Image Generation Model Tuning via Combined Noise Feature Guidance

The patent applies preliminary action by using a pre-trained noise prediction model to generate synthetic training images before actual model training. The system pre-generates images with various noise levels and conditions, creating a comprehensive training dataset in advance. This preliminary generation of training data eliminates the need for time-consuming manual data collection and annotation, while still providing sufficient training samples for achieving high model performance

The patent employs copying by generating synthetic images that replicate real-world scenarios through a noise prediction model. Instead of collecting actual physical images, the system creates copies of training data through computational generation, preserving the essential characteristics and variability needed for model training while avoiding the costs and time associated with physical data collection

The patent applies self-service by enabling the system to automatically generate its own training data with comprehensive features through the noise prediction model. The model autonomously creates images with varied noise levels, lighting conditions, and other features without requiring external data collection efforts. This self-generated data approach provides comprehensive training features while eliminating the difficulty of collecting high-quality diverse data

The patent utilizes parameter changes by systematically varying noise levels, image conditions, and other parameters during synthetic data generation. The noise prediction model generates training images across a spectrum of parameter values, ensuring comprehensive feature coverage. This parametric approach to data generation achieves high model accuracy by exposing the model to diverse conditions without the practical difficulties of collecting such varied real-world data

The patent applies copying by replacing manual annotation processes with synthetic data generation. The noise prediction model creates training images with automatically associated labels and metadata, copying the essential information structure needed for training without human intervention. This computational copying eliminates the need for complex manual annotation workflows while maintaining label completeness and accuracy

The system applies self-service by automatically generating labeled training data through the noise prediction model without requiring external annotation resources. The model inherently produces images with known ground truth and metadata, making the annotation process unnecessary. This self-generated labeling approach ensures complete and accurate labels while eliminating the complexity and costs associated with manual annotation processes

The patent applies copying by generating unlimited synthetic training samples through the noise prediction model. Instead of being constrained by the finite availability of manually collected high-quality images, the system creates computational copies of training data that can be generated in any quantity. This ensures sufficient training sample availability for achieving high model performance without being limited by physical data collection constraints

The patent applies preliminary action by pre-generating comprehensive training datasets before model training begins. The system creates a large repository of synthetic training images with various conditions and noise levels in advance, ensuring sufficient high-quality samples are available for training. This preliminary data preparation eliminates availability constraints during the actual training process, as the system can draw from the pre-generated pool of training samples