Accelerative Synthetic Data Generation with Early Exit Diffusion Pipelines

Overview

This project develops an intelligent diffusion-based filtering system that detects and removes inauthentic synthetic videos significantly faster than traditional approaches. By implementing early exit diffusion pipelines, the system optimizes workflows for scarce datasets while maintaining high-quality synthetic data generation.

Key Impact

• 75% compute savings through intelligent early exit strategies
• 6× faster video generation compared to baseline diffusion models
• 9× faster filtering for detecting inauthentic synthetic content
• Optimized for scarce dataset scenarios in robotics and computer vision

Technical Approach

The system leverages early exit mechanisms in diffusion pipelines to intelligently determine when sufficient quality has been achieved during the generation process. By analyzing intermediate representations, the model can terminate generation early for samples that have converged, while continuing refinement for more complex cases. This adaptive approach dramatically reduces computational requirements while maintaining output quality.

The filtering component uses learned classifiers to detect artifacts and inconsistencies in synthetic data, ensuring that only high-quality, authentic-looking samples are retained for downstream tasks. This is particularly valuable for robotics applications where training data scarcity is a critical bottleneck.

Applications

This accelerative synthetic data generation framework enables:

• Rapid augmentation of scarce robotics datasets for policy learning
• Efficient generation of diverse training scenarios for sim-to-real transfer
• Cost-effective scaling of synthetic data pipelines for vision-based tasks
• Real-time synthetic data generation for online learning systems