Diffusion Image Generation That Preserves Color and Composition
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Solution Overview
Problem
Conventional digital image systems using diffusion neural networks fail to accurately preserve color harmony and image composition when generating new images, leading to inaccurate image search results.
Innovation Solution
An image context modification system that generates a blurred digital image from a sample image to obscure content while retaining color harmony and composition, using a diffusion neural network to denoise this blurred image and generate new content based on a text prompt, ensuring the new image maintains the original color themes and visual element arrangement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional diffusion neural networks generate new images from random noise, then image content diversity is improved, but color harmony and composition preservation deteriorate
Solution Approach 1:
The system performs preliminary actions by extracting color palettes and composition structures from source images before the diffusion generation process. These extracted features are then preserved and applied during image generation, ensuring that color harmony and composition are maintained while allowing content diversity through the diffusion process.
Solution Approach 2:
The system introduces intermediary components including a color palette extractor that captures color relationships, a composition structure extractor that identifies spatial arrangements, and a feature preserver that maintains these extracted features throughout the diffusion process. These intermediaries act as mediators between the source image and generated images, ensuring fidelity in color and composition while enabling content variation.
2Adaptability or versatility
If conventional diffusion neural networks generate new images from random noise, then image content diversity is improved, but composition preservation deteriorate
Solution Approach 1:
The system performs preliminary actions by extracting composition structures from source images before the diffusion generation process. These extracted composition features are then preserved and applied during image generation, ensuring that spatial arrangements and structural elements are maintained while allowing content diversity through the diffusion process.
Solution Approach 2:
The system introduces an composition structure extractor and feature preserver as intermediary components that capture and maintain compositional elements throughout the diffusion process. These intermediaries ensure that the spatial relationships and structural composition from source images are preserved in generated images while enabling content variation.
3Measurement precision
If diffusion neural networks denoise blurred images toward text prompts, then image accuracy to prompt is improved, but color and composition fidelity deteriorate
Solution Approach 1:
The system introduces a feature preserver as an intermediary component that maintains color palettes and composition structures extracted from source images throughout the diffusion denoising process. This preserver acts as a constraint that guides the diffusion process to respect both the text prompt requirements and the original image's color and composition characteristics, resolving the conflict between prompt accuracy and fidelity preservation.
4Measurement precision
If diffusion neural networks denoise blurred images toward text prompts, then image accuracy to prompt is improved, but composition fidelity deteriorate
Solution Approach 1:
The system introduces a composition structure extractor and feature preserver as intermediary components that maintain compositional elements throughout the diffusion denoising process. These intermediaries constrain the diffusion process to preserve spatial relationships and structural composition while still achieving accuracy to the text prompt, resolving the conflict between prompt fulfillment and composition fidelity.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images utilizing a diffusion neural network to preserve color harmony and image composition from a sample digital image while modifying image content. In some embodiments, the disclosed systems receive, via user input, a text prompt defining query image content and a sample digital image depicting a color harmony. In some cases, the disclosed systems generate a blurred digital image by blurring pixels of the sample digital image while preserving the color harmony. In some embodiments, the disclosed systems generate, utilizing a diffusion neural network, a modified digital image depicting the query image content having the color harmony of the sample digital image by denoising the blurred digital image toward a noise vector of the text prompt.