Balanced conic rectified flow

• URL: http://arxiv.org/abs/2510.25229v1
• Date: Wed, 29 Oct 2025 07:06:01 GMT
• Title: Balanced conic rectified flow
• Authors: Kim Shin Seong, Mingi Kwon, Jaeseok Jeong, Youngjung Uh,
• Abstract summary: Rectified flow is a generative model that learns smooth transport mappings between two distributions through an ordinary differential equation (ODE)<n>In this work, we experimentally expose the limitations of the original rectified flow and propose a novel approach that incorporates real images into the training process.
• Score: 19.226787997122987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rectified flow is a generative model that learns smooth transport mappings between two distributions through an ordinary differential equation (ODE). Unlike diffusion-based generative models, which require costly numerical integration of a generative ODE to sample images with state-of-the-art quality, rectified flow uses an iterative process called reflow to learn smooth and straight ODE paths. This allows for relatively simple and efficient generation of high-quality images. However, rectified flow still faces several challenges. 1) The reflow process requires a large number of generative pairs to preserve the target distribution, leading to significant computational costs. 2) Since the model is typically trained using only generated image pairs, its performance heavily depends on the 1-rectified flow model, causing it to become biased towards the generated data. In this work, we experimentally expose the limitations of the original rectified flow and propose a novel approach that incorporates real images into the training process. By preserving the ODE paths for real images, our method effectively reduces reliance on large amounts of generated data. Instead, we demonstrate that the reflow process can be conducted efficiently using a much smaller set of generated and real images. In CIFAR-10, we achieved significantly better FID scores, not only in one-step generation but also in full-step simulations, while using only of the generative pairs compared to the original method. Furthermore, our approach induces straighter paths and avoids saturation on generated images during reflow, leading to more robust ODE learning while preserving the distribution of real images.

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