Guided Path Sampling Improves Diffusion Model Refinement
Analysis
This paper addresses a key limitation in iterative refinement methods for diffusion models, specifically the instability caused by Classifier-Free Guidance (CFG). The authors identify that CFG's extrapolation pushes the sampling path off the data manifold, leading to error divergence. They propose Guided Path Sampling (GPS) as a solution, which uses manifold-constrained interpolation to maintain path stability. This is a significant contribution because it provides a more robust and effective approach to improving the quality and control of diffusion models, particularly in complex scenarios.
Key Takeaways
- •Addresses instability in diffusion model refinement caused by Classifier-Free Guidance.
- •Proposes Guided Path Sampling (GPS) to maintain path stability using manifold-constrained interpolation.
- •Demonstrates improved performance in perceptual quality and prompt adherence compared to existing methods.
- •Provides a theoretical guarantee of error boundedness, ensuring stability.
Reference
“GPS replaces unstable extrapolation with a principled, manifold-constrained interpolation, ensuring the sampling path remains on the data manifold.”