Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models

Jul 15, 2026

Researchers introduce Self-Consistent Flow (SC-Flow), a method that jointly trains a single network to predict both local velocity and data endpoint in rectified-flow generative models. By adding a lightweight consistency loss, SC-Flow unifies the strengths of both parameterizations, stabilizing training and improving the straightness of generation paths. Experiments on image generation tasks show that SC-Flow achieves notable quality improvements over standard rectified-flow baselines with minimal computational overhead.

Why it matters: This work offers a principled approach to combining two key training targets in rectified flow models, potentially enhancing the stability and quality of generative image models.

Full story at: arXiv Computer Vision ↗