaccurate and biased results, especially when transforming images with characteristics different from the predefined code, resulting in unrealistic outputs and increased bandwidth usage due to the need for more iterations.

Innovation Solution

An image modification system selects an initial latent code from a cache based on the input text using a Contrastive Language-Image Pre-training (CLIP) model, blending it with the input image latent code to generate a blended image, reducing the number of iterations and improving accuracy by matching the semantic features of the input text.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If predefined initial latent codes are used for image morphing, then the process is simple to implement, but the accuracy and realism of the output images deteriorate

Engineering Contradiction:

Improveease of implementationVSAvoidimage morphing accuracy

Core Design Contradiction:

Ease of manufactureVSManufacturing precision

Solution Approach 1:

The system performs preliminary action by selecting an appropriate initial latent code from a cache based on the input text semantics before the image morphing process begins. This pre-selection step ensures that the starting point is semantically aligned with the target description, improving accuracy without complicating the overall implementation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes the parameter of the initial latent code from a fixed predefined value to a dynamically selected value based on CLIP code similarity. This parameter change allows the system to adapt to different input texts while maintaining a simple cache-based selection mechanism

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If predefined initial latent codes are used for image morphing, then the system complexity is low, but the output image realism deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidoutput image realism

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The system introduces an intermediary mechanism (CLIP code comparison) between the input text and the initial latent code selection. This intermediary enables semantic matching without requiring complex end-to-end learning, maintaining system simplicity while improving output realism through better initial code selection

Inventive Principle:

Principle #24Intermediary (Mediator)

3Manufacturing precision

If more iterations are performed to compensate for poor initial latent code selection, then the accuracy can be improved, but the computing resource usage and time increase

Engineering Contradiction:

Improveimage morphing accuracyVSAvoidprocessing speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

By performing the critical action of selecting an appropriate initial latent code before the morphing iterations begin, the system reduces the number of iterations needed to achieve accurate results. This preliminary action improves processing speed while maintaining or enhancing accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces the mechanical approach of simply iterating from a fixed initial code with a semantic-based selection mechanism using CLIP codes. This substitution reduces the computational burden of iterations while improving the quality of the morphing process

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Manufacturing precision

If more iterations are performed to improve morphing accuracy, then the precision can be increased, but the time consumption increases

Engineering Contradiction:

Improvemorphing accuracyVSAvoidtime consumption

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs the key action of selecting a semantically appropriate initial latent code before the time-consuming iteration process. This preliminary selection ensures that fewer iterations are needed to achieve high accuracy, thereby reducing overall time consumption

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12254597B2Semantic-aware initial latent code selection for text-guided image editing and generation

Publication Date: 2025.03.18 ADOBE INC

AI summary

An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a image modification model to the set of recommendable items. The image modification model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The image modification model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.