Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching

📰 ArXiv cs.AI

Learn to optimize condition-dependent source distribution for flow matching to improve text-to-image generation

advanced Published 2 Jun 2026

Action Steps

Implement flow matching algorithm for text-to-image generation
Replace standard Gaussian distribution with a learnable source distribution
Optimize the source distribution using a condition-dependent approach
Evaluate the performance of the optimized model using metrics such as image quality and diversity
Compare the results with existing diffusion-based generative models

Who Needs to Know This

Researchers and engineers working on generative models, particularly text-to-image generation, can benefit from this knowledge to improve their models' performance

Key Insight

💡 Optimizing the source distribution in flow matching can lead to better performance in text-to-image generation

Key Takeaways

Learn to optimize condition-dependent source distribution for flow matching to improve text-to-image generation

Full Article

Title: Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching

Abstract:
arXiv:2602.05951v2 Announce Type: replace-cross Abstract: Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation. Despite its flexibility in allowing arbitrary source distributions, most existing approaches rely on a standard Gaussian distribution, a choice inherited from diffusion models, and rarely consider the source distribution itself as an optimization target in such settings. In this work, we show that

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