9.7 C
London
HomeImproving GFlowNets for Text-to-Image Diffusion Alignment

Improving GFlowNets for Text-to-Image Diffusion Alignment

Related stories

Farewell: Fintech Nexus is shutting down

When we started Fintech Nexus in 2013 (known as...

Goldman Sachs loses profit after hits from GreenSky, real estate

Second-quarter profit fell 58% to $1.22 billion, or $3.08...

What Are the Benefits of Using Turnitin AI Detection?

In today’s digital age, academic integrity faces new challenges...

This Man Can Make Anyone Go Viral

Logan Forsyth helps his clients get hundreds of millions of...

This paper was accepted at the Foundation Models in the Wild workshop at ICML 2024.
Diffusion models have become the de-facto approach for generating visual data, which are trained to match the distribution of the training dataset. In addition, we also want to control generation to fulfill desired properties such as alignment to a text description, which can be specified with a black-box reward function. Prior works fine-tune pretrained diffusion models to achieve this goal through reinforcement learning-based algorithms. Nonetheless, they suffer from issues including slow credit assignment as well as low quality in their generated samples. In this work, we explore techniques that do not directly maximize the reward but rather generate high-reward images with relatively high probability — a natural scenario for the framework of generative flow networks (GFlowNets). To this end, we propose the Diffusion Alignment with GFlowNet (DAG) algorithm to post-train diffusion models with black-box property functions. Extensive experiments on Stable Diffusion and various reward specifications corroborate that our method could effectively align large-scale text-to-image diffusion models with given reward information.

Latest stories