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arXiv CS.AI·1d ago·5 min read
TraceSampler: Making Sampling Errors Predictable and Correctable for Accelerated Generative Sampling
TL;DR
TraceSampler is a novel method that corrects endpoint errors in low-step generative sampling without altering the pretrained model. It achieves significant reductions in trajectory mean squared error (MSE) while maintaining high image quality and efficiency.
✦ Why It Matters
Implement TraceSampler to enhance generative model performance with minimal computational cost today.
Key Takeaways
- Utilize TraceSampler for efficient generative sampling corrections.
- Expect significant reductions in trajectory MSE with minimal overhead.
- Leverage high-step references to improve low-step sampling accuracy.
- Integrate TraceSampler into existing workflows for better image quality.
How It Works
TraceSampler corrects endpoint errors by modeling them as a prediction problem. It uses a higher-step reference integration to provide a supervised target and employs a compact representation that captures shared structures and directions.
This allows for the prediction of sample-dependent coefficients through shallow multi-layer perceptron (MLP) heads, which drive a two-stage learned residual correction process.