BiFM: Bidirectional Flow Matching for Few-Step Image Editing and Generation

• URL: http://arxiv.org/abs/2603.24942v1
• Date: Thu, 26 Mar 2026 02:16:54 GMT
• Title: BiFM: Bidirectional Flow Matching for Few-Step Image Editing and Generation
• Abstract summary: BiFM (Bidirectional Flow Matching) is a unified framework that jointly learns generation and inversion within a single model.<n>BiFM consistently outperforms existing few-step approaches, achieving superior performance and editability.
• Score: 48.679227622825806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent diffusion and flow matching models have demonstrated strong capabilities in image generation and editing by progressively removing noise through iterative sampling. While this enables flexible inversion for semantic-preserving edits, few-step sampling regimes suffer from poor forward process approximation, leading to degraded editing quality. Existing few-step inversion methods often rely on pretrained generators and auxiliary modules, limiting scalability and generalization across different architectures. To address these limitations, we propose BiFM (Bidirectional Flow Matching), a unified framework that jointly learns generation and inversion within a single model. BiFM directly estimates average velocity fields in both ``image $\to$ noise" and ``noise $\to$ image" directions, constrained by a shared instantaneous velocity field derived from either predefined schedules or pretrained multi-step diffusion models. Additionally, BiFM introduces a novel training strategy using continuous time-interval supervision, stabilized by a bidirectional consistency objective and a lightweight time-interval embedding. This bidirectional formulation also enables one-step inversion and can integrate seamlessly into popular diffusion and flow matching backbones. Across diverse image editing and generation tasks, BiFM consistently outperforms existing few-step approaches, achieving superior performance and editability.

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