Continuous Adversarial Flow Models

We propose continuous adversarial flow models, a type ofcontinuous-time flow model trained with an adversarial objective. Unlikeflow matching, which uses a fixed mean-squared-error criterion, our ap-proach introduces a learned discriminator to guide training. This changein objective induces a different generalized distribution, which empiri-cally produces samples that are better aligned with the target data dis-tribution. Our method is primarily proposed for post-training existingflow-matching models, although it can also train models from scratch.On the ImageNet 256px generation task, our post-training substantiallyimproves the guidance-free FID of latent-space SiT from 8.26 to 3.63 andof 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 itachieves improved results on both the GenEval and DPG benchmarks.