PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator (2405.07510v5)

Abstract: 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

• The paper introduces PeRFlow, a novel method that divides iterative sampling into time windows to apply piecewise rectified flows for acceleration.
• The paper demonstrates that PeRFlow achieves high-quality outputs in as few as 4 steps, maintaining fidelity and compatibility across various models.
• The paper highlights PeRFlow's versatile, plug-and-play design, making advanced diffusion models accessible for real-time and resource-limited applications.

Understanding Piecewise Rectified Flow (PeRFlow) for Accelerating Diffusion Models

Introduction to PeRFlow

The recent surge in generative modeling, particularly diffusion models, has demonstrated remarkable capabilities in generating high-fidelity images, videos, and audio. However, the computational intensity of these models due to their iterative sampling processes hampers their practical application. Piecewise Rectified Flow (PeRFlow) introduces a novel method to accelerate this sampling process by dividing it into several time windows and applying a technique known as reflow to straighten the trajectories, essentially making the flow piecewise linear.

Key Innovations of PeRFlow

PeRFlow isn't just another model; it's a strategic enhancement designed to act as a universal accelerator for diffusion models. Here are its standout features:

• Time Efficiency: PeRFlow drastically reduces the number of required inference steps to generate high-quality outputs. This efficiency is achieved by applying the model in discrete steps across pre-defined time windows.
• Flexibility and Compatibility: The model adapits seamlessly across different data modalities and pre-trained models without extensive re-training or adjustments.
• Parameterization Techniques: PeRFlow introduces sophisticated parameterization methods that greatly retain the learning from the original diffusion models, enhancing the detail and fidelity of generated outputs.

Practical Implications

The implications of PeRFlow are twofold:

• Speed: By accelerating the generation process, PeRFlow opens the door to real-time applications of diffusion models that were previously too slow, such as dynamic content creation in gaming and interactive media.
• Accessibility: The reduced computational overhead makes high-quality diffusion models more accessible and feasible for more users and developers, including those with limited resources.

Speculation on Future Developments

As PeRFlow simplifies and accelerates the generative process, future iterations could lead to even more efficient models that push the boundaries of real-time generative AI. The concept of piecewise linear flows might extend beyond image and video generation, potentially transforming other areas of AI that rely on iterative generation processes.

Performance and Experimental Insights

PeRFlow not only accelerates the process but does so while maintaining, and in some cases improving, the visual quality and diversity of the generated content:

• High-Quality Outputs: Models accelerated with PeRFlow achieved results within 4 steps nearly indistinguishable from those produced by traditional, slower methods.
• Superior Transferability: The variation in weights, termed as $\Delta W$, demonstrated high compatibility and effectiveness when applied to different workflows and models that originate from the same pre-trained configurations.

Conclusion

PeRFlow represents a significant step forward in making powerful diffusion models more practical and versatile. By making these models quicker and more efficient, PeRFlow not only stretches the operational capacity of current generative models but also expands their potential applications, making high-quality generative content more accessible to a wider range of applications and users. While PeRFlow has already showcased impressive results, its true potential might be realized in how it adapts and evolves with the rapidly changing landscape of generative AI.