How to build a consistency model: Learning flow maps via self-distillation

★

☆

SHARE

Overview

Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

Good to know

Topic Hierarchy

File Information

Paper Summary

Paperzilla title

Flow Maps Learn from Themselves: Lagrangian Method Shows Off Its Stability!

This paper presents a unified algorithmic framework for training consistency models, which accelerate generative modeling by learning flow maps via self-distillation. The authors introduce three algorithmic families (Eulerian, Lagrangian, Progressive), demonstrating that the novel Lagrangian method offers significantly more stable training and higher performance compared to existing schemes, though some methods still struggle with fine details or higher step counts.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Eulerian Scheme Instability

The Eulerian distillation (ESD) method was found to be unstable during training and showed poor performance, particularly on datasets like CelebA-64, where results were not reported. This limits its practical applicability.

Struggles with Sharp Features

On datasets with very sharp features (e.g., Checkerboard), all methods, including the new Lagrangian and Progressive variants, exhibited some difficulty in capturing these sharp boundaries accurately or introduced artifacts at small step counts.

Lagrangian Method Performance at High Step Counts

While generally superior, the Lagrangian Self-Distillation (LSD) method performs less effectively than other variants (like PSD-M) at higher step counts (e.g., N=16 for CIFAR-10), indicating a trade-off in performance characteristics.

Rating Explanation

This paper provides a significant contribution by presenting a unified framework for consistency models and introducing novel Lagrangian methods for learning flow maps. The theoretical foundation is solid, and empirical results demonstrate improved stability and performance, particularly with the Lagrangian approach. While some limitations exist regarding performance on sharp features and specific step counts for different methods, the overall work advances the field of accelerated generative modeling.

Good to know

This is our free standard analysis. Paperzilla Pro fact-checks every citation, researches author backgrounds and funding sources, and uses advanced AI reasoning for more thorough insights.

Explore Pro →