Customized Textual Image Generation with Mask-Guided Font Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional diffusion models lack comprehensive control over the generation of textual images, particularly in generating multiple text boxes and small, dense text, leading to distorted characters and unnatural artifacts.
Innovation Solution
A method and system using a character mask and conditional mask generation technique, combined with a trained customized character map-guided consistency model, to generate customized textual images with precise control over font attributes like type, size, and background, iteratively refining intermediate images to ensure harmonization and photorealism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional diffusion models are used for text-to-image synthesis, then the generation process is simple and fast, but the control over font attributes and text rendering quality is insufficient
Solution Approach 1:
The patent segments the control process into multiple independent components: character mask generation for text region identification, conditional mask generation for font attribute control, and a diffusion model for image synthesis. This segmentation allows precise control over text rendering while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent performs preliminary actions by generating character masks and conditional masks before the main diffusion generation process. The character mask identifies text regions in advance, and the conditional mask pre-defines font attributes, enabling the diffusion model to focus solely on synthesis with predetermined control parameters.
2Manufacturing precision
If existing models like Glyph-Draw and TextDiffuser are used, then text location and structure control is improved, but the ability to generate multiple text boxes and handle dense small text is limited
Solution Approach 1:
The patent creates a universal mask generation system that can handle multiple text box scenarios and dense small text through a single unified approach. The character mask generation technique universally applies to different text configurations, and the conditional mask system universally controls font attributes across all text regions, enabling versatile application to various text image scenarios.
Solution Approach 2:
The patent implements feedback mechanisms where the generated character masks and conditional masks are iteratively refined and used to guide the diffusion process. This feedback loop allows the model to adjust and optimize text generation based on previous iterations, improving handling of complex text layouts and dense character arrangements.
3Manufacturing precision
If manual labor and iterative design processes are used, then control over font attributes is precise, but the productivity and time required for generation is high
Solution Approach 1:
The patent enables the system to perform self-service by automatically generating character masks, conditional masks, and the final image synthesis without requiring manual intervention. The diffusion model with integrated mask generation and conditional control performs all operations autonomously, maintaining precise font attribute control while dramatically increasing productivity compared to manual iterative processes.
Solution Approach 2:
The patent replaces manual mechanical design processes with an automated computational system. Instead of manual iterative design, the system uses algorithmic mask generation and diffusion modeling to automatically produce customized text images, substituting human labor with automated mechanical processes that maintain precision while enhancing productivity.
Data Source
AI summary
The present disclosure performs image in-painting with controlled text generations to overcome the challenges persisting in traditional diffusion-based methods, especially when it comes to generating textual content within the image with complex font attributes. In the present disclosure, initially, an input image and a textual prompt and a plurality of control parameters are given as input to the present disclosure. Further, character mask and conditional mask are extracted based on the inputs. Finally accurate customized textual images are generated based on the character mask and the conditional mask using a textual image generating diffusion model. The textual image generating diffusion model generates an intermediate image based on the input image and the random gaussian noise. This intermediate image is iteratively refined to generate a latent vector image, and the accurate customized textual image is generated from the latent vector image using a trained customized character map-guided consistency model.