Adversarial Flow Models
We present adversarial flow models, a class of generative models that unifies adversarial models and flow models. Our method supports native one-step or multi-step generation and is trained using the adversarial objective. Unlike traditional GANs, where the generator learns an arbitrary transport plan between the noise and the data distributions, our generator learns a deterministic noise-to-data mapping, which is the same optimal transport as in flow-matching models. This significantly stabilizes adversarial training. Also, unlike consistency-based methods, our model directly learns one-step or few-step generation without needing to learn the intermediate timesteps of the probability flow for propagation. This saves model capacity, reduces training iterations, and avoids error accumulation. Under the same 1NFE setting on ImageNet-256px, our B/2 model approaches the performance of consistency-based XL/2 models, while our XL/2 model creates a new best FID of 2.38. We additionally show the possibility of end-to-end training of 56-layer and 112-layer models through depth repetition without any intermediate supervision, and achieve FIDs of 2.08 and 1.94 using a single forward pass, surpassing their 2NFE and 4NFE counterparts.
692d299e4397b1ec214f691e
Shanchuan Lin
admin_assigned
2025-12-02T07:42:53.089Z
645863f7dc18eb1a9b5d29df
https://cdn-avatars.huggingface.co/v1/production/uploads/645863f7dc18eb1a9b5d29df/t49Nnyl4tbkUn7CmQqKZh.jpeg
Peter Lin
PeterL1n
user
PeterL1n
692d299e4397b1ec214f691f
Ceyuan Yang
692d299e4397b1ec214f6920
Zhijie Lin
692d299e4397b1ec214f6921
Hao Chen
692d299e4397b1ec214f6922
Haoqi Fan
Adversarial Flow Models
We present adversarial flow models, a class of generative models that unifies adversarial models and flow models. Our method supports native one-step or multi-step generation and is trained using the adversarial objective. Unlike traditional GANs, where the generator learns an arbitrary transport plan between the noise and the data distributions, our generator learns a deterministic noise-to-data mapping, which is the same optimal transport as in flow-matching models. This significantly stabilizes adversarial training. Also, unlike consistency-based methods, our model directly learns one-step or few-step generation without needing to learn the intermediate timesteps of the probability flow for propagation. This saves model capacity, reduces training iterations, and avoids error accumulation. Under the same 1NFE setting on ImageNet-256px, our B/2 model approaches the performance of consistency-based XL/2 models, while our XL/2 model creates a new best FID of 2.38. We additionally show the possibility of end-to-end training of 56-layer and 112-layer models through depth repetition without any intermediate supervision, and achieve FIDs of 2.08 and 1.94 using a single forward pass, surpassing their 2NFE and 4NFE counterparts.
Adversarial flow models unify adversarial and flow-based generative models, offering stable training, efficient generation, and high performance on image datasets.
adversarial flow models generative models adversarial models flow models adversarial training generator noise-to-data mapping optimal transport flow-matching models consistency-based methods one-step or few-step generation probability flow ImageNet-256px FID end-to-end training depth repetition forward pass