VDE: Training-Free Accelerating Rectified Flow Model via Velocity Decomposition and Estimation
Though rectified flow models have achieved remarkable performance in image, video, and 3D generation, their practical deployments are challenged by slow inference speeds. Prior acceleration methods reuse cached features from previous steps, which neglects the growing mismatch between static caches and the evolving input, leading to reduced output fidelity. This work proposes Velocity Decomposition and Estimation (VDE), a training-free acceleration method that shifts the paradigm from caching-and-reusing to decomposing-and-estimating. Specifically, VDE decomposes the model's velocity into components parallel and orthogonal to the input, exploiting their temporal predictability and directio...
| Authors | Junwen Tan, Jinglin Liang, Hongyuan Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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