Continuous Adversarial Flow Models

TL;DR

This paper introduces continuous adversarial flow models that use a learned discriminator to improve sample quality in flow-based generative models, especially for image and text-to-image tasks.

Contribution

It presents a novel adversarial training approach for continuous flow models, enhancing their ability to generate data closer to the target distribution.

Findings

Significant FID score improvements on ImageNet 256px generation.

Enhanced guided generation quality with lower FID scores.

Improved results on text-to-image generation benchmarks.

Abstract

We propose continuous adversarial flow models, a type of continuous-time flow model trained with an adversarial objective. Unlike flow matching, which uses a fixed mean-squared-error criterion, our approach introduces a learned discriminator to guide training. This change in objective induces a different generalized distribution, which empirically produces samples that are better aligned with the target data distribution. Our method is primarily proposed for post-training existing flow-matching models, although it can also train models from scratch. On the ImageNet 256px generation task, our post-training substantially improves the guidance-free FID of latent-space SiT from 8.26 to 3.63 and of pixel-space JiT from 7.17 to 3.57. It also improves guided generation, reducing FID from 2.06 to 1.53 for SiT and from 1.86 to 1.80 for JiT. We further evaluate our approach on text-to-image generation, where it achieves improved results on both the GenEval and DPG benchmarks.