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PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator

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arxiv 2405.07510 v5 pith:UEGUKYFN submitted 2024-05-13 cs.LG

Hanshu Yan , Xingchao Liu , Jiachun Pan , Jun Hao Liew , Qiang Liu , Jiashi Feng This is my paper
classification cs.LG
keywords modelsperflowdiffusionpiecewiseflowflowsplug-and-playrectified
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We present Piecewise Rectified Flow (PeRFlow), a flow-based method for accelerating diffusion models. PeRFlow divides the sampling process of generative flows into several time windows and straightens the trajectories in each interval via the reflow operation, thereby approaching piecewise linear flows. PeRFlow achieves superior performance in a few-step generation. Moreover, through dedicated parameterizations, the PeRFlow models inherit knowledge from the pretrained diffusion models. Thus, the training converges fast and the obtained models show advantageous transfer ability, serving as universal plug-and-play accelerators that are compatible with various workflows based on the pre-trained diffusion models. Codes for training and inference are publicly released. https://github.com/magic-research/piecewise-rectified-flow

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. M4V: Multimodal Mamba for Efficient Text-to-Video Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    M4V shows a Mamba-based text-to-video model can roughly match attention-based PyramidFlow on VBench while cutting mixer-layer FLOPs by 45% at 768x1280.

  2. Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Momentum Flow perturbs rectified flow velocities with a decaying random component and shows improved FID and recall on CelebA-HQ with half the sampling steps.

  3. Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution

    cs.CV 2026-07 reject novelty 4.0 of 10

    DDR-SR routes each real-world low-resolution image to one of two diffusion experts based on a high-frequency-loss difficulty score, using a low-compression VAE for hard images and a high-compression VAE for easy image...

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