Diffusion Model Synthetic Image Generation with Quality Assessment
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Solution Overview
Problem
Developers face challenges in accessing large amounts of high-quality images for training and testing machine learning models, particularly in contexts like self-driving vehicles, where image generation using diffusion models is time-consuming and lacks quality assurance.
Innovation Solution
A method that involves receiving an input image, removing unwanted objects through instance segmentation, inpainting the removed areas, estimating the position of other objects, generating a layout, and using a diffusion model to create synthetic objects, thereby generating diverse high-quality image data for ML models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If diffusion models are used to generate synthetic images for ML training/testing data, then the quantity of available training data increases, but the quality and reliability of generated images cannot be ascertained and manual verification is required
Solution Approach 1:
The system implements feedback loops where generated images are evaluated by quality assessment models, and the results are used to adjust generation parameters and refine the diffusion model. This closed-loop feedback mechanism ensures continuous improvement of image quality while maintaining high-volume generation capability.
Solution Approach 2:
Quality assessment models and evaluation metrics are introduced as intermediaries between the diffusion model and the final training dataset. These intermediaries automatically filter and validate generated images, ensuring quality standards are met without requiring manual verification of each image.
2Adaptability or versatility
If manual methods are used to generate image datasets with diffusion models, then diverse images can be created, but the process requires significant time for inputting conditions, generating images, and manually selecting appropriate images
Solution Approach 1:
The system enables self-service automation where the diffusion model automatically generates diverse images based on predefined parameters, and quality assessment models automatically filter and select appropriate images. This eliminates the need for manual image-by-image verification while maintaining diversity through automated parameter variation and conditional generation.
Solution Approach 2:
The system automatically varies generation parameters such as noise schedules, sampling steps, and conditional inputs to produce diverse images. By systematically changing parameters and using automated quality assessment, the system achieves diversity without manual intervention, significantly reducing preparation time.
3Productivity
If existing diffusion model methods are used for image generation, then synthetic images can be produced, but the quality of such images as training/testing data cannot be ascertained
Solution Approach 1:
Quality assessment models serve as intermediaries that automatically evaluate generated images against predefined quality criteria. These intermediaries provide objective quality measurements without slowing down the generation process, as the assessment occurs in parallel or through efficient automated pipelines.
Solution Approach 2:
Manual quality assessment is replaced with automated computational evaluation using quality assessment models and metrics. This substitution maintains high productivity by using efficient algorithms that can evaluate images rapidly, eliminating the need for slow manual verification while providing precise quality measurements.
Data Source
AI summary
Provided are a method, system, and device for generating synthetic images. The method may include, receiving an input image; removing at least one pre-existing object from the input image; inpainting the region where the at least one pre-existing object was removed; estimating a position of another pre-existing object from the input image; generating a layout over the input image based on the estimated position; and generating a synthetic object based on the layout.