Results of the summarizing process for the arXiv paper: 2302.07261v2

Diffusion-based generative models (DBGMs) have been successful in various tasks such as image generation, editing, translation, and conditional text-to-image tasks. These models perturb data to a target noise distribution and then reverse the process to generate samples. The choice of the inference diffusion process greatly impacts both likelihoods and sample quality. In this work, the authors focus on Multivariate Diffusion Models (MDMs) and address the challenge of exploring different multivariate diffusions without requiring extensive model-specific analysis. They propose a recipe for maximizing a lower-bound on MDMs likelihood without the need for model-specific analysis, regardless of the number of auxiliary variables. Additionally, they demonstrate how to parameterize the diffusion for a specified target noise distribution, enabling optimization of the inference diffusion process. By optimizing the diffusion process, researchers can now experiment with a wider range of linear diffusions automatically. To showcase their approach, two new specific diffusions are introduced, and a diffusion process is learned on popular datasets like MNIST, CIFAR10, and ImageNet32. The results show that learned MDMs achieve or surpass bits-per-dim (BPDs) relative to fixed choices of diffusions for a given dataset and model architecture. The study highlights that selecting an appropriate inference process is essential for DBGMs' performance. By providing a method to optimize the inference diffusion process without extensive analysis, this work enables rapid prototyping and evaluation of different multivariate diffusions in DBGMs.

• Diffusion-based generative models (DBGMs) have been successful in tasks such as image generation, editing, translation, and conditional text-to-image tasks.
• DBGMs perturb data to a target noise distribution and then reverse the process to generate samples.
• The choice of the inference diffusion process greatly impacts both likelihoods and sample quality.
• The authors propose a recipe for maximizing a lower-bound on Multivariate Diffusion Models (MDMs) likelihood without extensive model-specific analysis.
• They demonstrate how to parameterize the diffusion for a specified target noise distribution, enabling optimization of the inference diffusion process.
• Optimizing the diffusion process allows researchers to experiment with a wider range of linear diffusions automatically.
• Two new specific diffusions are introduced and a diffusion process is learned on popular datasets like MNIST, CIFAR10, and ImageNet32.
• Learned MDMs achieve or surpass bits-per-dim (BPDs) relative to fixed choices of diffusions for a given dataset and model architecture.
• Selecting an appropriate inference process is essential for DBGMs' performance.
• This work enables rapid prototyping and evaluation of different multivariate diffusions in DBGMs by providing a method to optimize the inference diffusion process without extensive analysis.

Exploring Diffusion-based Generative Models with Multivariate Diffusions

Generative models have become increasingly popular in recent years due to their ability to generate realistic data from a given target noise distribution. Diffusion-based generative models (DBGMs) are one type of generative model that has been successful in various tasks such as image generation, editing, translation, and conditional text-to-image tasks. DBGMs perturb data to a target noise distribution and then reverse the process to generate samples. The choice of the inference diffusion process greatly impacts both likelihoods and sample quality; however, exploring different multivariate diffusions requires extensive model-specific analysis.

In this research paper, the authors focus on Multivariate Diffusion Models (MDMs) and address the challenge of exploring different multivariate diffusions without requiring extensive model-specific analysis. They propose a recipe for maximizing a lower bound on MDMs likelihood without the need for model-specific analysis regardless of the number of auxiliary variables. Additionally, they demonstrate how to parameterize the diffusion for a specified target noise distribution which enables optimization of the inference diffusion process. By optimizing this process researchers can now experiment with wider ranges of linear diffusions automatically.

To showcase their approach two new specific diffusions are introduced and a diffusion process is learned on popular datasets like MNIST, CIFAR10, and ImageNet32. The results show that learned MDMs achieve or surpass bits per dim (BPDs) relative to fixed choices of diffusions for given dataset and model architecture; thus highlighting that selecting an appropriate inference process is essential for DBGMs performance.

By providing a method to optimize the inference diffusion process without extensive analysis this work enables rapid prototyping and evaluation of different multivariate diffusions in DBGMs; allowing researchers more freedom when experimenting with these types of generative models while still achieving desired results quickly.