Stable Diffusion Guidance Images for Precise Emblem Color Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing generative AI models like stable diffusion struggle to precisely match a desired color palette in image generation, particularly for applications such as custom in-game icons and emblems, leading to inconsistent color outputs.
Innovation Solution
The use of an enclosed shape, either monochrome or multi-color, superimposed on a background, with controlled noise addition within the shape boundaries and background removal, fine-tuned for stable diffusion models to generate images with precise color matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If stable diffusion model generates images from text input, then image generation capability is achieved, but color accuracy and matching of desired palette deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-processing the input image to extract its color palette and creating a guidance image that encodes the desired color information before feeding it to the stable diffusion model. This preliminary preparation ensures the model receives color constraints upfront, resolving the contradiction between generation capability and color accuracy.
Solution Approach 2:
The patent introduces a guidance image as an intermediary between the text prompt and the stable diffusion model. This guidance image acts as a mediator that translates the desired color palette into visual constraints, allowing the model to generate images that both fulfill the text description and match the target colors, thus resolving the contradiction.
2Extent of automation
If noise is added to entire image for stable diffusion processing, then generative processing is enabled, but background color contamination occurs
Solution Approach 1:
The patent applies segmentation by dividing the image into foreground (enclosed shape) and background regions, then applying noise addition only to the foreground region. This selective noise application enables generative processing of the subject while preserving the background's original color purity, resolving the contradiction between automation and color precision.
Solution Approach 2:
The patent implements local quality by applying different noise treatment to different regions of the image - the foreground receives noise for generative processing while the background remains noise-free to maintain its original color. This localized approach resolves the contradiction between enabling generative processing and preserving color purity.
3Manufacturing precision
If enclosed shape is used as guidance image, then color matching improves, but background removal processing is required
Solution Approach 1:
The patent applies the taking out principle by extracting the background from the final generated image, leaving only the foreground subject with the desired color properties. This post-processing step removes the background that was necessary as a canvas during generation, resolving the contradiction between achieving color matching and managing processing complexity.
Data Source
AI summary
Generating custom team emblems using stable diffusion based on input text describing a desired image. A circle is overlaid in the center of a pure-color background representing each team's “color” and used as the input to stable diffusion img2img to produce emblems. This produces high-quality emblem outputs that generally match the input color.