Brush Layer Segmentation for Image Generation Efficiency
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Solution Overview
Problem
Existing methods for generating styled images from target images are inefficient due to high computational requirements in determining the attributes of numerous brush objects, leading to slow image generation and low efficiency.
Innovation Solution
The method involves setting multiple brush layers with preset densities, determining brush position and size information based on sampling and preset densities, and storing brush objects in queues for efficient generation of styled images by overlaying brush layers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a mathematical model is established to determine attributes of all brush objects, then the attributes can be determined systematically, but the computational complexity and time consumption increase significantly
Solution Approach 1:
The image generation process is segmented into multiple independent brush layers, where each layer processes a subset of brush objects. This divides the large-scale mathematical model into smaller, more manageable models that can be solved independently and in parallel, reducing overall computational time while maintaining attribute determination accuracy.
Solution Approach 2:
Brush objects are pre-categorized and organized into different layers before the actual image generation process. Attributes such as transparency, color, and position are predetermined for each layer, allowing the system to skip repetitive calculations and directly render the styled image, significantly reducing generation time.
2Manufacturing precision
If numerous brush objects are processed to create detailed styled images, then image quality improves, but computational resources and processing time increase
Solution Approach 1:
The system segments brush objects into multiple layers with different attribute priorities. Critical attributes that significantly impact image quality are processed with high precision, while less critical attributes use simplified determination methods. This selective processing maintains overall image quality while reducing computational burden.
Solution Approach 2:
Different regions of the image are assigned different levels of processing quality. Areas with important visual features use detailed brush object processing, while less important areas use simplified processing. This local quality approach ensures high image quality where needed while improving overall generation efficiency.
3Measurement precision
If complex mathematical models are used to determine brush object attributes, then attribute accuracy improves, but system complexity and resource consumption increase
Solution Approach 1:
The complex mathematical model is decomposed into multiple simpler sub-models, each handling a specific brush layer. Each sub-model uses reduced sets of variables and constraints appropriate to its layer, making them computationally lighter while collectively achieving the same overall attribute determination accuracy as the original complex model.
Data Source
AI summary
An image generation method, apparatus, and device, and a storage medium are provided. The method comprises: setting at least two brush layers for a target image, wherein each of the at least two brush layers is set with a preset brush density; determining, for each brush layer, attributes of brush objects on the brush layer based on the preset brush density of the brush layer, wherein the attributes include brush position information and brush size; establishing a brush queue for each brush layer, and for each of the brush objects, storing the brush object in the brush queue of the brush layer corresponding to the brush object; and generating a target styled image corresponding to the target image based on the attributes of the brush objects in the brush queue of each brush layer.