Stabilizing, Scaling & Enhancing MeanFlow for Large-scale Diffusion Distillation

2026-05-18 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

A new distillation framework enhances MeanFlow, a technique for accelerating diffusion models, to address its instability and "mean-seeking bias" when applied to large-scale industrial models. The framework introduces a warm-up technique that initially replaces MeanFlow's differential solution with a discrete one, preventing training collapse from an undertrained model. Once a preliminary velocity field is learned, the objective switches back to the differential solution for refinement. Additionally, it incorporates trajectory distribution alignment as an auxiliary objective to mitigate "mean-seeking bias" during few-step inference with complex target distributions. This method achieves superior performance compared to existing distillation approaches on the FLUX.1-dev text-to-image model (up to 12B parameters) and demonstrates robust generalization when extended to the 80B-parameter HunyuanImage 3.0 model.

Key takeaway

For research scientists optimizing large-scale diffusion models, this enhanced MeanFlow framework offers a robust solution to overcome previous instability and bias issues. You should consider integrating the proposed warm-up technique and trajectory distribution alignment to achieve superior performance and generalization, especially when distilling models with billions of parameters for few-step inference.

Key insights

A new framework stabilizes and enhances MeanFlow for large-scale diffusion model distillation, improving performance and generalization.

Principles

• Warm-up with discrete solutions stabilizes early training.
• Trajectory distribution alignment mitigates "mean-seeking bias".

Method

The method uses a warm-up phase with a discrete solution, transitioning to a differential solution, and incorporates trajectory distribution alignment as an auxiliary objective for robust distillation.

In practice

• Apply warm-up to stabilize MeanFlow optimization.
• Use trajectory alignment for complex distributions.

Topics

• MeanFlow
• Diffusion Distillation
• Warm-up Technique
• Trajectory Distribution Alignment
• Text-to-Image Models

Code references

• Xinxi-Zhang/Re-MeanFlow

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.