Text-to-Image Diffusion Artifact Detection With Gradient Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating synthetic digital images from text struggle with identifying and mitigating natural and unnatural artifacts, leading to suboptimal image quality.
Innovation Solution
A computer-implemented method using text-to-image diffusion models with a forward and backward denoising process to determine synthetic digital images, employing gradient-based metrics to identify and correct artifacts, and iteratively refine the image until desired quality is achieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If text-to-image diffusion models are used to generate synthetic digital images, then image synthesis capability is improved, but artifacts (natural and unnatural) are introduced
Solution Approach 1:
The patent computes a metric based on gradient magnitudes for each pixel and uses this feedback to identify artifact locations. The system then iteratively regenerates images, replacing pixel values in artifact regions with new samples until the metric indicates acceptable quality, thus using feedback to resolve the contradiction between synthesis capability and image quality
Solution Approach 2:
The patent performs preliminary analysis by computing gradient-based metrics on the generated image to identify artifact locations before final use. This preliminary detection allows the system to prepare correction strategies in advance, such as targeting specific regions for regeneration, thereby improving overall image quality while maintaining synthesis capabilities
2Measurement precision
If automated text-image alignment metrics are used to evaluate synthetic images, then evaluation accuracy is improved, but computational complexity increases
Solution Approach 1:
Instead of evaluating the entire image uniformly, the patent computes gradient magnitudes locally for each pixel and aggregates them into a metric. This local analysis approach allows accurate identification of artifact locations while keeping computations manageable by focusing computational resources only where needed rather than processing the entire image at full resolution
3Manufacturing precision
If iterative regeneration is performed to correct artifacts, then image quality is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary metric computation on the initially generated image to identify artifact locations before initiating iterative regeneration. This preliminary action allows the system to target corrections efficiently, avoiding unnecessary regeneration of entire images and reducing overall processing time by focusing computational effort only on artifact-prone regions
Solution Approach 2:
The patent applies local quality analysis by computing gradient-based metrics for each pixel and identifying specific artifact regions. This localized approach enables the system to perform targeted regeneration only in problematic areas rather than regenerating the entire image repeatedly, thereby improving image quality while minimizing the time penalty associated with iterative processing
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Computer implemented method for digital image processing, comprising determining (304) a synthetic digital image with a text to image diffusion depending on an input (z0) that represents a digital image (x), depending on a noise sample (εt), and depending on an embedding (y) that represents the text, wherein the text to image diffusion comprises a forward diffusion process to determine a noisy latent (zt) depending on the input and the noise sample, wherein the noisy latent (zt) is parametrized by parameters (Φ), wherein the text to image diffusion comprises a backward denoising process to determine an output that represents the synthetic digital image depending on a linear combination of the noisy latent (zt) and predicted noise (εθ), wherein the synthetic digital image comprises pixels, wherein the method comprises determining (306) for at least one pixel a magnitude of a gradient (∇Φ L(Φ)) with respect to the parameters (Φ) of a difference between the predicted noise (εθ) for the pixel and the noise sample (εt) for the pixel, in particular a difference weighted by a weight that is variable.