Efficient Sampling of Diffusion and Flow Models with Bespoke Non-Stationary Solvers

Core Concepts

The author introduces Bespoke Non-Stationary (BNS) Solvers as an approach to improve sample efficiency of Diffusion and Flow models, showcasing considerable improvements in sample approximation over existing methods. The BNS solvers offer a tiny parameter space, fast optimization, and maintain diversity of samples.

Abstract

The paper introduces Bespoke Non-Stationary (BNS) Solvers as a solver distillation approach to enhance the sample efficiency of Diffusion and Flow models. It demonstrates significant improvements in sample approximation compared to existing methods by utilizing a family of non-stationary solvers that subsume previous numerical ODE solvers. The BNS solvers require fewer parameters, optimize faster, maintain sample diversity, and achieve impressive results in various applications such as conditional image generation, text-to-image generation, and text-to-audio generation.

The content discusses the importance of efficient training algorithms for generative models like images, videos, audio, 3D geometry, molecules, and proteins. It highlights the costly process of sampling that requires sequential function evaluations to produce samples efficiently. The paper focuses on reducing sampling complexity through dedicated solvers, model distillation techniques, and solver distillation approaches.

Furthermore, it provides insights into the theoretical analysis behind Bespoke Non-Stationary (BNS) solvers by introducing the concept of Non-Stationary (NS) solvers family. It explains how NS solvers can be optimized to find efficient samplers for specific diffusion or flow models through algorithmic frameworks. The paper also presents a full taxonomy of popular ODE solvers used for sampling diffusion and flow models.

In experiments conducted on various datasets including ImageNet-64/128 for class conditional image generation and LibriSpeech TTS/Audiocaps for text-to-audio generation, BNS solvers consistently outperform baselines in terms of PSNR improvement while using significantly less compute resources. The results demonstrate the effectiveness of BNS solvers in improving sample approximation quality at lower numbers of function evaluations.

Stats

For example, BNS solver achieves 45 PSNR / 1.76 FID using 16 NFE in class-conditional ImageNet-64.
We have experimented with BNS solvers for conditional image generation, Text-to-Image (T2I) generation, and Text-to-Audio generation showing significant improvement in sample approximation (PSNR) in all.

Quotes

"The main goal is to introduce Bespoke Non-Stationary (BNS) solvers that provably subsumes all previous dedicated and distillation solvers."
"BNS solver achieves considerable improvement in approximating the original model’s samples (PSNR) for lower NFEs."

Key Insights Distilled From

Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models

by Neta Shaul,U... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01329.pdf

Deeper Inquiries

How does the use of bespoke non-stationary solvers impact the overall efficiency and performance compared to traditional methods?

Bespoke non-stationary solvers offer significant improvements in sample efficiency and performance compared to traditional methods. These solvers are tailored to specific diffusion and flow models, allowing for faster sampling with fewer function evaluations (NFEs). By optimizing a solver within the non-stationary family, BNS solvers demonstrate better sample approximation (measured by PSNR) at lower NFEs. This means that they can achieve high-quality results with fewer computational resources, making them more efficient than generic or dedicated solvers.

What are some potential limitations or challenges associated with implementing bespoke non-stationary solvers in real-world applications?

While bespoke non-stationary solvers offer advantages in terms of efficiency and performance, there are also potential limitations and challenges in their implementation:

Training Data Dependency: BNS solvers require training on pairs of noise samples and generated outputs from pre-trained models. This reliance on training data may limit their applicability to scenarios where such data is not readily available or difficult to obtain.

Optimization Complexity: Optimizing BNS solvers involves searching for an optimal solver within a parameter space, which can be computationally intensive. Finding the right initialization parameters and preconditioning strategies adds complexity to the optimization process.

Generalization: The effectiveness of BNS solvers may vary across different datasets or model architectures. Ensuring that these bespoke solutions generalize well beyond the training set poses a challenge.

Low NFE Regime Performance: While BNS solvers excel at reducing NFEs for high-quality samples, they may not perform as well in extremely low NFE regimes (<8), limiting their utility in certain applications.

How might advancements in bespoke non-stationary solver technology influence future developments in generative modeling research?

Advancements in bespoke non-stationary solver technology have the potential to shape future developments in generative modeling research by:

Efficiency Improvements: By demonstrating superior sample approximation at lower NFEs, BNS solvers pave the way for more efficient generative modeling techniques that reduce computational costs without compromising quality.

Customized Solutions: The ability to tailor solvers specifically to individual models opens up possibilities for fine-tuning sampling processes based on model characteristics, leading to improved performance across various tasks.

Research Directions: The success of BNS solvers highlights the importance of exploring novel approaches like solver distillation within generative modeling research. Future studies may focus on refining optimization algorithms, enhancing generalization capabilities, and extending these techniques to other domains beyond image generation.