Enhancing Image Generation with Moving Average Sampling in Frequency Domain

Belangrijkste concepten

Moving Average Sampling in Frequency domain (MASF) enhances stability in generative diffusion models by leveraging prior samples and frequency domain decomposition.

Samenvatting

The content introduces MASF, a technique to stabilize diffusion models by utilizing moving average in the frequency domain. It reinterprets denoising as model optimization and decomposes samples into frequency components. Extensive experiments show significant performance improvements in image generation tasks.

Abstract

MASF introduces moving average in the frequency domain to enhance stability in diffusion models.
Reinterprets denoising as model optimization and decomposes samples into frequency components.
Extensive experiments demonstrate significant performance improvements in image generation tasks.

Introduction

Diffusion models revolutionize generative tasks in computer vision.
Forward process introduces noise, denoising network produces images.
Compared to GANs, diffusion models offer training stability and high-quality samples.

Proposed Approach

MASF reframes denoising as model optimization and utilizes moving average in the frequency domain.
Decomposes samples into frequency components for dynamic evolution.
Weighting scheme prioritizes low-frequency components early and high-frequency components later.

Experiments

MASF improves FID scores in conditional and unconditional models on various datasets.
Integration with other sampling techniques further enhances performance.
Ablation studies show the importance of each component in MASF.

Conclusion

MASF significantly improves stability in diffusion models for image generation.
Negligible computational overhead and seamless integration into existing models.

Statistieken

MASF brings only 0.97% extra complexity cost.
FID scores consistently improve with the integration of MASF.
Linear weight in adaptive weighting scheme outperforms constant and quadratic weights.
Frequency weighting scheme with increasing weights for high-frequency components shows the best results.

Citaten

"MASF introduces negligible computational overhead and can be readily integrated into existing diffusion models."
"Extensive experiments validate that MASF significantly improves performance across various datasets, models, and sampling techniques."

Belangrijkste Inzichten Gedestilleerd Uit

Boosting Diffusion Models with Moving Average Sampling in Frequency Domain

by Yurui Qian,Q... om arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17870.pdf

Diepere vragen

How can MASF be adapted for other types of generative models

MASF can be adapted for other types of generative models by incorporating the moving average mechanism in the frequency domain to stabilize the denoising process. This approach can be applied to various generative models that involve iterative denoising or optimization processes. By decomposing samples into different frequency components and applying moving average separately to each component, MASF can enhance the stability and quality of generated outputs. Additionally, the dynamic weighting scheme used in MASF can be adjusted to prioritize different components based on the specific characteristics of the generative model, making it adaptable to a wide range of applications.

What are the potential drawbacks or limitations of using moving average in the frequency domain

One potential drawback of using moving average in the frequency domain is the increased computational complexity, especially when dealing with high-dimensional data or complex frequency components. The process of decomposing samples into frequency components, applying moving average to each component, and then reconstructing the samples back to the original domain can introduce additional computational overhead. Additionally, the effectiveness of moving average in the frequency domain may vary depending on the specific characteristics of the data and the generative model, leading to potential challenges in generalizing the approach across different scenarios.

How might the concept of frequency domain decomposition be applied in other areas of machine learning or data analysis

The concept of frequency domain decomposition can be applied in other areas of machine learning or data analysis to enhance the understanding and processing of complex data structures. For example, in signal processing, frequency domain decomposition techniques like wavelet transforms can be used to analyze and extract features from signals in different frequency bands. In anomaly detection, decomposing data into frequency components can help identify unusual patterns or outliers in the data. In natural language processing, frequency domain analysis can be used to extract semantic information from text data by analyzing the distribution of word frequencies. Overall, the concept of frequency domain decomposition can be a powerful tool for exploring and understanding complex data structures in various machine learning and data analysis applications.