Simple ReFlow: Improved Techniques for Fast Flow Models (2410.07815v1)

Abstract: Diffusion and flow-matching models achieve remarkable generative performance but at the cost of many sampling steps, this slows inference and limits applicability to time-critical tasks. The ReFlow procedure can accelerate sampling by straightening generation trajectories. However, ReFlow is an iterative procedure, typically requiring training on simulated data, and results in reduced sample quality. To mitigate sample deterioration, we examine the design space of ReFlow and highlight potential pitfalls in prior heuristic practices. We then propose seven improvements for training dynamics, learning and inference, which are verified with thorough ablation studies on CIFAR10 $32 \times 32$, AFHQv2 $64 \times 64$, and FFHQ $64 \times 64$. Combining all our techniques, we achieve state-of-the-art FID scores (without / with guidance, resp.) for fast generation via neural ODEs: $2.23$ / $1.98$ on CIFAR10, $2.30$ / $1.91$ on AFHQv2, $2.84$ / $2.67$ on FFHQ, and $3.49$ / $1.74$ on ImageNet-64, all with merely $9$ neural function evaluations.

• The paper introduces seven enhancements to ReFlow that refine training and inference, reducing neural function evaluations while boosting sample quality.
• The paper achieves state-of-the-art FID scores on CIFAR10, AFHQv2, FFHQ, and ImageNet-64, using just nine evaluations per sample.
• The paper innovates in loss normalization, time distribution, and dropout adjustments, ensuring robust and scalable generative modeling.

Overview of Simple ReFlow: Improved Techniques for Fast Flow Models

The paper "Simple ReFlow: Improved Techniques for Fast Flow Models" offers a comprehensive discussion on enhancing the ReFlow procedure to improve generative performance, particularly focusing on reducing the number of neural function evaluations (NFEs) necessary to achieve high-quality sample generation. By addressing the limitations and pitfalls in existing ReFlow techniques, the authors present a refined approach that achieves state-of-the-art Fréchet Inception Distance (FID) scores across various datasets.

Key Contributions

1. Improved ReFlow Techniques: The paper introduces seven enhancements targeting the training dynamics, learning, and inference stages of ReFlow. These refinements aim to mitigate sample quality deterioration, a common issue in previous implementations.
2. Numerical Results: The proposed methods set state-of-the-art FID scores of 2.23 on CIFAR10, 2.30 on AFHQv2, 2.84 on FFHQ, and 3.49 on ImageNet-64 with only nine NFEs, both with and without guidance. These results underline the efficacy of the improvements in providing fast, high-quality generative processes.
3. Methodological Innovations: The authors propose modifications to the ReFlow training loss, time distribution, and dropout probability while evaluating the effects of forward and backward sampling on the marginals. These innovations ensure the robustness and consistency of the trained models across different tasks.
4. Theoretical and Empirical Validation: Through extensive ablation studies and rigorous theoretical backing, the paper demonstrates the stability and reliability of the new techniques, offering a clear path toward faster generative models without sacrificing quality.

Detailed Insights

• Weight and Time Distribution: The paper elucidates the importance of appropriate weighting and time distribution in training dynamics. By introducing a time distribution enhancement and loss normalization technique, the authors demonstrate significant FID improvements.
• Denoiser Initialization and Dropout: Recognizing the complexity of learning optimal transport ODEs, the paper reveals that smaller dropout rates and careful initialization with diffusion models can enhance the model's capacity and performance without over-regularizing.
• Coupling Generation: The introduction of forward and projected pairs during training optimizes the sample space, providing a critical advancement over previous methodologies that relied solely on backward pairs.
• Inference Optimizations: Shifting from uniform to sigmoid discretizations and employing DPM-Solver over Heun's method significantly reduces local truncation errors and refines the generative paths.

Implications and Future Directions

The enhancements proposed for ReFlow not only promise more practical applications in scenarios demanding rapid inference but also expand the theoretical framework underpinning flow models. These developments pave the way for integrating ReFlow with higher-order solvers and distillation techniques, potentially revolutionizing the speed-quality trade-off in generative modeling.

The implications for future AI developments are vast, particularly in areas that require real-time data generation and sampling efficacy. Researchers are encouraged to explore further integration of these enhancements into other generative frameworks and consider their applications in conditional scenarios.

Conclusion

This paper successfully advances the state-of-the-art in ReFlow methodologies, providing robust and scalable solutions for fast generative modeling. Its combination of theoretical insight and empirical validation makes it a significant contribution to the field, offering a blueprint for further explorations in optimizing generative model efficiency.