Diffusion Model Guidance With Nonlinear Regularization for Stable Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing diffusion models face stability issues and lack effective control over context in data generation, particularly in tasks like text-to-image generation, due to non-linear deviations in classifier-free guidance, leading to unstable and unnatural image generation.
Innovation Solution
An AI-based characteristic guidance method and system that employs a regularization module to enhance stability and control over context through a nonlinear correction vector and context regularization iterations, using a projection module to correct non-linearities in diffusion models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classifier-free guidance is used to enhance control strength in diffusion models, then conditional likelihood is improved, but stability deteriorates due to non-linear deviations when guidance scale is large
Solution Approach 1:
The patent transforms the non-linear correction problem into a parameter optimization problem by introducing a regularization term with parameter λ. The fixed-point iteration method adjusts parameters iteratively to find the optimal balance between control strength and stability, converting a qualitative stability issue into a quantitative parameter tuning process.
Solution Approach 2:
The patent implements feedback through the fixed-point iteration method, where the non-linear correction term is continuously refined based on the difference between predicted and actual distributions. The iterative process uses feedback from each iteration to improve the next, gradually converging to a stable solution that maintains both control strength and generation stability.
2Measurement precision
If guidance scale parameter is increased to concentrate on samples with highest conditional likelihood, then control precision is improved, but image quality deteriorates due to overly saturated and unnatural images
Solution Approach 1:
The patent introduces a non-linear correction term that dynamically adjusts the effective guidance scale based on the local characteristics of the data distribution. This transforms the fixed guidance scale parameter into a variable that adapts locally, maintaining high control precision where needed while preventing over-saturation in other regions through parameter transformation.
Solution Approach 2:
The correction term operates locally on the probability distribution, applying different adjustments to different regions of the data space. This allows high guidance scale effects to be applied only where they improve control precision, while other regions maintain natural characteristics, achieving local optimization of both control precision and image quality.
3Measurement precision
If non-linear correction term is added to characteristic guidance to address large guidance scale issues, then theoretical accuracy is improved, but computational complexity increases due to fixed-point iteration requirement
Solution Approach 1:
The patent applies partial correction by introducing a regularization term with parameter λ that controls the magnitude of the non-linear correction. Instead of fully correcting all non-linearities, the method applies a controlled amount of correction that is sufficient to improve theoretical accuracy while limiting the increase in computational complexity through parameter adjustment.
Solution Approach 2:
The patent performs preliminary linear correction using classifier-free guidance before applying the non-linear correction term. This preliminary action handles the bulk of the guidance requirement, allowing the subsequent non-linear correction to focus only on the residual inaccuracies, thereby reducing the overall computational burden while maintaining theoretical accuracy.
Data Source
AI summary
The invention provides artificial intelligence-based characteristic guidance method and system for enhancing quality of a diffusion model in generating a data from a noisy data based on a condition information. The method comprises: generating a nonlinear precorrection vector; performing, by a regularization module, a context regularization iteration to obtain an updated nonlinear correction vector and a nonlinear correction gradient; checking if a convergence criterion is met; and denoising the noisy data based on the updated nonlinear correction vector to generate the data. By using the regularization module, the provided characteristic guidance method not only greatly improves the stability of data generation by the diffusion model, but also provides enhanced control over context through two context modes: the detail enhancement mode and the context enhancement mode. The present invention can enhance the semantic characteristics of prompts and mitigate irregularities in image generation.