Synthetic Graph Generation via Segmented Diffusion and Masking
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Solution Overview
Problem
Conventional AI models, particularly AGI models like CLIP, struggle to effectively interpret and generate mathematical graphs due to lack of appropriate semantic information, inadequate representation in training datasets, and limited focus on accessibility and graph interpretation.
Innovation Solution
A system and method that involve generating graphical datasets using a graph structure generation module, a mask generation module, a diffusion module, and a merge layer module to create realistic images of graph components and layouts, while applying precise labelling and masking to control the image generation process and preserve graphical information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional AGI models are used to generate mathematical graphs, then general image generation capability is achieved, but precise control over graph semantic structure is lost
Solution Approach 1:
The patent segments the graph generation process into distinct components: graph structure generation (defining nodes, edges, and layout), mask generation (identifying regions requiring diffusion), and selective diffusion application. This segmentation allows precise control over graph semantic structure while still utilizing diffusion for realistic rendering of specific elements.
Solution Approach 2:
The patent applies diffusion processing selectively to specific regions of the graph image rather than the entire image. By generating masks that identify which areas need diffusion enhancement and applying diffusion only to those regions, the system maintains precise control over graph structure while improving visual realism locally where needed.
2Illumination intensity
If diffusion processing is applied to entire graph images, then visual realism is improved, but graphical information and structure are distorted
Solution Approach 1:
The patent extracts and preserves critical graph information (structure, labels, numerical data) before applying diffusion processing. By separating the essential graphical information from areas suitable for diffusion enhancement, the system maintains information integrity while still achieving visual realism in appropriate regions.
Solution Approach 2:
The patent performs preliminary graph structure generation and information extraction before applying diffusion processing. This preliminary action ensures that critical graphical information is established and protected before any diffusion-induced transformations occur, preventing distortion of essential graph elements.
3Manufacturing precision
If ControlNet is used to control diffusion output, then superficial structure control is achieved, but fine-grained graph component control is insufficient
Solution Approach 1:
The patent segments control into two levels: graph structure generation module that defines high-level graph components (nodes, edges, layout) and mask generation that identifies specific regions for diffusion. This multi-level segmentation enables fine-grained control over individual graph components while maintaining overall structure integrity.
Solution Approach 2:
The patent adds a mask dimension to the control process, creating a binary mask that specifies which regions should undergo diffusion. This additional dimension of control allows precise specification of which graph components receive diffusion processing, enabling fine-grained control beyond what traditional ControlNet approaches provide.
Data Source
AI summary
Embodiments of the present invention provide a method and system for procedural generation of synthetic diffusion-augmented graphical data set. The system is based on Artificial Neural network (ANN) that is trained to generate graphics in form of mathematical graphs. The system utilizes a pre-trained diffusion network and does not need retraining. The system comprises a graphical dataset in the pre-trained diffusion network and is capable of synthesizing all of its source data from the graphical dataset. The graphics are generated by a graphic generation engine which is implemented using traditional image processing and scalable vector graphics (SVG).